Routine blood tests and machine learning identify complications in high myopia

Domain: biology
Published: 2026-03-14
Editorial outcome: major_revision

Abstract
High myopia can lead to cataract, glaucoma, retinal detachment, choroidal neovascularization, and macular
degeneration, causing irreversible vision loss. Imaging detects these complications, but population
screening is limited by equipment, and specialist availability. Here we show that a machine learning model
using routine blood test results identifies people at increased risk of complications related to high myopia
during standard health examinations. We develop the model in a multicentre study of 10,661 participants
and validate it in two independent cohorts. The model shows high accuracy across centres (area under the
receiver operating characteristic curve=0.9010-0.9649) and flags individuals who receive a clinical
diagnosis in a hospital-based prospective follow-up study of 5,067 participants. In a community screening
study of 311,254 adults, the model increases the yield of detected complications among those referred for
ophthalmic assessment (positive predictive value=74%). This scalable blood-based approach supports
opportunistic screening and earlier referral in primary care and community settings.

Introduction
Myopia is a global epidemic, with high myopia identified as a growing concern because of its associated
risk of complications1,2. Myopia, particularly high myopia, is not merely a refractive error but is associated
with markedly increased risks of vision-threatening complications3,4, including cataracts (3–8-fold5,6),
glaucoma (~7-fold7), retinal detachment (12–398), myopic macular degeneration (~209,10), and choroidal
neovascularization (~108), compared with non-myopic eyes. While the risk of complications increases with
increasing myopia, the population impact varies: lower degrees are more prevalent and thus contribute
greatly to the overall burden, whereas high myopia, although less common, is associated with a much
higher individual risk and accounts for more than half of pathological myopia. Some complications, such as
retinal detachment and glaucoma, may benefit from early detection and timely intervention (e.g.,
prophylactic laser for high-risk retinal lesions), whereas effective treatments for myopic macular
degeneration are lacking, and this condition is the leading cause of irreversible blindness in working-age
populations, particularly in urbanized regions11. The substantial economic burden associated with these
complications and their impacts on quality of life underscore the critical need for effective early screening
strategies12.

Current diagnostic paradigms rely heavily on specialized imaging modalities such as optical coherence
tomography (OCT), fundus autofluorescence, and perimetry. While these techniques provide detailed
structural information of the eyes, they present substantial limitations in real-world clinical practice. First,
considerable barriers exist to accessing these technologies, with approximately 78% of low- and middle-
income countries lacking basic OCT infrastructure13. Second, considerable variability has been documented
in clinician interpretations of the imaging findings, with studies reporting up to 22% variability in glaucoma
detection using OCT parameters14. Third, the resource-intensive nature of these examinations limits their
applicability for population-level screening, resulting in delayed diagnoses, a critical concern given that
nearly 40% of myopic macular lesions are asymptomatic in the early stages15. Recent advances in portable
fundus cameras and smartphone-based imaging offer cost-effective, accessible, and point-of-care
alternatives for retinal assessment16,17. These technologies facilitate the early detection and monitoring of
retinal pathologies, including diabetic retinopathy, age-related macular degeneration, and retinal vascular
disorders, thereby improving accessibility to essential diagnostic services. Nevertheless, challenges remain
regarding image quality, standardization, regulatory approval, and medicolegal considerations, highlighting
the continued need for complementary screening approaches such as blood-based risk stratification
models.

Emerging evidence has demonstrated the underutilized potential of routine clinical blood tests in ocular
risk assessments. Haematological markers such as the lymphocyte count, basophil count, mean platelet
volume, and the neutrophil-to-lymphocyte ratio can predict the risk of retinal detachment18,19, whereas
metabolic parameters such as the level of haemoglobin A1c (HbA1c) are correlated with maculopathy
progression20, and higher levels of inflammatory markers such as IL-6 are clearly associated with an
increased glaucoma risk21. These findings suggest that routinely available clinical laboratory data may
provide valuable insights into the risks of myopia-related complications; nevertheless, they remain
systematically underused in current screening protocols. From a cost perspective, blood-based screening
is also highly efficient. In health examination centres and primary care settings, the required haematological
parameters are already determined through routine blood tests, enabling opportunistic screening for high
myopia-related complications at no additional expense. Even if blood tests were performed specifically for
ocular screening, the median cost is approximately 300 RMB per person, which is lower than that of fundus-
based imaging (400–600 RMB). Additionally, blood tests provide concurrent systemic health information,
offering dual-purpose clinical utility. These features underscore blood-based models as a complementary,
scalable, and low-cost strategy for identifying high myopia-related complications.

Routine clinical laboratory tests are among the most common diagnostic methods used in medical
practice and provide valuable and timely insights into various diseases22. As blood cells interact with
different tissues and organs, these tests can serve as powerful diagnostic tools. Their key advantages—
low cost, accessibility, consistency, and widespread use in primary healthcare settings—make them
indispensable in routine medical evaluations23. The integration of machine learning (ML) with routinely
available clinical laboratory data represents a transformative approach to address the challenges
mentioned above. Recent advances have shown that ML algorithms can effectively predict high myopia-
related complications by leveraging routinely available blood data18,19,24. These technological
advancements could help address the urgent need for scalable screening tools, particularly in resource-
limited settings, by enabling opportunistic screening during routine health examinations, where the burden
of high myopia–related complications is increasing most rapidly.

This large-scale study was conducted in three phases—a retrospective multicentre case–control study,
a hospital-based prospective cohort study, and a community-based cross-sectional study—and leveraged
routine blood test data combined with advanced machine learning architectures to establish a paradigm for
opportunistic screening during routine health examinations for high myopia–related complications. In the
retrospective multicentre case–control phase, we analysed data from a diverse cohort of 15,000 patients
across five tertiary care centres in Asia to develop a robust predictive model integrating haematological,
metabolic, and inflammatory markers. The performance of the model was validated through two large
sample cohort studies: a hospital-based prospective cohort study and a community-based cross-sectional
study. We hypothesize that blood-based screening during routine health examinations will be clinically
advantageous over a direct ophthalmic examination in settings where specialized imaging is unavailable or
impractical, particularly in health examination centres and community primary care facilities. This approach
prioritizes scalability and generalizability, aligns with the principles of precision medicine, and holds promise
for reducing the global burden of preventable blindness associated with high myopia.

Results
Overview of the case‒control study
The structure and methodology of the case‒control study are outlined in Figure 1. Between Jan. 1, 2016,
and Dec. 30, 2024, 10,661 individuals who met the inclusion criteria were enrolled at one of five hospitals
in China. The Eye & ENT cohort (discovery cohort) included 5,102 participants. The first external validation
cohort included 3,125 participants from Xuhui Central Hospital of Fudan University and Putuo People’s
Hospital, and the second external validation cohort included 2,434 participants from Wanbei Coal–
Electricity Group General Hospital and the First Affiliated Hospital of Anhui Medical University. The numbers
of patients with high myopia overall and associated with cataracts, macular degeneration, choroidal
neovascularization, glaucoma, and retinal detachment in the discovery cohort, validation cohort 1, and
validation cohort 2 are illustrated in Figure 1. Across all cohorts, patients with uncomplicated high myopia
were generally younger than those with complicated high myopia, with the differences reaching statistical
significance in the discovery cohort, validation cohort 1, and validation cohort 2 (P < 0.001). No significant
differences in the sex distribution were observed between the uncomplicated and complicated high myopia
groups in any cohort (P > 0.05). Detailed information about the variables of interest in the discovery cohort,
validation cohort 1, and validation cohort 2 is presented in Tables S1–S3. Approximately 70% of the routine
laboratory parameters differed significantly among the six groups (the overall high myopia group and the
five complication groups). Effect sizes were calculated as follows to better assess their potential clinical
relevance: η² for continuous variables from one- way ANOVA (0.01 = small effect, 0.06 = medium effect, and
≥0.14 = large effect) and Cramér’s V for categorical variables (0.1 = small effect, 0.3 = medium effect, and
≥0.5 = large effect). Notably, 38.71% (discovery cohort), 31.15% (validation cohort 1), and 37.7% (validation
cohort 2) of the parameters showed a medium or large effect size, suggesting potential clinical relevance
beyond statistical significance.

Development of screening models based on all blood test variables
In the discovery cohort, all 61 variables from the routine clinical blood tests were employed to train screening
models based on the 10 ML algorithms. The performance of the models developed from the different ML
algorithms is shown in Figure 2A, and the detailed results are provided in Figure S1 and Table S4. In terms
of the area under the receiver operating characteristic curve (AUC) (Figure 2B), the performance of the
random forest (RF) (macroaverage AUC = 0.9707 [95% CI=0.9480–0.9891]) and extreme gradient boosting
(XGBoost) (macroaverage AUC = 0.9601 [95% CI=0.9452–0.9806]) models was significantly better (Table
S5; P < 0.05) than that of the other models according to the DeLong test, whose macroaverage AUC values
ranged from 0.6007 to 0.9561. The results of the DeLong test (Table S5) also showed no significant
difference (P = 0.184) between the RF and XGBoost algorithms.

As shown in Figure 2C, the RF model achieved AUCs of 0.9725 (95% CI = 0.9523–0.9901), 0.9782 (95%
CI = 0.9581–0.9940), 0.9752 (95% CI = 0.9539–0.9918), 0.9721 (95% CI = 0.9504–0.9888), 0.9617 (95%
CI = 0.9391–0.9815), and 0.9643 (95% CI = 0.9422–0.9834); negative predictive value (NPV)s of 0.9249
(95% CI = 0.8972–0.9489), 0.9333 (95% CI = 0.9041–0.9568), 0.9631 (95% CI = 0.9397–0.9843), 0.9698
(95% CI = 0.9451–0.9895), 0.9867 (95% CI = 0.9701–0.9971), and 0.9813 (95% CI = 0.9595–0.9962); and
positive predictive value (PPV)s of 0.7626 (95% CI = 0.7271–0.7942), 0.7922 (95% CI = 0.7581–0.8239),
0.9072 (95% CI = 0.8773–0.9348), 0.9516 (95% CI = 0.9278–0.9735), 0.9231 (95% CI = 0.8929–0.9499),
and 0.8868 (95% CI = 0.8535–0.9173) for identifying high myopia overall and high myopia associated with
cataracts, macular degeneration, choroidal neovascularization, glaucoma, and retinal detachment,
respectively. A confusion matrix (Figure 2D) was created to visualize the performance of the RF model,
revealing sensitivities of 0.8133 (95% CI = 0.7784–0.8469), 0.8966 (95% CI = 0.8681–0.9248), 0.7586 (95%
CI = 0.7212–0.7929), 0.7108 (95% CI = 0.6749–0.7450), 0.6857 (95% CI = 0.6491–0.7192), and 0.7581
(95% CI = 0.7228–0.7906) and specificities of 0.9008 (95% CI = 0.8722–0.9271), 0.8603 (95% CI = 0.8251–
0.8919), 0.9878 (95% CI = 0.9715–0.9970), 0.9961 (95% CI = 0.9827–0.9996), 0.9976 (95% CI = 0.9849–
0.9997), and 0.9924 (95% CI = 0.9739–0.9991) for identifying high myopia overall and high myopia
associated with cataracts, macular degeneration, choroidal neovascularization, glaucoma, and retinal
detachment, respectively.

The XGBoost model (Figure 2E) achieved AUCs of 0.9620 (95% CI = 0.9526–0.9713), 0.9728 (95% CI =
0.9657–0.9799), 0.9694 (95% CI = 0.9592–0.9787), 0.9588 (95% CI = 0.9393–0.9752), 0.9595 (95% CI =
0.9331–0.9829), and 0.9720 (95% CI = 0.9568–0.9856); NPVs of 0.9174 (95% CI = 0.8983–0.9356),
0.9122 (95% CI = 0.8917–0.9328), 0.9589 (95% CI = 0.9467–0.9711), 0.9646 (95% CI = 0.9536–0.9747),
0.9868 (95% CI = 0.9796–0.9928), and 0.9802 (95% CI = 0.9718–0.9880); and PPVs of 0.7300 (95% CI =
0.6868–0.7755), 0.7746 (95% CI = 0.7405–0.8091), 0.8416 (95% CI = 0.7757–0.8990), 0.8594 (95% CI =
0.7879–0.9265), 1.000 (95% CI = 1.0000–1.0000), and 0.9388 (95% CI = 0.8824–0.9864) for identifying
high myopia overall and high myopia associated with cataracts, macular degeneration, choroidal
neovascularization, glaucoma, and retinal detachment, respectively. The confusion matrix (Figure 2F)
revealed sensitivities of 0.7967 (95% CI = 0.7520–0.8373), 0.8621 (95% CI = 0.8312–0.8931), 0.7328 (95%
CI = 0.6667–0.8000), 0.6627 (95% CI = 0.5750–0.7471), 0.6857 (95% CI = 0.5428–0.8065), and 0.7419
(95% CI = 0.6469–0.8298) and specificities of 0.8846 (95% CI = 0.8618–0.9057), 0.8510 (95% CI = 0.8254–
0.8748), 0.9784 (95% CI = 0.9689–0.9866), 0.9884 (95% CI = 0.9818–0.9936), 1.000 (95% CI = 1.0000–
1.0000), and 0.9962 (95% CI = 0.9925–0.9988) for identifying high myopia overall and high myopia
associated with cataracts, macular degeneration, choroidal neovascularization, glaucoma, and retinal
detachment, respectively.

Additionally, the area under the precision‒recall curve (PR-AUC) values of the RF (0.8917 [95% CI =
0.8786–0.9108]) and XGBoost (0.8788 [95% CI = 0.8514–0.8906]) models (Figure S2) were significantly
higher (P < 0.05) than those of the other models (Figure S2) according to the DeLong test, confirming the
superior performance of the models developed based on these two algorithms. Given these results, the RF
and XGBoost algorithms were selected for the subsequent steps in the development of the screening model.

Identification of the final model developed based on nine features
Feature reduction was performed on the data from the discovery cohort to optimize the performance of the
models and increase their clinical applicability. The number of features was sequentially reduced from 61
to 1 based on their importance rankings to determine the minimum number of features that yielded optimal
predictive capability.

The all-feature RF model was significantly better than the 8-feature model (P = 0.0227) but not significantly
better than the 9-feature model (P = 0.0970) (Figure 3A, Table S6). In other words, the macroaverage
changes in the AUC value indicated that the RF model incorporating nine features (EOS, PCT, BASO%, M,
UA, LPB, BASO, EOS%, and TG levels; macroaverage AUC = 0.9649 [95% CI=0.9528–0.9770]) retained
nearly the highest predictive ability. For the XGBoost algorithm, the all-feature model was significantly better
than the 19-feature model (P < 0.0010, macroaverage AUC = 0.9493) but not significantly better than the
20-feature model (P = 0.1860, macroaverage AUC = 0.9621) (Figure 3A, Table S6). Based on the AUC
(0.9649 vs. 0.9621) and the number of included features (9 vs. 20), the nine-feature RF model was selected
as the final screening model.

The global SHapley Additive exPlanations (SHAP) analysis provided insights into the overall behaviour of
the RF model, with SHAP summary plots (Figure 3B) illustrating the ranked contributions of each feature
based on their average SHAP values in descending order. The top nine contributing features were the EOS,
PCT, BASO%, M, UA, LPB, BASO, EOS%, and TG levels. The distributions and correlations of these nine
features are presented in Figure S3A. Strong correlations were observed between the EOS and EOS% and
between the BASO and BASO%, whereas no significant correlations were identified among the other
variables.

The 9-feature RF model achieved a macroaverage AUC of 0.9649 (95% CI=0.9528–0.9770) (Figure 3C)
and an PR-AUC of 0.8812 (95% CI=0.8045–0.9579) (Figure 3D). At the subgroup level, the RF model
achieved AUCs of 0.9736 (95% CI = 0.9480–0.9880), 0.9790 (95% CI = 0.9342–0.9885), 0.9705 (95% CI
= 0.9272–0.9772), 0.9589 (95% CI = 0.9348–0.9706), 0.9475 (95% CI = 0.8976–0.9788), and 0.9599 (95%
CI = 0.9214–0.9746); NPVs of 0.9387 (95% CI = 0.8915–0.9926), 0.9333 (95% CI = 0.8821–0.9692),
0.9650 (95% CI = 0.9150–0.9797), 0.9669 (95% CI = 0.9465–0.9759), 0.9867 (95% CI = 0.9478–0.9974),
and 0.9824 (95% CI = 0.9523–0.9894); and PPVs of 0.8602 (95% CI = 0.8075–0.9178), 0.8237 (95% CI =
0.7660–0.8618), 0.7860 (95% CI = 0.7436–0.8160), 0.7451 (95% CI = 0.6978–0.7964), 0.7273 (95% CI =
0.6941–0.7578), and 0.7869 (95% CI = 0.7352–0.8387) for identifying high myopia overall and high myopia
associated with cataracts, macular degeneration, choroidal neovascularization, glaucoma, and retinal
detachment, respectively (Figure 3C). The confusion matrix (Figure 3E) revealed sensitivities of 0.8423 (95%
CI = 0.7987–0.8823), 0.8934 (95% CI = 0.8398–0.9297), 0.7759 (95% CI = 0.7182–0.8305), 0.6867 (95%
CI = 0.6486–0.7280), 0.6857 (95% CI = 0.6513–0.7428), and 0.7742 (95% CI = 0.7265–0.8065) and
specificities of 0.9463 (95% CI = 0.8967–0.9943), 0.8864 (95% CI = 0.8383–0.9175), 0.9689 (95% CI =
0.9257–0.9755), 0.9832 (95% CI = 0.9416–0.9940), 0.9915 (95% CI = 0.9546–1.0000), and 0.9849 (95%
CI = 0.9517–0.9929) for identifying high myopia overall and high myopia associated with cataracts, macular
degeneration, choroidal neovascularization, glaucoma, and retinal detachment, respectively. The optimal
cut-off values of the final RF model for the detection of high myopia (Figure S4A) and high myopia
associated with cataracts (Figure S4B), macular degeneration (Figure S4C), choroidal neovascularization
(Figure S4D), glaucoma (Figure S4E), and retinal detachment (Figure S4F) were 0.47, 0.62, 0.16, 0.18,
0.11, and 0.12, respectively. A detailed description of the performance, including sensitivity, specificity, PPV,
NPV, accuracy, and F1 score, of the final RF model is provided in Table 1.

We subsequently performed a subgroup analysis after excluding patients with active systemic inflammatory
conditions (n= 126), recent infections (n=240), immunosuppressive medication use (n= 64), diabetes (n=
277), or hypertension (n=455), conditions that could affect haematological biomarkers to assess the
performance of the final RF model.

The final RF model achieved a macroaverage AUC of 0.9560 (95% CI: 0.9236–0.9884), which was not
significantly different from that of the model including all patients (DeLong test, P = 0.056). As shown in
Figure S5A, the RF model achieved AUCs of 0.9701 (95% CI = 0.9377–1.0000), 0.9751 (95% CI = 0.9427–
1.0000), 0.9685 (95% CI = 0.9361–1.0000), 0.9536 (95% CI = 0.9212–0.9860), 0.9236 (95% CI = 0.8912–
0.9560), and 0.9423 (95% CI = 0.9099–0.9747) for identifying high myopia overall and high myopia
associated with cataracts, macular degeneration, choroidal neovascularization, glaucoma, and retinal
detachment, respectively. Other performance metrics, including sensitivity, specificity, PPV, NPV, accuracy,
and F1 score, are summarized in Table S7, with macroaverage values of 0.7639, 0.9557, 0.9229, 0.9625,
0.9250, and 0.8250, respectively.

External validation of the final RF model
Models developed in the discovery cohort require validation with independent validation cohorts to ensure
robustness. Therefore, two external validation cohorts were used to evaluate the performance of the 9-
feature RF model. In validation cohort 1, the RF model achieved a macroaverage AUC of 0.9020 (95% CI
= 0.8815–0.9225; Figure 4A) and an PR-AUC of 0.7797 (95% CI = 0.7091–0.8504; Figure 4B). As shown
in Figure 4A, the RF model achieved AUCs of 0.9021 (95% CI = 0.8543–0.9505), 0.9133 (95% CI = 0.8626–
0.9640), 0.8951 (95% CI = 0.8505–0.9437), 0.8948 (95% CI = 0.8629–0.9390), 0.9324 (95% CI = 0.8739–
0.9649), and 0.8745 (95% CI = 0.8319–0.9193) for identifying high myopia overall and high myopia
associated with cataracts, macular degeneration, choroidal neovascularization, glaucoma, and retinal
detachment, respectively. The confusion matrix (Figure 4C) further illustrates the model’s performance,
revealing sensitivities of 0.7229 (95% CI = 0.6743–0.7656), 0.8482 (95% CI = 0.7973–0.9016), 0.5888 (95%
CI = 0.5396–0.6430), 0.5840 (95% CI = 0.5208–0.6374), 0.7481 (95% CI = 0.6910–0.7890), and 0.5592
(95% CI = 0.5028–0.6036) and specificities of 0.8702 (95% CI = 0.8292–0.9084), 0.7610 (95% CI = 0.7012–
0.8011), 0.9864 (95% CI = 0.9422–0.9943), 0.9886 (95% CI = 0.9546–0.9958), 0.9996 (95% CI = 0.9757–
1.0000), and 0.9940 (95% CI = 0.9655–1.0000) for identifying high myopia overall and high myopia
associated with cataracts, macular degeneration, choroidal neovascularization, glaucoma, and retinal
detachment, respectively.

The distributions and correlations of the nine blood test variables in validation cohort 1 are presented in
Figure S3B. Strong correlations were observed between the EOS and EOS% and between BASO and
BASO%, whereas no significant correlations were found among the other variables, suggesting limited
multicollinearity and supporting the independent contributions of these features to model performance.

Similar results were observed in validation cohort 2, where the RF model achieved a macroaverage AUC
of 0.9010 (95% CI = 0.8761–0.9259; Figure 4D) and an PR-AUC of 0.7841 (95% CI = 0.7057–0.8625;
Figure 4E). As shown in Figure 4D, the RF model achieved AUCs of 0.9064 (95% CI = 0.8604–0.9370),
0.9164 (95% CI = 0.8678–0.9569), 0.8988 (95% CI = 0.8520–0.9389), 0.8752 (95% CI = 0.8167–0.9216),
0.9351 (95% CI = 0.8876–0.9651), and 0.8743 (95% CI = 0.8440–0.9277) for identifying high myopia overall
and high myopia associated with cataracts, macular degeneration, choroidal neovascularization, glaucoma,
and retinal detachment, respectively. The confusion matrix (Figure 4F) confirmed these findings, with
sensitivities of 0.7456 (95% CI = 0.7152–0.7969), 0.8551 (95% CI = 0.8099–0.9115), 0.6007 (95% CI =
0.5342–0.6616), 0.5696 (95% CI = 0.5041–0.6110), 0.7890 (95% CI = 0.7427–0.8284), and 0.6145 (95%
CI = 0.5490–0.6812) and specificities of 0.8774 (95% CI = 0.8188–0.9281), 0.7828 (95% CI = 0.7458–
0.8198), 0.9812 (95% CI = 0.9437–0.9924), 0.9894 (95% CI = 0.9620–0.9975), 0.9995 (95% CI = 0.9667–
1.0000), and 0.9930 (95% CI = 0.9463–1.0000) for identifying high myopia overall and high myopia
associated with cataracts, macular degeneration, choroidal neovascularization, glaucoma, and retinal
detachment, respectively.

The distributions and correlations of these nine blood test variables in validation cohort 2 are presented in
Figure S3C. Strong correlations were observed between the EOS and EOS% and between the BASO and
BASO%, whereas no significant correlations were found among the other variables. A detailed description
of the performance, including sensitivity, specificity, PPV, NPV, accuracy, and F1 score, of the final RF
model in the validation cohort is provided in Table 1.

A subgroup analysis of validation cohort 1 was performed after excluding patients with active systemic
inflammatory conditions (n= 81), recent infections (n=144), immunosuppressive medication use (n= 25),
diabetes (n= 222), or hypertension (n=367) to assess the performance of the final RF model. The final RF
model achieved a macroaverage AUC of 0.9010, which was not significantly different from that of the model
including all patients (DeLong test, P = 0.868). As shown in Figure S5B, the RF model achieved AUCs of
0.9064, 0.9164, 0.8988, 0.8752, 0.9351, and 0.8743 for identifying high myopia overall and high myopia
associated with cataracts, macular degeneration, choroidal neovascularization, glaucoma, and retinal
detachment, respectively. Other performance metrics, including sensitivity, specificity, PPV, NPV, accuracy,
and F1 score, are summarized in Table S8, with macroaverage values of 0.6758, 0.9327, 0.8407, 0.9398,
0.7279, and 0.7359, respectively.

Furthermore, a subgroup analysis of validation cohort 2 was performed after excluding patients with active
systemic inflammatory conditions (n= 33), recent infections (n=76), immunosuppressive medication use (n=
18), diabetes (n= 109), or hypertension (n=138) to assess the performance of the final RF model. The final
RF model achieved a macroaverage AUC of 0.9008, which was not significantly different from that of the
model including all patients (DeLong test, P = 0.976). As shown in Figure S5C, the RF model achieved
AUCs of 0.9042, 0.9135, 0.8890, 0.8833, and 0.9456 for identifying high myopia overall and high myopia
associated with cataracts, macular degeneration, choroidal neovascularization, glaucoma, and retinal
detachment, respectively. Other performance metrics, including sensitivity, specificity, PPV, NPV, accuracy,
and F1 score, are summarized in Table S9, with macroaverage values of 0.6834, 0.9332, 0.8486, 0.9398,
0.7286, and 0.7429, respectively.

In the sensitivity analysis excluding UA, the only kidney function indicator included in the model, the final
RF model maintained robust performance (Table S10), achieving macroaverage AUCs of 0.8589 and
0.8613 in validation cohorts 1 (Figure S6A) and 2 (Figure S6B), respectively.

Furthermore, a comparison between the final nine-feature RF model and other machine learning
algorithms—constructed using the complete set of blood test variables as input features—was conducted
in validation cohorts 1 (Table S11, Figure S7) and 2 (Table S12, Figure S8) using key performance metrics.
As shown in Figure 4G, in validation cohort 1, the final RF model outperformed all the other models,
including those utilizing the full set of blood test variables. Notably, its performance was comparable to that
of the conventional RF model trained on the complete feature set. Similar results were observed in
validation cohort 2 (Figure 4H), further confirming the robustness of the final RF model.

Taken together, these findings indicate that the nine-feature RF model demonstrated strong screening
performance across all the validation cohorts, consistent with the findings in the discovery cohort.

Evaluation of the performance of the final RF model with DCA and calibration curves
DCA of the final RF model indicated that it consistently offered net benefits for the discovery cohort and
both validation cohorts over a range of threshold probabilities. In the discovery cohort (Figure S9A),
validation cohort 1 (Figure S9B), and validation cohort 2 (Figure S9C), the RF model outperformed the
“treat none” strategy, indicating its practical utility in clinical decision-making.

The calibration curves further confirmed the model’s robust screening performance. In the discovery cohort
(Figure 5A), the RF model showed good overall calibration, particularly for high myopia and high myopia
associated with macular degeneration. High myopia associated with cataracts was slightly overestimated
in the mid-to-high probability range, whereas the model tended to underestimate the likelihood of high
myopia associated with choroidal neovascularization, glaucoma, and retinal detachment, as indicated by
calibration curves consistently falling below the ideal diagonal line, suggesting a conservative prediction
tendency for these categories.

Similar calibration performance was observed in validation cohort 1 (Figure 5B) and validation cohort 2
(Figure 5C), further supporting the reliability and generalizability of the model across different populations.

Model interpretability and clinical application
Given the challenges clinicians face in adopting diagnostic models with limited interpretability, we employed
SHAP to elucidate the decision-making process of our final model by quantifying the contribution of each
feature to the model predictions. The SHAP summary plot (Figure 6A) presents the relative importance
values of the nine selected features based on their mean absolute SHAP values, providing a global
interpretation of model behaviour. SHAP dependence plots were generated to illustrate how individual
features influence the model output and to further improve the interpretability. Each plot depicts the
relationship between the actual values of the features and their corresponding SHAP values.

We developed a freely accessible online web application to facilitate seamless integration into clinical
practice (Figure 6B), which is available at https://high-myopia.streamlit.app/. This interactive platform allows
clinicians to input the nine required feature values, after which the application automatically computes and
displays the predicted risk based on the RF model.

Hospital-based prospective validation of the final RF model
A total of 5,067 participants with high myopia were enrolled in the hospital-based prospective cohort study
to screen for high myopia-related complications (Figure 7A). Of these participants, 4,572 met the eligibility
criteria and were assessed in real time with the online web application developed for assessing the risk of
high myopia-related complications. According to the screening results of the model, 1,523 individuals were
classified as high risk. Among them, 127 declined to cooperate, and 84 withdrew voluntarily, resulting in
211 individuals not being referred for further ophthalmic evaluation.

Among them, 1,312 high-risk participants were referred to the Department of Ophthalmology for further
evaluation, resulting in the confirmation of 938 cases of high myopia-related complications. An additional
2,270 individuals identified as being at low risk of complications were also referred for further ophthalmic
evaluation, and 347 cases of high myopia-related complications were identified at EENT Hospital.

Figure 7B presents the confusion matrix of the final RF model for detecting the five high myopia-related
complications. The model achieved a mean sensitivity of 70.0% (95% CI=66.43%–74.22%), a specificity of
94% (95% CI=90.18%–98.22%), a PPV of 86% (95% CI=82.33%–89.87%), and an NPV of 94% (95%
CI=91.60%–97.04%), as detailed in Figure 7C.

The final RF model yielded false positive rates of 14.22% for cataracts, 0.23% for macular degeneration,
0.52% for choroidal neovascularization, 0% for glaucoma, and 0.18% for retinal detachment, indicating a
low proportion of false alarms. In terms of downstream costs, the higher false positive rate for cataracts
may lead to some additional routine ophthalmologic visits, while the very low false positive rates for macular
degeneration, choroidal neovascularization, and retinal detachment minimize unnecessary procedures.
Overall, the model helps allocate ophthalmic resources efficiently, focusing on confirmatory evaluations of
high-risk individuals and limiting unnecessary healthcare expenditure.

Community-based validation of the final RF model
A total of 311,254 participants were included in the community-based cross-sectional study, among whom
1,905 were identified as having high myopia (Figure 7D). Of these, 1,894 met the inclusion criteria and were
assessed in real time with the online web application. According to the predictions of the RF model, 267
individuals were classified as high risk.

A total of 210 high-risk individuals were subsequently referred to the Department of Ophthalmology for
further clinical evaluation, which confirmed 148 cases of high myopia-related complications. Owing to the
cross-sectional nature of the study, follow-up validation was not performed for participants in the low-risk
group.

The final RF model had favourable PPVs (Figure 7D) in detecting the five high myopia-related complications,
with the highest PPV observed for the prediction of macular degeneration (0.795), followed by glaucoma
(0.75), retinal detachment (0.75), choroidal neovascularization (0.733), and cataracts (0.65), indicating
strong performance in identifying true positive cases within the high myopia population.

Methods
Ethical considerations
This study received ethical approval from the institutional review boards of the participating institutions:
Xuhui Central Hospital (Approval No. 2025-017), the Eye & ENT Hospital of Fudan University (Approval No.
2025124), Wanbei Coal–Electricity Group General Hospital (Approval No. WBZY-LLWYH-2025-020), and
the First Affiliated Hospital of Anhui Medical University (Approval No. PJ2025-06-34). This study was
conducted in accordance with the Declaration of Helsinki and the Ethical Guidelines for Medical and Health
Research Involving Human Subjects. This study was conducted in three phases. First, a multicentre
retrospective case–control study was performed to develop and internally and externally validate the
machine learning-based model using routine blood test data from patients with high myopia, both with and
without complications. Second, the clinical performance of the model was prospectively validated in a
hospital-based cohort. Finally, the model was further evaluated in a community-based cross-sectional study
to examine its utility for population-level screening. Given that the multicentre case–control study and
community-based cross-sectional study was noninterventional and retrospective in nature, the institutional
review boards waived the requirement for informed consent. In the prospective, hospital-based cohort, all
participants provided written informed consent prior to sample collection and study enrolment.

Study design and participants
Multicentre, case‒control study
A multicentre, case‒control study was conducted across five hospitals in China between January 1, 2016,
and December 30, 2024. Patients from the Eye & ENT Hospital of Fudan University composed the discovery
cohort. The first validation cohort consisted of patients from Xuhui Central Hospital of Fudan University and
Putuo District Central Hospital, and the second validation cohort included patients from Wanbei Coal–
Electricity Group General Hospital and The First Affiliated Hospital of Anhui Medical University. The overall
study design is illustrated in Figure 1. Sex of participants was recorded as male or female according to
hospital registration data, which were based on self-reported information at the time of admission. As sex
were not directly relevant to the study objectives, no sex-based analyses were performed.

Individuals were excluded if they met any of the following criteria to minimize the effects of potential
confounding factors: a history of haematologic diseases or cancers; systemic illnesses such as acute
infections, metabolic syndrome, or autoimmune diseases; pregnancy or intraocular surgery within the past
six months; aged under 18 years; or a lack of available laboratory test results. In the subgroup analyses,
patients with active systemic inflammatory conditions, recent infections, immunosuppressive medication
use, diabetes, or hypertension were excluded to further validate the robustness of the model. This
information was collected through a review of medical records and laboratory data, and individuals meeting
one or more criteria were removed from the analysis prior to model development.

During the multicentre, case–control study phase, all patients with high myopia who visited one of the five
participating hospitals between January 1, 2016, and December 30, 2024, were included, without sampling.
The discovery cohort comprised 1,464 patients with uncomplicated high myopia and patients with high who
were divided into the following complication-specific subgroups: 1,914 patients with cataracts, 698 with
macular degeneration, 478 with choroidal neovascularization, 185 with glaucoma, and 363 with retinal
detachment. External validation cohort 1 included 937 patients with uncomplicated high myopia, 986
patients with high myopia complicated with cataracts, 463 patients with high myopia complicated with
macular degeneration, 335 patients with high myopia complicated with choroidal neovascularization, 114
patients with high myopia complicated with glaucoma, and 290 patients with high myopia complicated with
retinal detachment. External validation cohort 2 included 787 patients with uncomplicated high myopia and
with high myopia presenting with the following complications: 771 patients with cataracts, 255 with macular
degeneration, 192 with choroidal neovascularization, 130 with glaucoma, and 299 with retinal detachment.
No participants had two or more of these five high myopia complications. Venous blood samples were
collected from patients newly diagnosed with high myopia or high myopia-related complications within 24
hours of presentation.

Large-sample cohort study
The performance of the screening model was validated through two large-sample cohort studies: a hospital-
based prospective cohort study and a community-based cross-sectional study. A prospective screening
program was initiated at the Eye & ENT Hospital of Fudan University and Xuhui Central Hospital of Fudan
University. Briefly, all patients with high myopia who presented to the hospital from January 2025 to May
2025 were sequentially enrolled, with approximately 5,000 patients recruited during the study period.
Routine blood tests were performed for all eligible participants. Individuals who were identified as positive
for high myopia-related complications according to the established screening model were further evaluated
with ophthalmic examinations to confirm the presence of these complications. Furthermore, individuals
identified as negative for high myopia-related complications were further assessed through ophthalmic
examinations.

A community-based cross-sectional screening program was launched in Xuhui District, Shanghai, in July
202446. Residents of Xuhui District with high myopia were invited to participate through two local community
health service centres, the Changqiao Community health service centre and the Caohejing Community
health service centre, with whom we have maintained strong collaborative relationships over the past
decade. Interested individuals were screened for eligibility according to predefined inclusion and exclusion
criteria. Eligible participants were provided with detailed information about the study objectives, procedures,
and potential risks. Approximately 300,000 residents of Xuhui District were included. Routine blood tests
were performed for all eligible participants as part of their routine physical examinations; those who were
identified as positive for high myopia-related complications according to the model were further evaluated
and confirmed to be positive through ophthalmic examinations conducted at the Eye & ENT Hospital of
Fudan University or Xuhui Central Hospital of Fudan University. Furthermore, individuals who were
identified as negative for high myopia-related complications were not further evaluated with ophthalmic
examinations.

The inclusion criteria were as follows: no prior medical history of high myopia-related complications; ability
to understand, sign, and date a written informed consent form; good physical condition; and clarity of
consciousness.

The exclusion criteria were as follows: a history of haematologic diseases or any form of cancer; pregnancy
or intraocular surgery within the past six months; and aged under 18 years.

The study exit criteria were as follows: development of serious comorbid conditions such as significant
cardiovascular, hepatic, or renal diseases or malignancies; noncooperation during follow-up diagnostic
confirmation for high myopia-related complications; voluntary withdrawal for any reason; and death from
any cause.

Eye examinations and diagnostic criteria
The procedures for the detailed ophthalmic examinations have been described previously21,47,48. All the
enrolled patients underwent comprehensive eye evaluations, which included intraocular pressure
measurements, slit-lamp biomicroscopy, optical coherence tomography, B-scan ultrasonography, ultrawide
fundus photography, ultra-widefield autofluorescence, and fluorescein angiography. In our study, refractive
error was measured using cycloplegic refraction, and high myopia was defined as a refractive error of ≤
−6.0 dioptres. Uncomplicated high myopia was defined as high myopia in the absence of related
complications, including cataracts, myopic macular degeneration, choroidal neovascularization, glaucoma,
and retinal detachment.

High myopia-related complications were identified based on the following standard ophthalmologic
diagnostic criteria:
Cataract—diagnosed by slit-lamp biomicroscopy, defined as lens opacity graded according to the Lens
Opacities Classification System III and confirmed to cause a best-corrected visual acuity reduction not
attributable to other ocular diseases;
Macular degeneration—diagnosed by fundus photography and/or OCT according to the Meta-Analysis for
Pathologic Myopia classification, including diffuse chorioretinal atrophy, patchy atrophy, and macular
atrophy;
Choroidal neovascularization—confirmed by OCT and fluorescein angiography or OCT angiography,
showing subretinal hyperreflective material, exudation, and leakage consistent with neovascular
membranes secondary to pathological myopia;
Glaucoma—diagnosed by a combination of characteristic optic nerve head changes (increased cup-to-disc
ratio and rim thinning), corresponding visual field defects on standard automated perimetry, and intraocular
pressure measurements, according to the European Glaucoma Society guidelines;
Retinal detachment—confirmed by a dilated fundus examination and/or ultrasonography, showing
separation of the neurosensory retina from the retinal pigment epithelium, with retinal breaks identified in
most cases.
All diagnoses were made by board-certified ophthalmologists at the participating centres, and patients with
secondary causes unrelated to high myopia were excluded.

Data sources
For this multicentre cohort study, routine clinical laboratory data were extracted from the patients’ electronic
medical records. Each institution’s principal investigator collected information on 61 laboratory parameters
per patient, which were classified into three major categories: blood cell analysis parameters, biochemical
analysis parameters, and coagulation analysis parameters.

At presentation, ophthalmologists first evaluated whether patients had any prior diagnosis or treatment for
high myopia-related complications. Those with previously diagnosed complications or a history of treatment
for such conditions were excluded to ensure that the laboratory measurements reflected the onset of newly
identified disease. For patients newly diagnosed with high myopia or high myopia-related complications,
venous blood samples were collected within 24 hours of presentation. Laboratory tests obtained during
stable or follow-up periods were not included in this analysis. Blood samples were obtained via standard
venipuncture and analysed at multiple clinical laboratories, including the Eye & ENT Hospital of Fudan
University, Putuo District Central Hospital, the First Affiliated Hospital of Anhui Medical University, Shanghai
Xuhui Central Hospital, and Wanbei Coal–Electricity Group General Hospital. The procedures for the
detailed laboratory analyses have been described previously18. The blood cell analyses included 24
variables, the biochemical analyses included 31 variables, and the coagulation analyses included 6
variables, as detailed in Table S13.

The selection of these haematological, biochemical, and coagulation parameters was guided by two main
considerations: availability and biological plausibility supported by prior research. First, in terms of
availability, all 61 parameters are routinely measured in standard clinical practice, which greatly enhances
the feasibility and potential clinical utility of our model. Second, regarding biological plausibility, prior
evidence supports the relevance of these parameters in ocular risk assessments. Recent studies have
demonstrated that routinely available blood test data can be effective at predicting high myopia-related
complications, particularly retinal detachment18,19. Blood markers of inflammation49, glucose levels50, and
lipid levels51 have been linked to an increased risk of ocular diseases, including high myopia and retinal
detachment. In addition, several haematological, biochemical, and coagulation parameters have been
reported to be associated with the onset or severity of other eye diseases, such as glaucoma52–54,
cataract55–57, and macular degeneration58,59. Therefore, based on the prior evidence, we developed a
machine learning–based screening model using routine blood test data to identify individuals at risk for high
myopia-related complications (cataracts, macular degeneration, choroidal neovascularization, glaucoma,
and retinal detachment).

Blood Sample Quality Control
Interlaboratory and intralaboratory quality control measures were implemented to ensure the reliability and
consistency of laboratory results across multiple centres. All the centres participated in external proficiency
testing organized by the Clinical Laboratory Center using standardized survey samples that were analysed
locally and evaluated centrally, with all the centres meeting the required criteria. In addition, standardized
internal quality control (IQC) materials were measured daily before patient samples were processed to
ensure assay precision, and the coefficient of variation (CV) was maintained within 5%. These procedures
ensured the accuracy, precision, and comparability of the haematological data across all the study sites.

Machine learning (ML) model development
A total of 61 features (detailed in Table S13) were utilized to develop diagnostic models. For each participant,
if more than 20% of the 61 laboratory variables were missing (i.e., 13 variables), the participant was
excluded from the study. Conversely, participants with ≤20% missing values (i.e., 12 variables) were
retained, and any remaining missing values for these variables were imputed using the medians60. Ten ML
algorithms were employed to ensure a diverse and robust modelling approach, namely, the RF, AdaBoost,
decision tree (DT), gradient boosting (GB), k-nearest neighbours (KNN), light gradient boosting machine
(LightGBM, LGBM), logistic regression (LR), multilayer perceptron (MLP), naïve Bayes (NB), and XGBoost
algorithms. Multiple classification methods were used to screen for different subtypes of high myopia
complications. We applied a Synthetic Minority Oversampling Technique (SMOTE) approach to adjust for
an unequal class distribution and mitigate the effect of class imbalance during model development. Each
algorithm was selected based on its ability to balance interpretability and predictive power and capture both
linear and nonlinear interactions among features. A combination of grid search and manual fine-tuning was
implemented to identify the optimal hyperparameters and optimize model performance. The final
hyperparameters for each of the ten machine learning models are detailed in Table S14. All modelling
procedures were conducted in Python (v3.11), and the corresponding code is available at
https://github.com/fudanRenjun/high-myopia/tree/master.

Model performance evaluation
The models were assessed with multiple performance metrics, including precision, recall, PPV, NPV, F1
score, and accuracy. Furthermore, their classification capabilities were evaluated by calculating AUC and
PR-AUC. The DeLong test61, a nonparametric statistical method, was used to compare AUC values across
the different machine learning models. The optimal cut-off value for the models was determined as the
value that maximized the Youden index and the sum of sensitivity and specificity. Based on this threshold,
the sensitivity, specificity, accuracy, PPV, and NPV were calculated to evaluate model performance62.
Decision curve analysis63 and calibration curves64 were used to further assess the performance of the model.

Feature selection and model interpretation
In the discovery cohort, feature selection was performed with the SHAP method65. The process refined the
predictive model by narrowing the 61 input features to the most critical predictors while maintaining optimal
predictive performance. The feature selection process was embedded within cross-validation to avoid
information leakage and ensure a fair comparison among the models. The DeLong test61 was further
applied to compare the AUC values of the models before and after feature selection, validating the impact
of this dimensionality reduction process. The SHAP analysis also provides both global interpretability and
local interpretability, offering insights into overall model behaviour and individualized predictions.

To facilitate clinical adoption, the final predictive model was deployed as a web-based application
(https://high-myopia.streamlit.app/) using the Streamlit framework in Python to facilitate clinical adoption.
By inputting the nine selected features (Table S15)—EOS, PCT, BASO%, M, UA, LPB, BASO, EOS%, and
TG levels—which are routinely available in typical clinical settings, the clinician receives probability
estimates for different high myopia-related complications along with a personalized force plot that visually
explains the prediction for individual patients. This interactive tool enhances the interpretability of the model
and supports clinical decision-making.

Statistical analysis
All the statistical analyses were performed using GraphPad Prism (v10) and Python (v3.11). The
Kolmogorov–Smirnov test was applied to assess the normality of the data distribution. For continuous
variables with a normal distribution, one-way analysis of variance (ANOVA) was used, whereas the
Kruskal–Wallis test was used for non-normally distributed data. Categorical variables were analysed using
the chi-square test, where appropriate. A false discovery rate correction was applied using the Benjamini–
Hochberg method to account for multiple comparisons and control for type I error. Effect sizes were also
calculated: η² (eta squared) for continuous variables from one-way ANOVA and Cramér’s V for categorical
variables. Continuous data are presented as the means ± standard deviations (SDs), whereas categorical
data are summarized as counts and percentages. A two-tailed P value < 0.05 was considered to indicate
statistical significance.

Statistics & Reproducibility
No statistical method was used to predetermine sample size. For each participant, if more than 20% of the
61 laboratory variables were missing (i.e., 13 variables), the participant was excluded from the study. The
experiments were not randomized. The Investigators were not blinded to allocation during experiments and
outcome assessment.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Overall comment\nThis manuscript presents a machine learning–based screening model using routine blood tests to identify high myopia-\nrelated complications. The approach is innovative and potentially valuable, especially in resource-limited settings, and the\nauthors provide large-scale validation and a web-based tool for clinical use. The idea of leveraging routine blood tests for\nocular disease screening, particularly for high myopia complications, is innovative and may offer a low-cost, scalable\nsolution in settings with limited ophthalmic resources. While blood-based markers have been explored in other systemic\ndiseases, this represents a relatively novel application in ophthalmology. If validated in broader populations, the tool could\nhave significant impact. However, several important concerns need to be addressed. Please, see my specific comments\nbelow.\nAbstract\n• Lines 40 to 43: The sensitivity of 70% indicates that a notable proportion of true cases may be missed, which could have\nclinical implications in early detection and management. It would be beneficial to include confidence intervals for these\nmetrics and provide further details on the diagnostic criteria used for defining high myopia-related complications.\n• Lines 45 to 46: While the proposed model shows promise as a scalable tool for early detection of high myopia-related\ncomplications, the claim of cost-effectiveness is not supported by the current data. The authors did not conduct a formal cost-\neffectiveness analysis, nor did they provide comparative data on resource use or economic impact. As such, this statement\nshould be revised or more cautiously framed to reflect the actual scope of the study findings.\nIntroduction\n• Lines 48 to 49: The statement that high myopia is a global epidemic may be overstated. While myopia itself is widely\nrecognized as a growing global public health concern, the term \"epidemic\" is more commonly applied to overall myopia\nprevalence rather than high myopia specifically. Although the prevalence of high myopia is increasing, particularly in East\nAsia, it remains substantially lower than that of general myopia in most regions. I recommend the authors rephrase this\nsentence to more accurately reflect the epidemiological distinction, perhaps by stating that myopia is a global epidemic, with\nhigh myopia posing a growing concern due to its associated risk of complications.\n• Lines 86 to 88; 99 to 101: There are inconsistencies in the description of the study design that require clarification. The\nstudy is described as \"retrospective in nature,\" yet the hospital-based cohort is referred to as \"prospective,\" and a\n\"prospective screening program phase\" is also mentioned. These discrepancies may confuse readers and should be\nresolved by clearly distinguishing between the retrospective case–control development phase and the prospective\nvalidation phase. Additionally, the use of a cross-sectional design in the community-based cohort limits the ability to draw\ntemporal or causal conclusions. Furthermore, as both cohorts are derived from Asian populations, the generalizability of the\nfindings to other regions may be limited. The authors should acknowledge this and discuss the need for external validation\nin more diverse populations.\nMethods\n• Lines 114 to 123: The composition of the discovery and validation cohorts raises concerns about class imbalance. In each\ncohort, the number of patients with high myopia–related complications exceeds the number of patients with uncomplicated\nhigh myopia. This disproportion may affect model training and performance, particularly in real-world screening contexts\nwhere the prevalence of complications is typically lower. The authors should discuss how this class distribution may have\ninfluenced the model’s predictive performance and generalizability. Additionally, clarification is needed on how the cohorts\nwere sampled and whether any balancing techniques (e.g., weighting, resampling) were applied to account for this\nimbalance during model development and validation.\n• Line 129 & 139: The use of the present tense, “are expected to be recruited”, suggests that patient recruitment is ongoing or\nplanned, which appears inconsistent with other parts of the manuscript that describe the study and model validation in the\npast tense. To maintain consistency and clarity, this sentence should be revised to reflect whether recruitment has already\noccurred (e.g., “were recruited”) or is still anticipated.\n• Lines 130 to 132; 141 to 143: The sentence indicates that only individuals identified as positive by the screening model\nunderwent confirmatory ophthalmic examinations. This raises concerns about potential verification bias, as it is unclear\nwhether individuals classified as negative by the model were also assessed to confirm the absence of complications.\nWithout systematic evaluation of both positive and negative cases, the reported sensitivity, specificity, and predictive values\nmay be overestimated. The authors should clarify whether any subset of model-negative individuals underwent confirmatory\ntesting, and if not, acknowledge this as a limitation that may affect the validity of the model’s performance metrics.\n• Lines 154 to 157: This sentence provides a thorough list of ophthalmic evaluations performed; however, it omits a key\ndetail relevant to the study population: how high myopia was defined and measured. Since refractive error is central to\nclassifying high myopia, the authors should specify whether refractive error was measured using cycloplegic or non-\ncycloplegic refraction, and clearly state the cut-off used for inclusion. In addition the authors should define both\nuncomplicated and complication-associated high myopia.\n• Lines 162 to 167: The statements \"Laboratory tests were conducted at the time of disease onset\" and \"Blood laboratory\ntests were performed at the same time as disease diagnosis\" require further clarification. First, it is unclear whether the\nauthors are referring to the onset or diagnosis of the high myopia-related complications, or to high myopia itself. Additionally,\nfor many of the complications listed (e.g., macular degeneration, glaucoma), the exact onset is difficult to determine clinically.\nThe authors should clarify whether laboratory tests were conducted at the time of initial diagnosis of the complication, and\nhow this timing was standardized across participants.\n• Lines 168 to 186: While the manuscript presents an extensive set of haematological, biochemical, and coagulation\nparameters, it is unclear whether all these variables have a known or plausible association with high myopia or its\ncomplications. The authors should clarify the rationale for including these specific parameters. Were they selected based on\nprior evidence, clinical relevance, or data-driven methods? Without a theoretical or empirical justification, the inclusion of so\nmany variables raises concerns about model interpretability and the potential for spurious associations. A discussion of the\nbiological plausibility of the key predictive features, particularly those most strongly associated with high myopia-related\ncomplications, would enhance the scientific value of the model and help readers understand its relevance to ocular\npathology.\n• Line 218: The phrase “By inputting relevant feature values” is vague and should be more clearly defined. Given the large\nnumber of laboratory parameters used in model development, it is unclear which features are required for input into the web-\nbased application. Are all 60+ variables needed, or was the model reduced to a more practical subset for clinical use? The\nauthors should specify which inputs are required for the tool to function and discuss whether these tests are routinely\navailable in typical clinical settings.\nResults\n• Lines 242 to 243: Given the large number of comparisons made, the authors should clarify whether any correction for\nmultiple testing (e.g., Bonferroni or false discovery rate) was applied to control for type I error. Additionally, it would be\nhelpful to report effect sizes or clinical relevance, not just statistical significance, to better understand whether these\ndifferences are meaningful in a diagnostic or pathophysiological context. Simply stating that 70% of parameters were\nsignificantly different risks overstating their utility without this additional context.\n• Lines 251 to 257: The reported AUC values for both the RF and XGBoost models are notably high across all categories,\nsuggesting strong overall discriminative performance. However, several important clarifications are needed. First, only a\nsingle sensitivity (0.76) and specificity (0.97) are provided for the RF model, without corresponding metrics for each specific\ncomplication. Given that model performance can vary significantly across disease subtypes, the authors should report\nsensitivity, specificity, PPV, and NPV for each condition separately. Second, the relatively modest sensitivity contrasts with\nthe high AUCs and warrants explanation. This discrepancy may reflect issues related to threshold selection, class\nimbalance, or overfitting. Third, the manuscript should include 95% confidence intervals for all key performance metrics\n(AUC, sensitivity, specificity, etc.) to assess the precision and robustness of these estimates. Finally, if performance\ndifferences between the RF and XGBoost models were formally tested (e.g., using the DeLong test), the results should be\nreported.\n• Lines 268 to 275: Although the nine-feature RF model was ultimately selected, the criteria for model selection seem to rely\nprimarily on AUC and feature count. The authors should also comment on model interpretability, clinical feasibility (e.g., are\nthese nine features routinely available?), and potential trade-offs in performance across subgroups. Lastly, it would be\nbeneficial to specify whether the feature selection process was embedded within cross-validation to avoid information\nleakage and ensure a fair model comparison.\n• Lines 296 to 299: The inclusion of a confusion matrix and reporting of sensitivity (0.56–0.84) and specificity (0.76–0.99)\nranges is useful in highlighting the variability in the model’s performance across subgroups. However, this range, particularly\nthe lower end of sensitivity (0.56), raises concerns about the model’s ability to reliably detect certain high myopia\ncomplications. The authors should specify which subgroups correspond to the lowest sensitivity and discuss the clinical\nimplications of these missed cases. Finally, while Figure S3B shows distributions and correlations of the nine variables, a\nbrief summary in the main text regarding potential multicollinearity and the clinical relevance of these features would\nenhance the interpretability of the model.\n• Lines 350 to 354: Among the 1,523 individuals classified as high risk, only 1,312 were referred for further ophthalmic\nevaluation. The authors should explain why 211 high-risk individuals were not followed up—was this due to loss to follow-\nup, refusal, or logistical constraints? The identification of 347 cases of complications among low-risk individuals suggests a\nsubstantial false-negative rate and warrants a deeper discussion of the model’s sensitivity in clinical practice.", "reviewer_2": "A Machine Learning–Based Framework Using Routine Blood Tests for Identifying Complications in\nPatients with High Myopia\nA interesting paper indeed. Need some clarification of methods and perhaps better exploration of\nclinical role and utility within a community of hospital workflow.\nIntroduction\nline 48 Reference 1 in fact defines high myopia in a non-standard way at <= -5D, consider changing reference.\nline 50-55 Most of the complications and references relate to both low myopia and high myopia and gives the false\nimpression that\nonly high myopia leads to complications. The complications risk/ frequency is certainly higher in high myopia but there are a\nlot fewer high myopes as indicated by the citation of ref 11 - “high myopia accounts for more than 50% of cases of\npathological\nmyopia”. At a population level lower myopia is important, for individual risk profiling high myopia is more important.\nAlso the point that is not clearly made that of all complications it is MMD that remains without effective interventions, but\nearly intervention is important for some like glaucoma and RD (i.e. laser for high risk retinal lesions).\nIntro para 2 and 3 line 57-82\nGood set up of rationale of what at first look seems an unusual approach.\nMethods line 110 - how was this exclusion done?\nline 114 what was the age/sex difference between uncomplicated and complicated high myopia. Important in terms of strong\nage dependence on the complications of high myopia.\nLine 160 was the 61 chosen for availability or was any biological plausibility or prior research?\nLine 162 “Laboratory tests were conducted at the time of disease onset,” clarify, surely some follow up measurements\nline 168-187 a table would be easier to read I would think, keeping the separation in blood/bio/coagulation groups\n188 how many variables and which ones dropped. I’d recommend saying from an initial 61 blood biomarkers, X (list these)\nwere dropped and table X lists the variables used in the model. Otherwise readers won’t know what input actually are and\n168-187 is misleading.\n212 vs 188, is it 61 or 61 minus the variables with > 20% missing? needs clarity and consistency here.\n217-221 Is there a link for this? I see it is listed at line 342-345 https://high-myopia.streamlit.app/\n226 “non-normally”\n366 for interpretation of clinical utility in community validation also need sensitivity to assess risk of false negatives with NPV\n409 - 415 with the PPV ranging from 0.65 to 0.795, with the blood models, there are still going to be a lot of false positives\nand worth discussing merits role of blood as as pre-screening tool\nDiscussion needs inclusions limitations, generalizability across difference regions eg asia vs west and role within a clinical\nor community work flow see comment above re 409.", "reviewer_3": "This study developed a machine learning framework using routine blood tests to screen for high myopia-related\ncomplications. The research enrolled 10,661 patients and utilized 61 hematological parameters to train 10 machine learning\nalgorithms. The random forest model based on 9 key features (eosinophil count, thrombocytocrit, basophil percentage, etc.)\ndemonstrated optimal performance with a macro-average AUC of 0.9649 in the discovery cohort. The model achieved\n70.0% sensitivity and 94.0% specificity in hospital-based prospective validation and 74% positive predictive value in\ncommunity-based cross-sectional study. Notably, the study identified associations between inflammatory markers\n(particularly eosinophils and basophils) and retinal detachment and choroidal neovascularization. While the technical\nexecution is sound, the fundamental clinical utility question needs to be addressed. The authors should consider\nredirecting their findings toward prognostic applications rather than screening applications, which would significantly enhance the\nclinical relevance and impact of this work.\n1.Major Concerns: Retinal detachment and choroidal neovascularization can currently be rapidly diagnosed through fundus\nphotography at lower cost and higher accuracy. Blood-based screening is prone to false positives, potentially increasing\nunnecessary ophthalmologic examination costs rather than reducing them. The clinical decision pathway lacks clear\njustification for why blood testing should precede direct ophthalmologic examination.\n2.Methodological Limitations: First, the retrospective case-control design introduces inherent selection bias. And missing\ndiscussion of how acute systemic conditions might confound inflammatory markers. In addition, the study is with limited\nvalidation across different ethnic populations and healthcare systems.\n3.Limited Clinical Significance: While the technical achievement is noteworthy, the clinical significance is questionable. The\nproposed screening approach may not offer advantages over existing diagnostic methods (fundus image) for the studied\ncomplications. The identification of inflammatory pathways is interesting but not sufficiently novel given existing literature on\ninflammation and retinal diseases.\n4.Data and MethodsThe study exhibits notable methodological strengths, including a large multi-center cohort of 10,661\npatients, comprehensive analysis of 61 blood parameters, and robust statistical approaches with appropriate external\nvalidation and feature selection using SHAP methodology.\nHowever, critical limitations significantly compromise clinical applicability. The retrospective design lacks longitudinal\nfollow-up to establish causality between biomarkers and disease onset. Crucially missing are details on blood sampling\ntiming relative to diagnosis and cost-effectiveness analysis comparing blood-based screening to existing imaging methods.\nThe study inadequately addresses potential confounding from medications, comorbidities, and systemic inflammatory\nconditions that could influence the identified blood parameters.\n5.Analytical Methods：The study demonstrates strong statistical rigor with appropriate use of multiple machine learning\nalgorithms, cross-validation, proper SHAP implementation for interpretability, and comprehensive performance metrics\nincluding calibration curves and decision curve analysis.However, critical analytical gaps limit clinical applicability. The\nstudy lacks sensitivity analyses excluding patients with systemic inflammatory conditions, missing analysis of optimal\nscreening intervals, and most importantly, absent cost-benefit modeling to justify clinical implementation over existing\nimaging-based diagnostic approaches.\n6.References：The manuscript adequately cites relevant literature on machine learning applications in ophthalmology and\ninflammatory markers in eye disease. However, it lacks sufficient discussion of recent advances in point-of-care\nophthalmologic imaging and cost-effectiveness studies of screening programs.\n7. Suggested Improvements\nLine 32-33: Replace \"effective strategies for identifying these complications within high myopia populations remain largely\nunavailable\" with more nuanced statement acknowledging existing imaging-based methods\nLine 46: Qualify the conclusion by specifying target populations where blood-based screening would be most appropriate\nLines 57-65: Expand discussion of current imaging limitations to include recent advances in portable fundus cameras and\nsmartphone-based imaging\nLines 66-72: Provide more detailed cost analysis comparing blood tests (~300 RMB) with basic fundus photography costs\nLines 83-91: Add specific research hypothesis about when blood testing would be clinically superior to direct examination\nLines 110-113: Expand exclusion criteria to include patients with active systemic inflammatory conditions, recent infections,\nor immunosuppressive medications\nLines 114-123: Report the time interval between blood sampling and ophthalmologic diagnosis for each patient group\nLines 162-167: Specify exact timing of blood collection relative to symptom onset and clinical diagnosis\nLines 168-186: Add inflammatory markers beyond routine CBC (e.g., CRP, ESR, specific cytokines) to strengthen\nmechanistic understanding\nAdd new paragraph: Describe quality control measures for blood sample collection and processing across multiple centers\nLines 268-279: Provide biological rationale for why these specific 9 features were mechanistically relevant to each\ncomplication type\nLines 280-289: Add subgroup analysis excluding patients with concurrent systemic inflammatory conditions\nAdd new analysis: Compare model performance in early-stage vs. advanced disease to determine optimal screening timing\nLines 346-357: Include breakdown of false positive rates by complication type and their downstream healthcare costs\nLines 358-369: Add analysis of patients who were blood-test negative but later developed complications during follow-up\nperiod\nLines 379-388: Restructure to focus on prognostic value rather than screening utility - analyze whether biomarker levels\npredict treatment response or disease progression\nLines 429-434: Propose specific clinical scenarios where blood testing would be the preferred first-line approach\nLines 379-384: Expand discussion of IL-4/IL-13 pathways to include therapeutic implications - could anti-inflammatory\ntreatments modify risk?\nLines 385-388: Add analysis of whether biomarker levels correlate with disease severity or treatment outcomes\nAdd sensitivity analysis: Exclude patients with diabetes, hypertension, or other systemic conditions affecting the 9\nbiomarkers"}, "author_response": "Response: We sincerely thank the reviewer for recognizing the novelty of our study and its potential\nclinical applicability, especially in resource-limited settings. We greatly appreciate your constructive\nfeedback and insightful suggestions, which have helped us improve the quality and clarity of the\nmanuscript. We have carefully revised the manuscript in accordance with your comments and have\nprovided detailed, point-by-point responses to each specific concern below.\nAbstract • Lines 40 to 43: The sensitivity of 70% indicates that a notable proportion of true cases may be\nmissed, which could have clinical implications in early detection and management. It would be beneficial\nto include confidence intervals for these metrics and provide further details on the diagnostic criteria used\nfor defining high myopia-related complications.\nResponse: Thank you for this insightful comment. We agree with your concern regarding the importance\nof reporting confidence intervals and clarifying diagnostic criteria. In the revised manuscript, we have\nadded the corresponding 95% confidence intervals for all the reported performance metrics. We have also\nspecified the high myopia-related complications evaluated in this study (high myopia (refractive error ≤\n−6.0 dioptres)-related cataracts, macular degeneration, choroidal neovascularization, glaucoma, and\nretinal detachment) and provided a detailed description of the diagnostic criteria for each in the Methods\nsection.\n1\n• Lines 45 to 46: While the proposed model shows promise as a scalable tool for early detection of high\nmyopia-related complications, the claim of cost-effectiveness is not supported by the current data. The\nauthors did not conduct a formal cost-effectiveness analysis, nor did they provide comparative data on\nresource use or economic impact. As such, this statement should be revised or more cautiously framed to\nreflect the actual scope of the study findings.\nResponse: Thank you for this insightful comment. We agree that our study did not include a formal cost-\neffectiveness analysis or comparative data on resource utilization or economic impacts. Accordingly, we\nhave removed the term “cost-effective” from the manuscript and revised the statement to more accurately\nreflect the scope of our findings. The sentence now reads as follows:\n“This scalable and easy-to-implement screening model based on routine blood tests enables the early\ndetection of complications in high myopia populations, particularly in health examination centres and\ncommunity primary care settings.”\nIntroduction\n• Lines 48 to 49: The statement that high myopia is a global epidemic may be overstated. While myopia\nitself is widely recognized as a growing global public health concern, the term \"epidemic\" is more commonly\napplied to overall myopia prevalence rather than high myopia specifically. Although the prevalence of high\nmyopia is increasing, particularly in East Asia, it remains substantially lower than that of general myopia in\nmost regions. I recommend the authors rephrase this sentence to more accurately reflect the\nepidemiological distinction, perhaps by stating that myopia is a global epidemic, with high myopia posing\na growing concern due to its associated risk of complications\nResponse: Thank you for this insightful comment. We agree with your observation regarding the\nepidemiological distinction between myopia and high myopia. In the revised manuscript, we have\nrephrased the sentence as follows to more accurately reflect this distinction:\n“Myopia is a global epidemic, with high myopia identified as a growing concern because of its associated\nrisk of complications”\n2\n• Lines 86 to 88; 99 to 101: There are inconsistencies in the description of the study design that require\nclarification. The study is described as \"retrospective in nature,\" yet the hospital-based cohort is referred\nto as \"prospective,\" and a \"prospective screening program phase\" is also mentioned. These discrepancies\nmay confuse readers and should be resolved by clearly distinguishing between the retrospective case–\ncontrol development phase and the prospective validation phase. Additionally, the use of a cross-sectional\ndesign in the community-based cohort limits the ability to draw temporal or causal conclusions.\nFurthermore, as both cohorts are derived from Asian populations, the generalizability of the findings to\nother regions may be limited. The authors should acknowledge this and discuss the need for external\nvalidation in more diverse populations.\nResponse: Thank you for your comment. We have further clarified the study design. This study was\nconducted in three phases. First, a multicentre retrospective case–control study was performed to develop\nand internally and externally validate the machine learning-based model using routine blood test data from\npatients with high myopia, both with and without complications. Second, the clinical performance of the\nmodel was prospectively validated in a hospital-based cohort. Finally, the model was further evaluated in\na community-based cross-sectional study to examine its utility for population-level screening. Given that\nthe multicentre case–control study was noninterventional and retrospective in nature, the institutional\nreview boards waived the requirement for informed consent. In the prospective hospital-based screening\nphase, informed consent was obtained from all participants.\nWe also acknowledge that the cross-sectional design of the community-based cohort limits our ability to\ndraw temporal or causal conclusions. Additionally, as both cohorts were derived from Asian populations,\nthe generalizability of our findings to patients from other regions may be limited. These limitations have\nbeen explicitly acknowledged in the revised manuscript, and we emphasize the need for external validation\nin more diverse populations.\nMethods\n• Lines 114 to 123: The composition of the discovery and validation cohorts raises concerns about class\nimbalance. In each cohort, the number of patients with high myopia–related complications exceeds the\nnumber of patients with uncomplicated high myopia. This disproportion may affect model training and\n3\nperformance, particularly in real-world screening contexts where the prevalence of complications is\ntypically lower. The authors should discuss how this class distribution may have influenced the model’s\npredictive performance and generalizability. Additionally, clarification is needed on how the cohorts\nwere sampled and whether any balancing techniques (e.g., weighting, resampling) were applied to account for\nthis imbalance during model development and validation.\nResponse: Thank you for this insightful comment. As noted, in both the discovery and validation cohorts,\nthe number of patients with high myopia-related complications exceeded that of patients with\nuncomplicated high myopia, which could influence model training and performance, especially in real-\nworld screening where the complication prevalence is lower. This imbalance reflects the nature of our study\nsetting: as a tertiary referral centre for eye diseases, we receive a disproportionately high number of\npatients with suspected complications.\nWe applied a Synthetic Minority Oversampling Technique (SMOTE) approach to adjust for unequal class\ndistribution and mitigate the effect of class imbalance during model development. The model demonstrated\ngood performance not only in the validation cohort but also in a hospital-based prospective cohort and a\ncommunity-based cross-sectional study. Nonetheless, we acknowledge that even with SMOTE, the\noverrepresentation of complication cases may still limit generalizability in populations with lower\nprevalence.\nRegarding the cohort composition, in the case–control phase, all patients with high myopia who visited the\none of the five participating hospitals between January 1, 2016, and December 30, 2024, were included\nwithout sampling. In contrast, participants in the hospital-based prospective and community-based cross-\nsectional studies were enrolled through large-scale population-based designs, minimizing sampling bias.\nWe have now clarified the SMOTE approach in the Methods section and added the potential limitation of\nclass imbalance to the Discussion section.\n• Line 129 & 139: The use of the present tense, “are expected to be recruited”, suggests that patient\nrecruitment is ongoing or planned, which appears inconsistent with other parts of the manuscript that\ndescribe the study and model validation in the past tense. To maintain consistency and clarity, this\n4\nsentence should be revised to reflect whether recruitment has already occurred (e.g., “were recruited”) or\nis still anticipated.\nResponse: Thank you for your comment. We have revised the sentences for tense consistency as follows:\n“Briefly, all patients with high myopia who presented to the hospital from January 2025 to May 2025 were\nsequentially enrolled, with approximately 5,000 patients recruited during the study period.”\n“Approximately 300,000 residents of Xuhui District were included.”\n• Lines 130 to 132; 141 to 143: The sentence indicates that only individuals identified as positive by the\nscreening model underwent confirmatory ophthalmic examinations. This raises concerns about potential\nverification bias, as it is unclear whether individuals classified as negative by the model were also assessed\nto confirm the absence of complications. Without systematic evaluation of both positive and negative cases,\nthe reported sensitivity, specificity, and predictive values may be overestimated. The authors should clarify\nwhether any subset of model-negative individuals underwent confirmatory testing, and if not, acknowledge\nthis as a limitation that may affect the validity of the model’s performance metrics.\nResponse: Thank you for this insightful comment. In the hospital-based prospective cohort study,\nindividuals who were identified as positive for high myopia-related complications according to the\nestablished screening model underwent confirmatory ophthalmic examinations to verify the presence of\nthese complications. In addition, individuals classified as negative by the model also underwent ophthalmic\nexaminations to confirm the absence of such complications.\nIn the community-based cross-sectional study, however, individuals identified as negative by the model\ndid not undergo further ophthalmic examinations. We acknowledge this limitation: only participants who\nscreened positive underwent confirmatory ophthalmic examinations, whereas model-negative individuals\nwere not systematically assessed. The screening of approximately 300,000 model-negative participants\nwould have required resources and capacity beyond what was available to the research team. We agree\nthat this approach may introduce verification bias and potentially overestimate the model’s performance\nmetrics.\nthe Discussion section.\n• Lines 154 to 157: This sentence provides a thorough list of ophthalmic evaluations performed; however,\nit omits a key detail relevant to the study population: how high myopia was defined and measured. Since\nrefractive error is central to classifying high myopia, the authors should specify whether refractive error\nwas measured using cycloplegic or non-cycloplegic refraction, and clearly state the cut-off used for\ninclusion. In addition the authors should define both uncomplicated and complication-associated high\nmyopia.\nResponse: Thank you for this insightful comment. In our study, refractive error was measured using\ncycloplegic refraction, and high myopia was defined as a refractive error of ≤ −6.0 dioptres. We have\nrevised the Methods section to clearly state this definition and to provide detailed diagnostic criteria for\nboth uncomplicated high myopia and high myopia with complications.\n• Lines 162 to 167: The statements \"Laboratory tests were conducted at the time of disease onset\" and\n\"Blood laboratory tests were performed at the same time as disease diagnosis\" require further clarification.\nFirst, it is unclear whether the authors are referring to the onset or diagnosis of the high myopia-related\ncomplications, or to high myopia itself. Additionally, for many of the complications listed (e.g., macular\ndegeneration, glaucoma), the exact onset is difficult to determine clinically. The authors should clarify\nwhether laboratory tests were conducted at the time of initial diagnosis of the complication, and\nhow this timing was standardized across participants.\nResponse: Thank you for this insightful comment. We have clarified the description in the revised\nmanuscript. Upon presentation to the clinic, ophthalmologists first confirmed whether participants had any\nprior diagnosis or treatment for high myopia-related complications; patients with previously diagnosed\ncomplications were excluded. For participants who presented with high myopia or newly developed high\nmyopia-related complications, blood samples for laboratory tests were collected at the time of initial\ndiagnosis of the complication. This approach ensured that laboratory tests were standardized relative to\n6\nthe onset of the complication across participants. This information has been incorporated into the Methods\nsection in the revised manuscript.\n• Lines 168 to 186: While the manuscript presents an extensive set of haematological, biochemical, and\ncoagulation parameters, it is unclear whether all these variables have a known or plausible association\nwith high myopia or its complications. The authors should clarify the rationale for including these specific\nparameters. Were they selected based on prior evidence, clinical relevance, or data-driven methods?\nWithout a theoretical or empirical justification, the inclusion of so many variables raises concerns about\nmodel interpretability and the potential for spurious associations. A discussion of the biological plausibility\nof the key predictive features, particularly those most strongly associated with high myopia-related\ncomplications, would enhance the scientific value of the model and help readers understand its relevance\nto ocular pathology.\nResponse: Thank you for this insightful comment. The selection of these haematological, biochemical,\nand coagulation parameters was guided by two main considerations: availability and biological plausibility\nsupported by prior research. First, in terms of availability, all 61 parameters are routinely measured in\nstandard clinical practice, which greatly enhances the feasibility and potential clinical utility of our model.\nSecond, regarding biological plausibility, prior evidence supports the relevance of these parameters in\nocular risk assessments.\nRecent studies have shown that routinely available blood test data can be effective at predicting high\nmyopia-related complications, particularly retinal detachment [1,2]. Blood markers of inflammation [3],\nglucose levels [4], and lipid levels [5] have been linked to an increased risk of ocular diseases, including\nhigh myopia and retinal detachment. In addition, several haematological, biochemical, and coagulation\nparameters have been reported to be associated with the onset or severity of other eye diseases, such as\nglaucoma [6–8], cataracts [9–11], and age-related macular degeneration (AMD) [12,13].\nTherefore, based on availability and prior evidence, we developed a machine learning-based screening\nmodel using routine blood test data to identify individuals at risk of high myopia-related complications\n(cataracts, macular degeneration, choroidal neovascularization, glaucoma, and retinal detachment).\nscientific value of the model and enhance its relevance to ocular pathology. Accordingly, we have\nincorporated this discussion into the Discussion section of the revised manuscript.\nReferences:\n[1] Li, S. et al. Developing and validating a clinlabomics-based machine-learning model for early detection\nof retinal detachment in patients with high myopia. J Transl Med 22, 405 (2024).\n[2] Li, S. et al. Development and validation of a routine blood parameters-based model for screening the\noccurrence of retinal detachment in high myopia in the context of PPPM. EPMA J 14, 219–233 (2023).\n[3] Long Q, Ye J, Li Y, Wang S, Jiang Y. C-reactive protein and complement components in patients with\npathological myopia. Optom Vis Sci. 2013;90(5):501–6.\n[4] Arndt C, Hubault B, Hayate F, et al. Increased intravitreal glucose in rhegmatogenous retinal\ndetachment. Eye (Lond). 2023;37(4):638–43.\n[5] Luo S, Xu H, Yang L, et al. Quantitative proteomics analysis of human vitreous in rhegmatogenous\nretinal detachment associated with choroidal detachment by data-independent acquisition mass\nspectrometry. Mol Cell Biochem. 2022;477(6):1849–63.\n[6] Li, S., Qiu, Y., Yu, J. et al. Association of systemic inflammation indices with visual field loss progression\nin patients with primary angle-closure glaucoma: potential biomarkers for 3P medical approaches. EPMA\nJournal 12, 659–675 (2021).\n[7] Li S, Gao Y, Shao M, et al. Association between coagulation function and patients with primary angle\nclosure glaucoma: a 5-year retrospective case–control study. BMJ Open 2017;7:e016719.\n[8] Shao M, Li Y, Teng J, Li S and Cao W (2021) Association Between Serum Lipid Levels and Patients\nWith Primary Angle-Closure Glaucoma in China: A Cross Sectional, Case–Control Study. Front.\nMed. 8:618970.\n[9] Li S, Li D, Zhang Y, et al. Association between serum lipids concentration and patients with age-related\ncataract in China: a cross-sectional, case–control study. BMJ Open 2018;8:e021496.\n[10] Mingxi Shao, Danhui Li, Jisen Teng, Yudong Zhang, Shengjie Li, Wenjun Cao; Association Between\nSerum Complement C3 Levels and Age-Related Cataract. Invest. Ophthalmol. Vis. Sci. 2017;58(11):4934-\n4939.\n[11] Li B, Hou X, Ning B, Li X, Zhang M, et al. (2024) Predictive role of the peripheral blood inflammation\nindices neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and systemic\nimmunoinflammatory index (SII) for age-related cataract risk. PLOS ONE 19(11): e0313503.\n[12] Pinna, A., Porcu, T., D’Amico-Ricci, G., Dore, S., Boscia, F., Paliogiannis, P., … Zinellu, A. (2018).\nComplete Blood Cell Count–Derived Inflammation Biomarkers in Men with Age-Related Macular\nDegeneration. Ocular Immunology and Inflammation, 27(6), 932–936.\n[13] Wang Y, Wang M, Zhang X, Zhang Q, Nie J, Zhang M, Liu X, Ma L. The Association between the\nLipids Levels in Blood and Risk of Age-Related Macular Degeneration. Nutrients. 2016; 8(10):663.\n• Line 218: The phrase “By inputting relevant feature values” is vague and should be more clearly defined.\nGiven the large number of laboratory parameters used in model development, it is unclear which features\nare required for input into the web-based application. Are all 60+ variables needed, or was the model\nreduced to a more practical subset for clinical use? The authors should specify which inputs are required\nfor the tool to function and discuss whether these tests are routinely available in typical clinical settings.\nResponse: Thank you for your comment. We have now clearly specified the selected input features for\nthe web-based application, which include EOS, PCT, BASO%, M, UA, LPB, BASO, EOS%, and TG levels.\nAll of these tests are routinely available in typical clinical settings. This clarification has been incorporated\ninto both the Methods and Discussion sections of the revised manuscript.\nResults\n• Lines 242 to 243: Given the large number of comparisons made, the authors should clarify whether any\ncorrection for multiple testing (e.g., Bonferroni or false discovery rate) was applied to control for type I error.\nAdditionally, it would be helpful to report effect sizes or clinical relevance, not just statistical significance,\nto better understand whether these differences are meaningful in a diagnostic or pathophysiological\ncontext. Simply stating that 70% of parameters were significantly different risks overstating their utility\nwithout this additional context.\nResponse: We appreciate the reviewer’s insightful comment. In this study, we applied a false discovery\nrate (FDR) correction (Benjamini–Hochberg method) to account for multiple comparisons and control for\ntype I error. In addition, based on your suggestion, we reported effect sizes for each significant parameter—\neta squared (η²) for continuous variables and Cramér’s V for categorical variables—to quantify the\nmagnitude of group differences (details are provided in Tables S4–S6).\nApproximately 70% of the routine laboratory parameters differed significantly among the six groups (the\noverall high myopia group and the five complication groups). Effect sizes were calculated to better assess\ntheir potential diagnostic and pathophysiological relevance: η² for continuous variables from one-way\nvariables (0.1 = small effect, 0.3 = medium effect, and ≥0.5 = large effect). Notably, 38.71% (discovery\ncohort), 31.15% (validation cohort 1), and 37.7% (validation cohort 2) of the parameters showed a medium\nor large effect size, suggesting potential clinical relevance beyond statistical significance.\nThese additions provide a more balanced interpretation of our findings by ensuring that statistical\nsignificance is accompanied by an assessment of the magnitude of the effect. The corresponding revisions\nhave been incorporated into the Results, Methods, and Supplementary Tables 4-6 of the revised\nmanuscript.\n• Lines 251 to 257: The reported AUC values for both the RF and XGBoost models are notably high across\nall categories, suggesting strong overall discriminative performance. However, several important\nclarifications are needed. First, only a single sensitivity (0.76) and specificity (0.97) are provided for the RF\nmodel, without corresponding metrics for each specific complication. Given that model performance can\nvary significantly across disease subtypes, the authors should report sensitivity, specificity, PPV, and NPV\nfor each condition separately. Second, the relatively modest sensitivity contrasts with the high AUCs and\nwarrants explanation. This discrepancy may reflect issues related to threshold selection, class\nimbalance, or overfitting. Third, the manuscript should include 95% confidence intervals for all key performance\nmetrics (AUC, sensitivity, specificity, etc.) to assess the precision and robustness of these estimates.\nFinally, if performance differences between the RF and XGBoost models were formally tested (e.g., using\nthe DeLong test), the results should be reported.\nResponse: Thank you for your insightful comments.\n(1) The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for each\ncondition have been added to the manuscript.\n(2) We agree that the relatively modest sensitivity contrasts with the high AUCs and warrants an\nexplanation. This discrepancy can be attributed to several factors. First, decision thresholds were selected\nbased on the Youden index to balance sensitivity and specificity rather than maximizing sensitivity alone.\nSecond, a class imbalance was present in the dataset, with some conditions being less frequent than\n10\nothers, which may have reduced the sensitivity of the model to these less common categories. Taken\ntogether, these factors explain why the sensitivity did not fully reflect the exceptionally high AUC values.\nAn awareness of this limitation is important for the interpretation and clinical application of the model,\nparticularly when considering threshold adjustments or future model refinements to optimize sensitivity\nwithout compromising overall accuracy. This point has been discussed in the revised Discussion section.\n(3) Ninety-five percent confidence intervals (95% CIs) for all key performance metrics, including AUC,\nsensitivity, and specificity, have been added.\n(4) Model performance was compared using the DeLong test. The RF (macroaverage AUC = 0.9707) and\nXGBoost (macroaverage AUC = 0.9601) models performed significantly better than the other models\n(macroaverage AUCs ranging from 0.6007 to 0.9561; DeLong test, P < 0.05; Table S8). The DeLong test\nalso revealed no significant difference between the RF and XGBoost models (P = 0.184). Therefore, both\nthe RF and XGBoost algorithms were selected for the subsequent steps in the development of the\nscreening model (Figure 2B, Table S8).\n• Lines 268 to 275: Although the nine-feature RF model was ultimately selected, the criteria for model\nselection seem to rely primarily on AUC and feature count. The authors should also comment on model\ninterpretability, clinical feasibility (e.g., are these nine features routinely available?), and potential trade-\noffs in performance across subgroups. Lastly, it would be beneficial to specify whether the feature selection\nprocess was embedded within cross-validation to avoid information leakage and ensure a fair model\ncomparison.\nResponse: Thank you for your insightful comment. As you noted, the criteria for model selection in our\nstudy primarily relied on the AUC and the number of features. Importantly, the nine selected features are\nroutinely available in clinical practice, which enhances the feasibility of the application of our model.\nIn addition, we recognize the importance of interpretability and subgroup performance. Therefore, we\nemployed a SHAP analysis to provide transparent explanations of feature contributions, and potential\ntrade-offs in performance across subgroups were also considered. Notably, the AUC values for all disease\nsubgroups exceeded 0.9 in the discovery cohort, and no significant decrease in performance was observed\n11\nin any subgroup. Finally, the feature selection process was embedded within cross-validation to avoid\ninformation leakage and ensure a fair comparison among the models.\nAs suggested, this information has been incorporated into the Methods and Discussion sections of the\nrevised manuscript.\n• Lines 296 to 299: The inclusion of a confusion matrix and reporting of sensitivity (0.56–0.84) and\nspecificity (0.76–0.99) ranges is useful in highlighting the variability in the model’s performance across\nsubgroups. However, this range, particularly the lower end of sensitivity (0.56), raises concerns about the\nmodel’s ability to reliably detect certain high myopia complications. The authors should specify which\nsubgroups correspond to the lowest sensitivity and discuss the clinical implications of these missed cases.\nFinally, while Figure S3B shows distributions and correlations of the nine variables, a brief summary in the\nmain text regarding potential multicollinearity and the clinical relevance of these features would enhance\nthe interpretability of the model.\nResponse: Thank you for your valuable comment. The sensitivity and specificity for each disease category\nhave now been added to the manuscript. As shown in the confusion matrix (Figure 4C), the model achieved\nsensitivities of 0.72, 0.85, 0.59, 0.58, 0.75, and 0.56 and specificities of 0.87, 0.76, 0.99, 0.99, 0.99, and\n0.99 for detecting high myopia overall and high myopia with cataracts, macular degeneration, choroidal\nneovascularization, glaucoma, and retinal detachment, respectively. The subgroups with the lowest\nsensitivities were macular degeneration, choroidal neovascularization, and retinal detachment. Clinically,\nmissed cases in these categories could delay diagnosis and treatment for vision-threatening complications.\nThe distributions and correlations of the nine blood test variables in validation cohort 1 are presented in\nFigure S3B. Strong correlations were observed between the EOS and EOS% and between the BASO and\nBASO%, whereas no significant correlations were found among the other variables. This result suggests\nlimited multicollinearity and supports the independent contributions of these features to the model’s\nperformance.\nThese details have been added to the Results and Discussion sections of the revised manuscript.\n12\n• Lines 350 to 354: Among the 1,523 individuals classified as high risk, only 1,312 were referred for further\nophthalmic evaluation. The authors should explain why 211 high-risk individuals were not followed up—\nwas this due to loss to follow-up, refusal, or logistical constraints? The identification of 347 cases of\ncomplications among low-risk individuals suggests a substantial false-negative rate and warrants a deeper\ndiscussion of the model’s sensitivity in clinical practice.\nResponse: Thank you for your comment, and we apologize for the confusion. As shown in Figure 7A, the\n211 high-risk individuals who were not referred for further ophthalmic evaluation included 127 who did not\ncooperate and 84 who withdrew voluntarily. This information has been added to the Results section. We\nagree with your observation that the identification of 347 cases of complications among low-risk individuals\nindicates a considerable false-negative rate. We have expanded the Discussion section to address the\nimplications of these findings for model sensitivity in clinical practice.\nA Machine Learning–Based Framework Using Routine Blood Tests for Identifying Complications in\nPatients with High Myopia\nA interesting paper indeed. Need some clarification of methods and perhaps better exploration of clinical\nrole and utility within a community of hospital workflow.\nResponse: We sincerely thank the reviewer for recognizing the novelty of our study. We greatly appreciate\nyour constructive feedback and insightful suggestions, which have helped us improve the quality and clarity\nof the manuscript. We have carefully revised the manuscript in accordance with your comments and have\nprovided detailed, point-by-point responses to each specific concern below.\nIntroduction\nline 48 Reference 1 in fact defines high myopia in a non-standard way at <= -5D, consider changing\nreference.\nResponse: Thank you for your valuable comment. We have replaced Reference 1 with the following\nupdated citation, which defines high myopia at ≤ -6D:\nFlitcroft, D. I. et al. IMI - Defining and Classifying Myopia: A Proposed Set of Standards for Clinical and\nEpidemiologic Studies. Invest Ophthalmol Vis Sci. 2019;60(3):M20–M30.\nline 50-55 Most of the complications and references relate to both low myopia and high myopia and gives\nthe false impression that only high myopia leads to complications. The complications risk/ frequency is\ncertainly higher in high myopia but there are a lot fewer high myopes as indicated by the citation of ref 11\n- “high myopia accounts for more than 50% of cases of pathological myopia”. At a population level lower\nmyopia is important, for individual risk profiling high myopia is more important.\nAlso the point that is not clearly made that of all complications it is MMD that remains without effective\ninterventions, but early intervention is important for some like glaucoma and RD (i.e. laser for high risk\nretinal lesions).\nResponse: Thank you for your valuable comment. We agree with your suggestion and have revised the\n14\ntext in lines 50–55 to avoid the misleading impression that only high myopia leads to complications. We\nnow clarify that while both low and high myopia are associated with ocular complications at the population\nlevel, the risks and frequencies are substantially higher in patients with high myopia, which remains\nparticularly important for individual risk profiling. We further clarify that effective interventions for myopic\nmacular degeneration are lacking, whereas complications such as glaucoma and retinal detachment can\nbenefit from early detection and timely intervention.\nThe revised text reads as follows:\n“Myopia is a global epidemic, with high myopia identified as a growing concern because of its associated\nrisk of complications1,2. Myopia, particularly high myopia, is not merely a refractive error but is associated\nwith markedly increased risks of vision-threatening complications3,4, including cataracts (3–8-fold5,6),\nglaucoma (~7-fold7), retinal detachment (12–398), myopic macular degeneration (~209,10), and choroidal\nneovascularization (~108), compared with non-myopic eyes. While the risk of complications increases with\nincreasing myopia, the population impact varies: lower degrees are more prevalent and thus contribute\ngreatly to the overall burden, whereas high myopia, although less common, is associated with a much\nhigher individual risk and accounts for more than half of pathological myopia. Some complications, such\nas retinal detachment and glaucoma, may benefit from early detection and timely intervention (e.g.,\nprophylactic laser for high-risk retinal lesions), whereas effective treatments for myopic macular\ndegeneration are lacking, and this condition is the leading cause of irreversible blindness in working-age\npopulations, particularly in urbanized regions11.”\nIntro para 2 and 3 line 57-82\nGood set up of rationale of what at first look seems an unusual approach.\nResponse: We sincerely thank the reviewer for recognizing the novelty and rationale of our approach.\nMethods line 110 - how was this exclusion done?\nResponse: Thank you for your valuable comment. Individuals meeting any of the listed criteria were\nidentified through a review of their medical records and laboratory test results. Those who met one or more\n15\nexclusion criteria were removed from the analysis prior to model development to minimize potential\nconfounding effects.\nline 114 what was the age/sex difference between uncomplicated and complicated high myopia. Important\nin terms of strong age dependence on the complications of high myopia.\nResponse: Thank you for your comment. We analysed age and sex differences between the\nuncomplicated and complicated high myopia (HM) groups across all cohorts:\n Discovery cohort: The uncomplicated HM group was significantly younger than the complicated\nHM group (52.72 ± 15.90 vs. 58.63 ± 12.67 years, P < 0.001), with no significant difference in the\nsex distribution (P = 0.882).\n In validation cohort 1, the mean age of patients in the HM group was significantly lower than that\nin the non-HM group (50.64 ± 11.39 years vs. 59.52 ± 12.20 years, p < 0.0001), and the sex\ndistribution was similar (P = 0.982).\n In validation cohort 2, the uncomplicated HM group was significantly younger than the complicated\nHM group (44.81 ± 17.66 vs. 56.94 ± 14.10 years, P < 0.001), with no significant difference in the\nsex distribution (P = 0.063).\nOverall, age was strongly associated with high myopia-related complications, whereas the sex distribution\ndid not differ significantly between the uncomplicated and complicated HM groups. These results have\nbeen added to the Results section and Tables S4–S6 of the revised manuscript.\nLine 160 was the 61 chosen for availability or was any biological plausibility or prior research?\nResponse: Thank you for your valuable comment. As you noted, the selection of the 61 parameters was\nbased on two main considerations: availability and biological plausibility supported by prior research.\nFirst, regarding availability, all 61 parameters are routinely obtained in standard clinical practice, which\nsubstantially enhances the feasibility and potential clinical utility of our model.\nSecond, with respect to biological plausibility, emerging evidence supports the underexplored potential of\n16\nroutine laboratory tests in ocular risk assessments. Recent studies have demonstrated that routinely\navailable blood test data can be effective at predicting high myopia-related complications, particularly\nretinal detachment [1,2]. Blood markers of inflammation [3], glucose levels [4], and lipid levels [5] have\nbeen linked to an increased risk of ocular diseases, including high myopia and retinal detachment. In\naddition, several haematological, biochemical, and coagulation parameters have been reported to be\nassociated with the onset or severity of other eye diseases, such as glaucoma [6–8], cataracts [9–11], and\nage-related macular degeneration [12,13].\nTherefore, we systematically incorporated blood cell analysis parameters, biochemical analysis\nparameters, and coagulation analysis parameters into our model, yielding a comprehensive set of 61\nroutinely available laboratory parameters.\nWe have incorporated this information into the Methods section of the revised manuscript.\nReferences:\n[1] Li, S. et al. Developing and validating a clinlabomics-based machine-learning model for early detection\nof retinal detachment in patients with high myopia. J Transl Med 22, 405 (2024).\n[2] Li, S. et al. Development and validation of a routine blood parameters-based model for screening the\noccurrence of retinal detachment in high myopia in the context of PPPM. EPMA J 14, 219–233 (2023).\n[3] Long Q, Ye J, Li Y, Wang S, Jiang Y. C-reactive protein and complement components in patients with\npathological myopia. Optom Vis Sci. 2013;90(5):501–6.\n[4] Arndt C, Hubault B, Hayate F, et al. Increased intravitreal glucose in rhegmatogenous retinal\ndetachment. Eye (Lond). 2023;37(4):638–43.\n[5] Luo S, Xu H, Yang L, et al. Quantitative proteomics analysis of human vitreous in rhegmatogenous\nretinal detachment associated with choroidal detachment by data-independent acquisition mass\nspectrometry. Mol Cell Biochem. 2022;477(6):1849–63.\n[6] Li, S., Qiu, Y., Yu, J. et al. Association of systemic inflammation indices with visual field loss progression\nin patients with primary angle-closure glaucoma: potential biomarkers for 3P medical approaches. EPMA\nJournal 12, 659–675 (2021).\n[7] Li S, Gao Y, Shao M, et al. Association between coagulation function and patients with primary angle\nclosure glaucoma: a 5-year retrospective case–control study. BMJ Open 2017;7:e016719.\n[8] Shao M, Li Y, Teng J, Li S and Cao W (2021) Association Between Serum Lipid Levels and Patients\nWith Primary Angle-Closure Glaucoma in China: A Cross Sectional, Case–Control Study. Front.\nMed. 8:618970.\n[9] Li S, Li D, Zhang Y, et al. Association between serum lipids concentration and patients with age-related\ncataract in China: a cross-sectional, case–control study. BMJ Open 2018;8:e021496.\n[10] Mingxi Shao, Danhui Li, Jisen Teng, Yudong Zhang, Shengjie Li, Wenjun Cao; Association Between\nSerum Complement C3 Levels and Age-Related Cataract. Invest. Ophthalmol. Vis. Sci. 2017;58(11):4934-\n4939.\n[11] Li B, Hou X, Ning B, Li X, Zhang M, et al. (2024) Predictive role of the peripheral blood inflammation\nindices neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and systemic\nimmunoinflammatory index (SII) for age-related cataract risk. PLOS ONE 19(11): e0313503.\n[12] Pinna, A., Porcu, T., D’Amico-Ricci, G., Dore, S., Boscia, F., Paliogiannis, P., … Zinellu, A. (2018).\nComplete Blood Cell Count–Derived Inflammation Biomarkers in Men with Age-Related Macular\nDegeneration. Ocular Immunology and Inflammation, 27(6), 932–936.\n[13] Wang Y, Wang M, Zhang X, Zhang Q, Nie J, Zhang M, Liu X, Ma L. The Association between the\nLipids Levels in Blood and Risk of Age-Related Macular Degeneration. Nutrients. 2016; 8(10):663.\nLine 162 “Laboratory tests were conducted at the time of disease onset,” clarify, surely some follow up\nmeasurements\nResponse: Thank you for your comment, and we apologize for the confusion. At presentation,\nophthalmologists evaluated whether patients had any prior diagnosis or treatment for high myopia-related\ncomplications. Patients with a previous diagnosis or treatment history were excluded to ensure that the\nlaboratory measurements reflected the onset of newly identified disease. Venous blood samples were\ncollected from patients newly diagnosed with high myopia or high myopia-related complications at\npresentation. Laboratory tests obtained during stable or follow-up periods were not included in this analysis.\nWe have clarified this point in the revised manuscript.\nLine 168-187 a table would be easier to read I would think, keeping the separation in blood/bio/coagulation\ngroups\nResponse: Thank you for your valuable comment. We agree with your suggestion that a tabular format\nimproves clarity. In the revised manuscript, we have reorganized the 61 variables into a table (New Table\nS1), which are grouped into blood cell analyses (24 variables), biochemical analyses (31 variables), and\ncoagulation analyses (6 variables).\nBlood cell analyses (24 variables)\nNeutrophil Platelet\nNeutrophil count Red blood cell Thrombocytocrit\npercentage Platelet count (PLT) distribution width\n(NE) count (RBC) (PCT)\n(NE%) (PDW)\nEosinophil\nHaemoglobin Eosinophil count Basophil count Basophil percentage Mean platelet\npercentage\n(HB) (EOS) (BASO) (BASO%) volume (MPV)\n(EOS%)\nLymphocyte\nLymphocyte Packed cell Monocyte count Monocyte Platelet large cell\npercentage\ncount (LYM) volume (PCV) (M) percentage (M%) ratio (PLCR)\n(LYM%)\nRed cell Mean corpuscular\nRed cell Mean corpuscular\nWhite blood cell distribution Mean corpuscular haemoglobin\ndistribution width– haemoglobin\ncount (WBC) width–SD volume (MCV) concentration\nCV (RDW-CV) (MCH)\n(RDW-SD) (MCHC)\nBiochemical analyses (31 variables)\nAlbumin-to-\nTotal protein Total bile acid Total bilirubin Total cholesterol\nAlbumin (ALB) globulin ratio\n(TP) (TBA) (TBIL) (TC)\n(A/G)\nLactate\nLipoprotein B Lipoprotein A\nGlucose (GLU) dehydrogenase Globulin (GLB) Prealbumin (PA)\n(LPB) (LPA)\n(LDH)\nBlood urea Direct bilirubin\nUric acid (UA) Sodium (Na) Chloride (Cl) Phosphorus (P)\nnitrogen (BUN) (DBIL)\nAlkaline Aspartate Alanine\nCreatine kinase Creatinine\nphosphatase Potassium (K) aminotransferase aminotransferase\n(CK) (CREA)\n(ALP) (AST) (ALT)\nGamma-\nHigh-density\nglutamyl Carbon dioxide Low-density\nlipoprotein Triglycerides (TG) Calcium (Ca)\ntransferase (CO₂) lipoprotein (LDL)\n(HDL)\n(GGT)\nComplement\nC1q (C1q)\nCoagulation analyses (6 variables)\nActivated partial\nProthrombin Thrombin time Prothrombin\nFibrinogen (FIB) thromboplastin D-dimer (DD)\ntime (PT) (TT) percentage (PT%)\ntime (APTT)\n188 how many variables and which ones dropped. I’d recommend saying from an initial 61 blood\nbiomarkers, X (list these) were dropped and table X lists the variables used in the model. Otherwise\n19\nreaders won’t know what input actually are and 168-187 is misleading.\nResponse: Thank you for your valuable comment. In the discovery cohort, all 61 variables from routine\nclinical blood tests were initially used to train the screening models across 10 machine learning algorithms.\nA complete list of the 61 variables is provided in Table S1 for clarity.\nFeature reduction was subsequently performed using data from the discovery cohort to enhance clinical\napplicability and optimize model performance. As a result, nine contributing features—EOS, PCT, BASO%,\nM, UA, LPB, BASO, EOS%, and TG levels—were retained in the final model. These nine features are\nhighlighted in both the Methods and Results sections to emphasize their roles in the model and are detailed\nin Table S3.\nTable S3. Variables used in the final model\nEosinophil count Basophil percentage\nThrombocytocrit (PCT) Monocyte count (M) Uric acid (UA)\n(EOS) (BASO%)\nEosinophil percentage\nLipoprotein B (LPB) Basophil count (BASO) Triglycerides (TG)\n(EOS%)\n212 vs 188, is it 61 or 61 minus the variables with > 20% missing? needs clarity and consistency here.\nResponse: Thank you for your comment, and we apologize for the confusion. The issue of variables\nwith >20% missing values was considered at the individual level rather than excluding variables.\nSpecifically, for each participant, if more than 20% of the 61 variables were missing (i.e., 13 variables),\nthe\nparticipant was excluded from the study. Conversely, if a participant had ≤20% missing values among the\n61 variables (i.e., 12 variables), they were retained, and any missing values for the remaining variables\nwere imputed using the medians. This clarification has been incorporated into the Methods section.\n217-221 Is there a link for this? I see it is listed at line 342-345 https://high-myopia.streamlit.app/\nResponse: Thank you for your comment. The web application link (https://high-myopia.streamlit.app/) has\nnow been added to this section for clarity.\n226 “non-normally”\nin the revised manuscript.\nResponse: Thank you for your valuable comment. Notably, the statistical superiority of the RF and\nXGBoost models was confirmed using the DeLong test. The manuscript has been revised to state the\nfollowing: “Additionally, the AUPRC values of the RF (0.8917 [95% CI = 0.8786–0.9108]) and XGBoost\n(0.8788 [95% CI = 0.8514–0.8906]) models (Figure S2) were significantly greater (P < 0.05) than those of\nthe other models (Figure S2) according to the DeLong test, confirming the superior performance of the\nmodels developed based on these two algorithms.”\nResponse: Thank you for your comment. The nine features used in the smaller models were EOS, PCT,\nBASO%, M, UA, LPB, BASO, EOS%, and TG levels (detailed in Table S3). These features have now been\nadded to the sentence to clarify the model inputs.\nResponse: Thank you for your comment. The nine features used in the models were EOS, PCT, BASO%,\nM, UA, LPB, BASO, EOS%, and TG levels. These parameters have now been added to the sentence in\nline 271 to clarify the model inputs.\nThe web application described in line 345 was developed after internal and external validation to provide\nan accessible interface for the finalized model. Therefore, it was not included in the section beginning on\nline 271.\n366 for interpretation of clinical utility in community validation also need sensitivity to assess risk of false\nnegatives with NPV\nResponse: We thank the reviewer for this valuable comment and fully agree with your concern. We\nacknowledge this limitation: only participants who screened positive underwent confirmatory ophthalmic\nexaminations, whereas model-negative individuals were not systematically assessed. The screening of\napproximately 300,000 model-negative participants would have required resources and capacity beyond\nwhat was available to the research team.\nWe have clarified this limitation in the revised Discussion section, which reads as follows:\n“The cross-sectional design of the community-based cohort precludes the assessment of temporal or\ncausal relationships between routine blood test markers and high myopia-related complications.\nFurthermore, the fact that only participants who screened positive underwent confirmatory ophthalmic\nexaminations, while model-negative individuals were not systematically assessed, introduces a potential\nverification bias that may lead to an overestimation of the sensitivity, specificity, and predictive values. In\nparticular, the lack of systematic evaluation among model-negative participants prevents an accurate\nestimation of the false negative and negative predictive values, which is essential for assessing the clinical\nutility of the model.”\n409 - 415 with the PPV ranging from 0.65 to 0.795, with the blood models, there are still going to be a lot\nof false positives and worth discussing merits role of blood as pre-screening tool\nDiscussion needs inclusions limitations, generalizability across difference regions eg asia vs west and role\nwithin a clinical or community work flow see comment above re 409.\nResponse: We thank the reviewer for this valuable comment and fully agree with this concern. We\nacknowledge that the blood-based models developed in this study generated a considerable number of\nfalse positives. We have addressed this issue by revising the Discussion to emphasize that the model\nshould primarily serve as a prescreening tool. By flagging potentially high-risk individuals at the community\nor primary-care level, the model can facilitate timely referral for confirmatory ophthalmic imaging and\nspecialist evaluation, thereby reducing missed cases while promoting a more efficient allocation of\nhealthcare resources, especially in settings where advanced imaging is not widely accessible. In addition,\nwe have expanded the Discussion to consider the generalizability of our findings across different\npopulations (e.g., Asian versus Western cohorts) and to clarify the potential role of the model within both\n22\nclinical and community workflows, such as integration into health checkup centres and primary care\nsystems.\nWe have clarified this information in the revised Discussion section, which reads as follows:\n“Our blood-based screening framework offers notable advantages over AI-driven fundus imaging for\ndetecting high myopia-related complications, particularly in resource-limited settings. Although deep\nlearning models based on retinal photographs have achieved strong diagnostic performance (AUCs of\n0.89–0.95 for myopic maculopathy), their widespread implementation is constrained by the need for high-\nresolution imaging equipment, which is unavailable in more than 90% of rural clinics in low-income\ncountries, and the requirement for trained personnel. These challenges are further compounded by the\nfrequent need for pharmacologic pupil dilation in highly myopic eyes with peripheral retinal pathology. In\ncontrast, routine blood testing is almost universally accessible, with 98% of primary care facilities\nworldwide equipped to perform basic haematological assessments. Moreover, blood collection requires no\nophthalmic expertise and remains feasible in conditions that compromise fundus imaging, such as media\nopacities (e.g., cataracts) or severe fundus distortions associated with high myopia.\nNevertheless, our model had a nonnegligible false-positive rate, suggesting that its optimal role is as a\nprescreening tool in community clinics, primary care facilities, or health examination centres to identify\nindividuals at high risk who should undergo a confirmatory ophthalmic evaluation. In particular, within\nhealth examination centres, individuals with high myopia could be screened at no additional cost or\nresource burden, since the required haematological parameters are already included in standard blood\ntest panels. By leveraging routinely collected data, our model enables efficient risk stratification for multiple\nhigh myopia-related complications, thereby reducing missed diagnoses and supporting the more rational\nallocation of limited ophthalmic resources.”\nWe have now clarified this information in the revised Discussion-Limitations section, which reads as follows:\n“The hospital-based prospective cohort and community-based cross-sectional studies were conducted\nexclusively in Asian populations, which may limit the generalizability of our findings to other ethnic groups\nor geographic regions. Therefore, the performance of our model requires external validation in more\ndiverse populations.”\nThis study developed a machine learning framework using routine blood tests to screen for high myopia-\nrelated complications. The research enrolled 10,661 patients and utilized 61 hematological parameters to\ntrain 10 machine learning algorithms. The random forest model based on 9 key features (eosinophil count,\nthrombocytocrit, basophil percentage, etc.) demonstrated optimal performance with a macro-average AUC\nof 0.9649 in the discovery cohort. The model achieved 70.0% sensitivity and 94.0% specificity in hospital-\nbased prospective validation and 74% positive predictive value in community-based cross-sectional study.\nNotably, the study identified associations between inflammatory markers (particularly eosinophils and\nbasophils) and retinal detachment and choroidal neovascularization. While the technical execution is\nsound, the fundamental clinical utility question needs to be addressed. The authors should consider\nredirecting their findings toward prognostic applications rather than screening applications, which would\nsignificantly enhance the clinical relevance and impact of this work.\nResponse: We sincerely thank the reviewer for recognizing the sound technical execution of our study\nand its potential clinical applicability, particularly in resource-limited settings. We greatly appreciate your\nconstructive feedback and insightful suggestions, which have been invaluable in improving the quality and\nclarity of the manuscript. In response, we have carefully revised the manuscript in accordance with your\ncomments and provided detailed, point-by-point responses to each concern below.\n1.Major Concerns: Retinal detachment and choroidal neovascularization can currently be rapidly\ndiagnosed through fundus photography at lower cost and higher accuracy. Blood-based screening is prone\nto false positives, potentially increasing unnecessary ophthalmologic examination costs rather than\nreducing them. The clinical decision pathway lacks clear justification for why blood testing should precede\ndirect ophthalmologic examination.\nResponse: We thank the reviewer for this valuable comment. The primary aim of our study was to provide\na convenient screening approach for identifying individuals with high myopia who are at risk of multiple\ncomplications. We fully agree that ophthalmic imaging modalities, such as fundus photography and OCT,\nare the gold standards for a definitive diagnosis. However, several barriers limit their use as first-line tools\nin population-level screening:\n24\n1. Limited accessibility: Many patients with high myopia do not undergo regular ophthalmic\nexaminations in the absence of obvious symptoms. Unfortunately, by the time they present to\nhospitals with visual complaints, some patients may already experience sustained irreversible\nvision loss. Furthermore, ophthalmic imaging, particularly OCT, is often unavailable in primary care\nor community health centres. Current data indicate that nearly 78% of low- and middle-income\ncountries do not have basic OCT infrastructure.\n2. Diagnostic variability: Even when imaging is available, considerable interclinician variability\nexists in interpreting the results. For example, studies have shown up to 22% variability in\nglaucoma detection using OCT parameters.\n3. Resource intensity: Fundus imaging and OCT are resource- and cost-intensive procedures,\nwhich hinders their use for large-scale screening. This finding is particularly concerning because\nnearly 40% of myopic macular lesions are asymptomatic in their early stages, underscoring the\nimportance of earlier and more accessible risk stratification.\nIn contrast, the haematological parameters incorporated into our model are derived from routine blood\ntests that are universally available in primary care and health examination centres, including in resource-\nlimited settings. Although our model has a nonnegligible false-positive rate, we envision its role primarily\nas a prescreening tool to identify high-risk individuals who should be referred for a confirmatory ophthalmic\nevaluation. Importantly, in health examination centres, no additional cost or resource burden is incurred\nbecause the required blood parameters are already collected as part of routine testing. By leveraging these\nroutinely available data, our model can improve early risk stratification, reduce missed diagnoses, and\nsupport the more rational allocation of limited ophthalmic resources.\n2.Methodological Limitations: First, the retrospective case-control design introduces inherent selection\nbias. And missing discussion of how acute systemic conditions might confound inflammatory markers. In\naddition, the study is with limited validation across different ethnic populations and healthcare systems.\nResponse: We thank the reviewer for this valuable comment and fully agree with your concerns.\n25\n(1) Study design: We acknowledge that the retrospective case–control design may introduce inherent\nselection bias. We have addressed this issue by validating the performance of our screening model in two\nindependent large-sample cohorts: a hospital-based prospective cohort study and a community-based\ncross-sectional study.\n(2) Systemic confounding factors: As suggested, we performed a subgroup analysis by excluding patients\nwith diabetes, hypertension, or other systemic conditions that could affect the included haematological\nbiomarkers. The results (detailed in Tables S10–S12; Figure S5) remained consistent, supporting the\nrobustness of our model.\n(3) Generalizability: We also acknowledge the limitations of limited validation across different ethnic\npopulations and healthcare systems. This point has now been explicitly added to the Discussion section\nof the revised manuscript.\n3.Limited Clinical Significance: While the technical achievement is noteworthy, the clinical significance is\nquestionable. The proposed screening approach may not offer advantages over existing diagnostic\nmethods (fundus image) for the studied complications. The identification of inflammatory pathways is\ninteresting but not sufficiently novel given existing literature on inflammation and retinal diseases.\nResponse: We thank the reviewer for this valuable comment. The primary aim of our study was not to\nreplace ophthalmic imaging but to provide a convenient prescreening approach for identifying individuals\nwith high myopia who are at risk of multiple complications. We fully acknowledge that fundus photography\nand OCT remain the gold standards for a definitive diagnosis. However, several practical barriers limit their\nuse as first-line tools in population-level screening:\nLimited accessibility: Many patients with high myopia do not undergo regular ophthalmic examinations\nwithout obvious symptoms, and irreversible vision loss may already be present at the time of the hospital\nvisit. Moreover, OCT is largely unavailable in primary care or community health settings; notably,\napproximately 78% of low- and middle-income countries lack OCT infrastructure.\nDiagnostic variability: Even when imaging is available, substantial interclinician variability exists in\ninterpretation (e.g., up to 22% variability in glaucoma detection using OCT parameters), which may affect\n26\nearly risk identification.\nResource intensity: Ophthalmic imaging is costly and resource-demanding, limiting the feasibility of large-\nscale screening. This finding is concerning since nearly 40% of myopic macular lesions are asymptomatic\nin early stages.\nIn contrast, the haematological parameters used in our model are derived from routine blood tests, which\nare universally available in primary care and health examination centres—even in resource-limited settings.\nImportantly, no additional costs are incurred in these settings because the tests are already performed for\nother health assessments. While our model may yield some false positives, we envision it as a\nprescreening tool to stratify risk and guide referrals for confirmatory ophthalmic imaging. By leveraging\nexisting data, our approach may improve early risk identification, reduce missed diagnoses, and enable\nthe more rational allocation of limited ophthalmic resources.\nFinally, regarding novelty, we agree that inflammation is an established factor in retinal diseases. However,\nour contribution lies in integrating routine inflammatory markers into a machine learning model specifically\ndesigned for high myopia-related complications, thereby extending their application from a mechanistic\nunderstanding to a scalable screening strategy.\n4.Data and Methods The study exhibits notable methodological strengths, including a large multi-center\ncohort of 10,661 patients, comprehensive analysis of 61 blood parameters, and robust statistical\napproaches with appropriate external validation and feature selection using SHAP methodology.\nHowever, critical limitations significantly compromise clinical applicability. The retrospective design lacks\nlongitudinal follow-up to establish causality between biomarkers and disease onset. Crucially missing are\ndetails on blood sampling timing relative to diagnosis and cost-effectiveness analysis comparing blood-\nbased screening to existing imaging methods. The study inadequately addresses potential confounding\nfrom medications, comorbidities, and systemic inflammatory conditions that could influence the identified\nblood parameters.\nResponse: We thank the reviewer for this valuable comment and fully acknowledge the concerns raised.\nOur point-by-point responses are as follows:\n27\n(1) Retrospective design and causality\nWe agree that the retrospective design limits our ability to establish causal relationships between the\nbiomarkers and disease onset. The main objective of our study was to develop a machine learning-based\nscreening model using routine blood test data to identify individuals at risk of high myopia-related\ncomplications (cataracts, macular degeneration, choroidal neovascularization, glaucoma, and retinal\ndetachment). Although causality could not be addressed, the robustness of our model was supported by\na large sample size, external validation across multiple cohorts, and feature selection using the SHAP\nmethodology.\n(2) Timing of blood sampling\nAt presentation, ophthalmologists first evaluated whether patients had any prior diagnosis or treatment for\nhigh myopia-related complications. Patients with previously diagnosed complications or a treatment history\nwere excluded to ensure that blood measurements reflected newly identified disease. Venous blood\nsamples were collected from patients newly diagnosed with high myopia or its complications at\npresentation. Laboratory tests performed during stable or follow-up periods were not included, thereby\nensuring temporal alignment between disease onset and biomarker assessments.\n(3) Cost-effectiveness considerations\nWe acknowledge the importance of a cost-effectiveness analysis. While a formal economic evaluation was\nbeyond the scope of this study, importantly, the haematological parameters used in our model are derived\nfrom routine blood tests, which are universally available in primary care and health examination centres—\neven in resource-limited settings. Importantly, no additional costs are incurred in these contexts, since the\ntests are already conducted for general health assessments.\n(4) Potential confounding factors\nAs suggested, we conducted a subgroup analysis excluding patients with systemic conditions, such as\nactive systemic inflammatory conditions, recent infections, immunosuppressive medication use, diabetes,\nor hypertension, which could influence haematological biomarkers. The results remained consistent,\nsupporting the robustness and generalizability of our model despite potential systemic confounding.\n28\n5.Analytical Methods：The study demonstrates strong statistical rigor with appropriate use of multiple\nmachine learning algorithms, cross-validation, proper SHAP implementation for interpretability, and\ncomprehensive performance metrics including calibration curves and decision curve analysis. However,\ncritical analytical gaps limit clinical applicability. The study lacks sensitivity analyses excluding patients with\nsystemic inflammatory conditions, missing analysis of optimal screening intervals, and most importantly, absent\ncost-benefit modeling to justify clinical implementation over existing imaging-based diagnostic approaches.\nResponse: We thank the reviewer for recognizing the statistical rigor of our analysis and for highlighting\ncritical aspects that influence clinical applicability. In response to the concerns raised, we have made the\nfollowing revisions:\n(1) Subgroup analyses of systemic inflammatory confounding\nWe performed additional subgroup analyses by excluding patients with active systemic inflammatory\nconditions, recent infections, use of immunosuppressive medications, diabetes, or hypertension to mitigate\npotential bias from systemic inflammatory conditions. The results were consistent with the primary findings,\nsupporting the robustness and generalizability of our model. These analyses have been added to the\nResults section (Tables S10–S12, Figure S5) and referenced in the Results section.\n(2) Optimal screening interval\nWe agree with the reviewer that the retrospective, baseline-only design precludes a direct assessment of\noptimal screening intervals. We now explicitly acknowledge this limitation in the Discussion section. We\nsuggest aligning screening with routine health examinations (e.g., annually) to provide a pragmatic\nframework for clinical application, which would maximize feasibility and compliance in real-world settings.\n(3) Cost–benefit modelling\nWe acknowledge that a formal economic evaluation was beyond the scope of the present study. This\nlimitation has been added to the Discussion section. Importantly, we emphasize that the haematological\nparameters employed by our model are derived from routine blood tests already performed in primary care\n29\nand health examination centres, thereby incurring minimal incremental cost.\nCollectively, these findings clarify the intended role of our model as a cost-efficient prescreening tool,\ndemonstrate its robustness against systemic inflammatory confounding, and transparently acknowledge\nthe current limitations regarding screening intervals and economic evaluation.\n6.References：The manuscript adequately cites relevant literature on machine learning applications in\nophthalmology and inflammatory markers in eye disease. However, it lacks sufficient discussion of recent\nadvances in point-of-care ophthalmologic imaging and cost-effectiveness studies of screening programs.\nResponse: We thank the reviewer for this important comment. In accordance with your suggestion, we\nhave added a discussion of recent advances in point-of-care ophthalmologic imaging as well as cost-\neffectiveness studies of screening programs.\nCost analyses also support the utility of our model. In health examination centres, the required\nhaematological parameters are already included in standard blood test panels, enabling our model to\nscreen for multiple high myopia-related complications without generating any additional expense. Similarly,\nin community or primary care settings, patients frequently undergo routine blood testing for nonophthalmic\nconditions; integrating these results into our model allows opportunistic screening at no extra cost.\nMoreover, when equipment amortization and personnel expenses are accounted for, the median cost per\nblood-based screening is approximately 300 RMB. An additional advantage of blood testing is its ability to\nsimultaneously provide systemic health information (e.g., uric acid levels for gout and triglyceride levels\nfor dyslipidaemia), thereby offering dual-purpose clinical utility that fundus imaging alone cannot achieve.\nWe also acknowledge recent advances in point-of-care ophthalmic imaging. Smartphone-based fundus\nphotography has emerged as a cost-effective, portable, and accessible method for the early detection of\nretinal diseases such as diabetic retinopathy, age-related macular degeneration, and retinal vascular\ndisorders, thus expanding access to essential diagnostic services [1,2]. Our model complements these\nimaging-based approaches by leveraging routinely collected blood test data, enabling opportunistic risk\nstratification for high myopia-related complications at no additional cost.\nWe have incorporated a discussion of recent advances in point-of-care ophthalmic imaging as well as cost-\n30\neffectiveness analyses of screening programs.\nReferences:\n[1] Ahn, S. J. & Kim, Y. H. Clinical Applications and Future Directions of Smartphone Fundus Imaging.\nDiagnostics (Basel) 14, 1395 (2024).\n[2] Rajalakshmi, R., Prathiba, V., Arulmalar, S. & Usha, M. Review of retinal cameras for global coverage\nof diabetic retinopathy screening. Eye (Lond) 35, 162–172 (2021).\n7. Suggested Improvements\nLine 32-33: Replace \"effective strategies for identifying these complications within high myopia populations\nremain largely unavailable\" with more nuanced statement acknowledging existing imaging-based methods\nResponse: We thank the reviewer for this valuable comment. The sentence has been revised to “Imaging\nmodalities can detect complications associated with high myopia; however, their utility as population-level\nscreening strategies is limited by substantial resource requirements and restricted accessibility.”\nLine 46: Qualify the conclusion by specifying target populations where blood-based screening would be\nmost appropriate\nResponse: We thank the reviewer for this valuable comment. The sentence has been revised to “This\nscalable and easy-to-implement screening model based on routine blood tests enables the early detection\nof complications in high myopia populations, particularly in health examination centres and community\nprimary care settings.”\nLines 57-65: Expand discussion of current imaging limitations to include recent advances in portable\nfundus cameras and smartphone-based imaging\nResponse: We thank the reviewer for this valuable comment and fully agree. In response, we have\nexpanded the discussion of current imaging limitations to incorporate recent advances in portable fundus\ncameras and smartphone-based imaging. These technologies offer cost-effective, accessible, and point-\nof-care alternatives for retinal assessments [1,2], facilitating earlier detection and monitoring of retinal\n31\npathologies such as diabetic retinopathy, age-related macular degeneration, and retinal vascular disorders.\nDespite these advantages, important challenges remain regarding image quality, standardization,\nregulatory approval, and medicolegal considerations, highlighting the continued need for complementary\nscreening strategies, including blood-based risk stratification models.\nReferences:\n[1] Chen, Y. et al. Acceptability, applicability, and cost-utility of artificial-intelligence-powered low-cost\nportable fundus camera for diabetic retinopathy screening in primary health care settings. Diabetes Res\nClin Pract 223, 112161 (2025).\n[2] Ahn, S. J. & Kim, Y. H. Clinical Applications and Future Directions of Smartphone Fundus Imaging.\nDiagnostics (Basel) 14, 1395 (2024).\nLines 66-72: Provide more detailed cost analysis comparing blood tests (~300 RMB) with basic fundus\nphotography costs\nResponse: We thank the reviewer for this valuable comment. A detailed cost analysis comparing blood-\nbased screening with basic fundus photography has been added as follows:\n“From a cost perspective, blood-based screening is also highly efficient. In health examination centres and\nprimary care settings, the required haematological parameters are already determined through routine\nblood tests, enabling opportunistic screening for high myopia-related complications at no additional\nexpense. Even if blood tests were performed specifically for ocular screening, the median cost is\napproximately 300 RMB per person, which is lower than that of fundus-based imaging (400–600 RMB).\nAdditionally, blood tests provide concurrent systemic health information, offering dual-purpose clinical utility.\nThese features underscore blood-based models as a complementary, scalable, and low-cost strategy for\nidentifying high myopia-related complications.”\nLines 83-91: Add specific research hypothesis about when blood testing would be clinically superior to\ndirect examination\nResponse: We thank the reviewer for this valuable comment. Specific research hypotheses regarding\nscenarios in which blood-based screening may be clinically superior to direct ophthalmic examination have\n32\nbeen added as follows:\n“We hypothesize that blood-based screening will be clinically advantageous over a direct ophthalmic\nexamination in settings where specialized imaging is unavailable or impractical, particularly in health\nexamination centres and community primary care facilities. This approach prioritizes scalability and\ngeneralizability, aligns with the principles of precision medicine, and holds promise for reducing the global\nburden of preventable blindness associated with high myopia.”\nLines 110-113: Expand exclusion criteria to include patients with active systemic inflammatory conditions,\nrecent infections, or immunosuppressive medications\nResponse: We thank the reviewer for this valuable comment. In accordance with your previous and\nsubsequent comments, we performed additional subgroup analyses excluding patients with active\nsystemic inflammatory conditions, recent infections, or use of immunosuppressive medications, as well as\nthose with diabetes or hypertension, to further confirm the robustness of our model. The exclusion criteria\napplied in these additional subgroup analyses have been incorporated into the revised manuscript.\nLines 114-123: Report the time interval between blood sampling and ophthalmologic diagnosis for each\npatient group\nResponse: Venous blood samples were obtained from patients who were newly diagnosed with high\nmyopia or high myopia-related complications within 24 hours of their initial ophthalmologic evaluation. This\ninformation has been added to the revised manuscript.\nLines 162-167: Specify exact timing of blood collection relative to symptom onset and clinical diagnosis\nResponse: Venous blood collection from patients newly diagnosed with high myopia or high myopia-\nrelated complications was performed within 24 hours of symptom onset and clinical presentation. This\nclarification has been incorporated into the revised manuscript.\nstrengthen mechanistic understanding\nResponse: We thank the reviewer for this valuable comment and agree that incorporating inflammatory\nmarkers beyond a routine CBC (e.g., levels of CRP, ESR, and specific cytokines) could improve our\nunderstanding of the underlying mechanism. However, as this study has a retrospective case–control\ndesign, these markers were not collected. This limitation has been explicitly acknowledged in the\nDiscussion section as follows:\n“Finally, although we incorporated multiple haematological, metabolic, and inflammatory markers, other\npotentially relevant biomarkers were not included, and future studies integrating multiomics data may\nfurther improve the predictive accuracy.”\nAdd new paragraph: Describe quality control measures for blood sample collection and processing across\nmultiple centers\nResponse: We thank the reviewer for this valuable comment. Quality control for blood sample collection\nand processing was ensured at both the interlaboratory and intralaboratory levels.\nInterlaboratory quality control: All the participating centres participated in external proficiency testing\norganized by the Clinical Laboratory Center. The centre distributed standardized survey samples to each\nsite, which were analysed locally. The results were evaluated centrally, and all the centres successfully\npassed the external quality assessments.\nIntralaboratory quality control: Each centre used standardized internal quality control (IQC) materials\nprovided by the Clinical Laboratory Center. Before processing patient samples each day, the centres\nmeasured these IQC samples to ensure assay precision, maintaining a coefficient of variation (CV) within\n5%.\nThese measures ensured consistent and reliable laboratory results across all the centres.\nLines 268-279: Provide biological rationale for why these specific 9 features were mechanistically relevant\n34\nto each complication type\nResponse: We thank the reviewer for this valuable comment. We agree that providing the biological\nrationale for the selection of these nine features is important for understanding their mechanistic relevance\nto each type of complication. As our study was designed as a data-driven machine learning analysis based\non routine clinical laboratory parameters, we did not directly investigate the underlying mechanisms. Thus,\nthe biological rationale is largely based on evidence from prior studies, which are summarized as follows:\n\"Routine blood indices, biochemical indices, and coagulation indices can reflect a wide range of\nphysiological and pathological states in the body[1,2], providing information on aspects such as\ninflammation, blood circulation, metabolic status, and tissue injury. In this study, the selected biomarkers\nwere associated with known mechanisms of myopic complications. The inclusion of eosinophil and\nbasophil counts and percentages aligns with recent evidence showing that localized inflammation drives\nretinal detachment and choroidal neovascularization through IL-4/IL-13-mediated vascular leakage[3-5]. IL-\n4 and IL-13, key Th2-type cytokines, play critical roles in mediating localized inflammation in the eye. They\npromote the recruitment and activation of eosinophils and basophils, increasing vascular permeability,\nwhich may contribute to the development of retinal detachment and choroidal neovascularization. The\nevidence suggests that these cytokines induce the endothelial expression of adhesion molecules,\nfacilitating leukocyte adhesion and migration and thereby amplifying inflammatory responses in the retina\nand choroid. Importantly, targeting IL-4/IL-13 signalling with anti-inflammatory therapies, such as\nmonoclonal antibodies against IL-4 or IL-13, could reduce local inflammation and vascular leakage,\ndecreasing the risk of disease progression[6-7]. While preclinical studies support this approach, the safety\nand efficacy of such interventions in clinical settings remain to be established, and future prospective\nstudies are warranted to evaluate their therapeutic potential.\nUA, a potent antioxidant, paradoxically showed a protective association in our model, although this finding\nis consistent with its dual role in neutralizing oxidative stress in early macular degeneration while\nexacerbating inflammation at higher concentrations[8]. In this study, we also revealed that abnormalities in\ntriglyceride and lipoprotein levels were associated with complications of high myopia. Systemic\ndyslipidaemia–induced blood lipid deposition plays a critical role in the pathogenesis of macular\ndegeneration, where lipid dysregulation promotes lipid accumulation, oxidative stress, and chronic\n35\ninflammation in Bruch’s membrane, ultimately leading to retinal pigment epithelial dysfunction and disease\nprogression[9,10]. The prominence of the thrombocytocrit and the level of lipoprotein B corroborates the\nroles of thrombotic and dysregulated lipid pathways in the progression of myopic glaucoma[11]. This\nbiological agreement between the identified biomarkers and known mechanisms strengthens the clinical\nplausibility of the model, suggesting that systemic shifts in haematometabolic parameters may precede\ndetectable fundus changes by months to years.\"\nCollectively, although our selection of these features was driven by statistics, these biomarkers are\nbiologically plausible in light of prior mechanistic studies, and we have added this discussion to the revised\nmanuscript.\nReferences:\n[1] Foy, B. H. et al. Haematological setpoints are a stable and patient-specific deep phenotype. Nature 637,\n430–438 (2025).\n[2] Bossi, E. et al. Revolutionizing Blood Collection: Innovations, Applications, and the Potential of\nMicrosampling Technologies for Monitoring Metabolites and Lipids. Metabolites 14, 46 (2024).\n[3] Gause, W. C., Wynn, T. A. & Allen, J. E. Type 2 immunity and wound healing: evolutionary refinement\nof adaptive immunity by helminths. Nat Rev Immunol 13, 607–614 (2013).\n[4] Rosenberg, H. F., Dyer, K. D. & Foster, P. S. Eosinophils: changing perspectives in health and disease.\nNat Rev Immunol 13, 9–22 (2013).\n[5] Nishiguchi, K. M. et al. C9-R95X polymorphism in patients with neovascular age-related macular\ndegeneration. Invest Ophthalmol Vis Sci 53, 508–512 (2012).\n[6] Leung, D. Y. M. et al. Dupilumab Inhibits Vascular Leakage of Blood Proteins Into Atopic Dermatitis\nSkin. J Allergy Clin Immunol Pract 11, 1421–1428 (2023).\n[7] Gao, L., Jiang, W., Liu, H., Chen, Z. & Lin, Y. Receptor-selective interleukin-4 mutein attenuates laser-\ninduced choroidal neovascularization through the regulation of macrophage polarization in mice. Exp Ther\nMed 22, 1367 (2021).\n[8] Ames, B. N., Cathcart, R., Schwiers, E. & Hochstein, P. Uric acid provides an antioxidant defense in\nhumans against oxidant- and radical-caused aging and cancer: a hypothesis. Proc Natl Acad Sci U S A 78,\n6858–6862 (1981).\n[9] van Leeuwen, E. M. et al. A new perspective on lipid research in age-related macular degeneration.\nProg Retin Eye Res 67, 56–86 (2018).\n[10] Li, B., Goss, D., Miller, J. W., Lin, J. B. & Vavvas, D. G. Systemic Dyslipidemia in Age-related Macular\nDegeneration: An Updated Systematic Review and Meta-analysis. Ophthalmol Sci 4, 100341 (2024).\n[11] Ma, Y., Li, S., Shao, M., Cao, W. & Sun, X. Platelet Parameters and Their Relationships With the\nThickness of the Retinal Nerve Fiber Layer and Ganglion Cell Complex in Primary Open-Angle Glaucoma.\nFront Neurol 13, 867465 (2022).\nconditions\nResponse: We thank the reviewer for this insightful suggestion. In response, we performed a subgroup\nanalysis excluding patients with active systemic inflammatory conditions, recent infections, use of\nimmunosuppressive medications, diabetes, or hypertension, all of which could influence haematological\nbiomarkers.\nDiscovery cohort: After excluding these subjects, the final RF model achieved a macroaverage AUC of\n0.9560, which was not significantly different from that of the model including all patients (DeLong test, P =\n0.056).\nValidation cohort 1: After excluding these subjects, the final RF model achieved a macroaverage AUC of\n0.9010, which was not significantly different from that of the model including all patients (DeLong test, P =\n0.868).\nValidation cohort 2: After excluding these subjects, the final RF model achieved a macroaverage AUC of\n0.9008, which was not significantly different from that of the model including all patients (DeLong test, P =\n0.976).\nThese results indicate that excluding patients with potential confounding systemic conditions did not\nmaterially affect the performance of the RF model. The subgroup analysis has been added to the Results\nsection of the revised manuscript and is presented in Figure S5 and Tables S10–S12.\nWe sincerely appreciate the reviewer’s suggestion, which has strengthened the rigor of our study.\nAdd new analysis: Compare model performance in early-stage vs. advanced disease to determine optimal\nscreening timing\nResponse: We thank the reviewer for this valuable comment. We agree that comparing model\n37\nperformance in patients with early-stage versus advanced disease would help determine the optimal timing\nof the screen. However, due to the retrospective case–control design of the current study and because\nseveral types of complications could not be reliably stratified by severity, this analysis was not feasible.\nWe plan to address this question in future prospective studies.\nLines 346-357: Include breakdown of false positive rates by complication type and their downstream\nhealthcare costs\nResponse: We thank the reviewer for this valuable comment. We further analysed the false positive rates\nby complication type. The RF model yielded false positive rates of 14.22% for cataracts, 0.23% for macular\ndegeneration (MD), 0.52% for choroidal neovascularization (CN), 0% for glaucoma, and 0.18% for retinal\ndetachment (RD), indicating a low proportion of false alarms. In terms of downstream costs, the higher\nfalse positive rate for cataracts may lead to some additional routine ophthalmologic visits, whereas the\nvery low false positive rates for MD, CN, and RD minimize unnecessary procedures. Overall, the model\nhelps allocate ophthalmic resources efficiently, focusing on confirmatory evaluations of high-risk individuals\nand limiting unnecessary healthcare expenditure.\nLines 358-369: Add analysis of patients who were blood test negative but later developed complications\nduring follow-up period\nResponse: We thank the reviewer for this valuable comment. We agree that analysing patients who were\nnegative based on the blood test but later developed complications during follow-up would be highly\ninformative. However, due to the community-based cross-sectional study design, this analysis was not\nfeasible. We plan to address this question in future prospective studies.\nLines 379-388: Restructure to focus on prognostic value rather than screening utility - analyze whether\nbiomarker levels predict treatment response or disease progression\nResponse: We thank the reviewer for this valuable comment. We agree that analysing whether biomarker\nlevels predict the treatment response or disease progression would be highly informative. However, due\n38\nto the case–control/cross-sectional study design, this analysis was not feasible in the current study. We\nplan to address this question in future prospective studies.\nLines 429-434: Propose specific clinical scenarios where blood testing would be the preferred first-line\napproach\nResponse: We thank the reviewer for this valuable comment. Our model is proposed as a preferred first-\nline approach in health examination centres and community primary care settings. This information has\nbeen incorporated into the revised manuscript.\nLines 379-384: Expand discussion of IL-4/IL-13 pathways to include therapeutic implications - could anti-\ninflammatory treatments modify risk?\nResponse: We thank the reviewer for this valuable comment. We have expanded the discussion of IL-\n4/IL-13 pathways to include their therapeutic implications, specifically addressing whether anti-\ninflammatory treatments could modify the disease risk.\n“IL-4 and IL-13, key Th2-type cytokines, play critical roles in mediating localized inflammation in the eye.\nThey promote the recruitment and activation of eosinophils and basophils, increasing vascular permeability,\nwhich may contribute to the development of retinal detachment and choroidal neovascularization. The\nevidence suggests that these cytokines induce the endothelial expression of adhesion molecules,\nfacilitating leukocyte adhesion and migration and thereby amplifying inflammatory responses in the retina\nand choroid. Importantly, targeting IL-4/IL-13 signalling with anti-inflammatory therapies, such as\nmonoclonal antibodies against IL-4 or IL-13, could reduce local inflammation and vascular leakage,\ndecreasing the risk of disease progression[1,2]. While preclinical studies support this approach, the safety\nand efficacy of such interventions in clinical settings remain to be established, and future prospective\nstudies are warranted to evaluate their therapeutic potential.”\nReferences:\n[1] Leung, D. Y. M. et al. Dupilumab Inhibits Vascular Leakage of Blood Proteins Into Atopic Dermatitis\nSkin. J Allergy Clin Immunol Pract 11, 1421–1428 (2023).\n39\n[2] Gao, L., Jiang, W., Liu, H., Chen, Z. & Lin, Y. Receptor-selective interleukin-4 mutein attenuates laser-\ninduced choroidal neovascularization through the regulation of macrophage polarization in mice. Exp Ther\nMed 22, 1367 (2021).\nLines 385-388: Add analysis of whether biomarker levels correlate with disease severity or treatment\noutcomes\nResponse: We thank the reviewer for this valuable suggestion. We agree that analysing whether\nbiomarker levels correlate with disease severity or treatment outcomes would provide important insights.\nHowever, given the case–control/cross-sectional design of the current study, such an analysis was not\nfeasible. We plan to address this question in future prospective studies.\nAdd sensitivity analysis: Exclude patients with diabetes, hypertension, or other systemic conditions\naffecting the 9 biomarkers\nResponse: We thank the reviewer for this insightful suggestion. In response, we performed a subgroup\nanalysis excluding patients with active systemic inflammatory conditions, recent infections, use of\nimmunosuppressive medications, diabetes, or hypertension, all of which could influence haematological\nbiomarkers.\nDiscovery cohort: After excluding these subjects, the final RF model achieved a macroaverage AUC of\n0.9560, which was not significantly different from that of the model including all patients (DeLong test, P =\n0.056).\nValidation cohort 1: After excluding these subjects, the final RF model achieved a macroaverage AUC of\n0.9010, which was not significantly different from that of the model including all patients (DeLong test, P =\n0.868).\nValidation cohort 2: After excluding these subjects, the final RF model achieved a macroaverage AUC of\n0.9008, which was not significantly different from that of the model including all patients (DeLong test, P =\n0.976).\nThese results indicate that excluding patients with potential confounding systemic conditions did not\nmaterially affect the performance of the RF model. The subgroup analysis has been added to the Results\nsection of the revised manuscript and is presented in Figure S5 and Tables S10–S12.\nWe sincerely appreciate the reviewer’s suggestion, which has strengthened the rigor of our study.\n41"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have addressed all my previous comments satisfactorily, and I do not have any further comments.", "reviewer_2": "The authors are to be commended for a thorough response and they had addressed my queries and to my\nmind, those of the other reviewers very well.", "reviewer_3": "The authors have made substantial improvements. However, several aspects regarding clinical positioning and\npractical implementation require further clarification."}, "author_response": "Response: We sincerely thank the reviewer for their positive assessment and constructive feedback throughout\nthe review process. We are pleased to learn that all previous comments have been satisfactorily addressed.\nWe greatly appreciate the reviewer’s time and effort in evaluating our manuscript.\nResponse: We sincerely thank the reviewer for their thoughtful and encouraging comments. We are grateful\nfor their recognition of our thorough revisions and careful responses to all prior queries. We deeply appreciate\nthe reviewer’s positive evaluation and constructive engagement throughout the review process.\nResponse: We sincerely thank the reviewer for their constructive feedback and for recognizing the substantial\nimprovements made in the revised version. We appreciate the reviewer’s insightful comments regarding the\nclinical positioning and practical implementation of our findings. In response, we have further clarified these\naspects in the revised manuscript."}]

Decision letter