JMIR MEDICAL INFORMATICS

Yamashita et al

Original Paper

Early Diagnosis of Hereditary Angioedema in Japan Based on a
US Medical Dataset: Algorithm Development and Validation
Kouhei Yamashita1, MD, PhD; Yuji Nomoto2, MD; Tomoya Hirose3, MD, PhD; Akira Yutani4, PhD; Akira Okada5,
ME; Nayu Watanabe5, MMG; Ken Suzuki5, ME; Munenori Senzaki5, MS; Tomohiro Kuroda4, PhD
1

Department of Hematology and Oncology, Graduate School of Medicine, Kyoto University, Kyoto, Japan2

Department of Palliative Care Medicine, Niigata City General Hospital, Niigata, Japan3

Department of Traumatology and Acute Critical Medicine, Graduate School of Medicine, Osaka University, Osaka, Japan

4

Division of Medical Information Technology and Administration Planning, Kyoto University Hospital, Kyoto, Japan

5

Healthcare and Life Science, IBM Consulting, IBM Japan, Ltd, Tokyo, Japan

Corresponding Author:
Kouhei Yamashita, MD, PhD
Department of Hematology and Oncology
Graduate School of Medicine
Kyoto University
54 Shogoin-kawahara-cho, Sakyo-ku
Kyoto, 606-8507
Japan
Phone: 81 75 751 4964
Fax: 81 75 751 4963
Email: kouhei@kuhp.kyoto-u.ac.jp

Abstract
Background: Hereditary angioedema (HAE), a rare genetic disease, induces acute attacks of swelling in various regions of the
body. Its prevalence is estimated to be 1 in 50,000 people, with no reported bias among different ethnic groups. However,considering the estimated prevalence, the number of patients in Japan diagnosed with HAE remains approximately 1 in 250,000,
which means that only 20% of potential HAE cases are identified.
Objective: This study aimed to develop an artificial intelligence (AI) model that can detect patients with suspected HAE using
medical history data (medical claims, prescriptions, and electronic medical records [EMRs]) in the United States. We also aimed
to validate the detection performance of the model for HAE cases using the Japanese dataset.
Methods: The HAE patient and control groups were identified using the US claims and EMR datasets. We analyzed thecharacteristics of the diagnostic history of patients with HAE and developed an AI model to predict the probability of HAE basedon a generalized linear model and bootstrap method. The model was then applied to the EMR data of the Kyoto University
Hospital to verify its applicability to the Japanese dataset.
Results: Precision and sensitivity were measured to validate the model performance. Using the comprehensive US dataset, theprecision score was 2% in the initial model development step. Our model can screen out suspected patients, where 1 in 50 ofthese patients have HAE. In addition, in the validation step with Japanese EMR data, the precision score was 23.6%, which
exceeded our expectations. We achieved a sensitivity score of 61.5% for the US dataset and 37.6% for the validation exercise
using data from a single Japanese hospital. Overall, our model could predict patients with typical HAE symptoms.
Conclusions: This study indicates that our AI model can detect HAE in patients with typical symptoms and is effective inJapanese data. However, further prospective clinical studies are required to investigate whether this model can be used to diagnose
HAE.
(JMIR Med Inform 2024;12:e59858) doi: 10.2196/59858
KEYWORDS
machine learning; screening; AI; prediction; rare diseases; HAE; electronic medical record; real world data; big data; angioedema;
edema; ML; artificial intelligence; algorithm; algorithms; predictive model; predictive models; predictive analytics; predictive
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Yamashita et alsystem; practical model; practical models; early warning; early detection; real world data; RWD; Electronic health record; EHR;
electronic health records; EHRs; EMR; electronic medical records; EMRs; patient record; patient record; health record; healthrecords; personal health record; PHR

Introduction
The rare genetic disease hereditary angioedema (HAE) induces
acute attacks of swelling in various regions of the body,
including the face, hands, arms, legs, abdomen, genitals,
buttocks, and throat. Gastrointestinal disturbances such as
abdominal pain, nausea, and vomiting are frequently associatedwith edema. Laryngeal edema is rare, even though more thanhalf of the patients with HAE encounter this life-threatening
condition [1]. Its global prevalence is estimated to be 1 in 50,000
people, with no reported bias among different ethnic groups[2]. In Japan, about 1 in 250,000 people are diagnosed with
HAE, which suggests that only 20% of potential HAE cases are
identified [3], suggesting that many patients with HAE remain
undiagnosed in Japan. Furthermore, in Japan, the mean durationfrom the first symptoms to diagnosis is 15.6 years [4], which
is longer than that in Europe and the United States [5,6]. Early
detection of undiagnosed patients is critical for effective
treatment of HAE.
To overcome this situation in Japan, the Diagnostic Consortium
to Advance the Ecosystem for Hereditary Angioedema(DISCOVERY) was established in 2021 [7]; it aimed to identify
patients with undiagnosed HAE and provide them withappropriate treatment as early as possible.
In this study, we aimed to develop an artificial intelligence (AI)model that can detect suspected patients with HAE using
medical history data (claims and electronic medical records[EMRs]) in the United States. We then sought to validate themodel’s performance in detecting HAE cases. In addition, we
conducted a pilot study at Kyoto University Hospital (KUHP)
using the EMR data to verify the model’s applicability to
medical data obtained from the Japanese population. The main

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objective of this study was to verify whether this model could
identify patients with a history of HAE or related diseases.

Methods
Overview
First, we developed an AI model using medical history datafrom the United States as a reference. Thereafter, we applied
the model to medical history data from Japan and verified its
efficacy using a Japanese dataset. Note that we used a large
dataset of patients from the United States as input for the model,
considering that HAE is a rare disease.

Initial Model Development with US Dataset
Data Selection
The Merative MarketScan Explorys Claims-EMR Data Set(formerly IBM Watson Health) [8] was used to obtainpatient-level linked claims and EMR data for US patients. The
diagnoses and prescription histories of patients with edema ordigestive symptoms from January 2012 to January 2021 wereidentified from the dataset and were used to build our model.
Data from a total of 4,283,815 patients were used in the study.
To identify the diagnosis history of patients, the International
Classification of Diseases (ICD) [9] code (ninth and 10thedition) available in this dataset was used. However, the ICD
code for HAE (D84.1) represents “defects in the complement
system,” which is also applicable to other similar diseases.
Therefore, we used the prescription history of drugs
administered only for HAE (Table 1) to distinguish patientswith HAE. We categorized the patients with a prescription
history of these drugs as the “HAE group,” representing patients
presumed to have HAE.

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Table 1. US Food and Drug Administration–approved medications used only for hereditary angioedema (as of January 2022).
Proprietary nameNonproprietary nameProduct NDCaBERINERT

Human C1-esterase inhibitor

63833-825

CINRYZE

Human C1-esterase inhibitor

42227-081
42227-083

FIRAZYR

Icatibant acetate

54092-702

HAEGARDA

Human C1-esterase inhibitor

63833-828
63833-829

KALBITOR

Ecallantide

47783-101

ORLADEYO

Berotralstat hydrochloride

72769-101
72769-102

RUCONESTC1 esterase inhibitor recombinant

70383-350
69913-350
71274-350

TAKHZYRO

lanadelumab-flyo

47783-644

Icatibant (Generic)Icatibant acetate or Icatibant

0093-3066
24201-207
60505-6214
63323-574
68462-828
69097-664
71225-114

SAJAZIRa

Icatibant70709-013

NDC: National Drug Code.

To maintain the demographic characteristics of the original data,
the control group was randomly sampled from 1% of theremaining patients, with a fixed ratio of age groups andmale-to-female ratio (Figure 1). Note that this was crucial toreduce the data volume to operate the model using limited
computation resources (2 central processing units and 16 GBof memory). This was done considering the potential use of themodel in various medical institutions in the future.

Figure 1. Comparison of the distribution of the 1% sampled data set with that of the population. dobyr: date of birth year.

Finally, 3 groups were included for model development andvalidation (Figure 2): the HAE group with 179 patients, D84.1(including individuals that likely have HAE but do not have a
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patients, and the control group with 42,839 patients.

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Figure 2. Flowchart depicting the different patient groups created using the US data set; HAE: hereditary angioedema.

To develop the model, the ICD code was used to create featuresthat described the diagnostic history of patients. As this dataset
contained both ICD-9 and ICD-10 codes throughout the data
period, we standardized the 2 ICD types. We assigned codes
representing the same disease items from both ICD-9 andICD-10 codes under a single ID.

Model Development
Feature Selection
We counted the number of types of ICD codes diagnosed inboth the HAE and D84.1 groups, as these 2 groups should have
similar features. Furthermore, the differences in ICD code types
between the groups were required to create a model that canidentify patients with HAE. We examined rank correlations
between the 2 groups and found it to be approximately 0.08,
which suggested that the 2 groups had different characteristics.
We then examined specific ICD codes that were significantly
ranked differently between the two groups and identified 25
such ICD codes, which were then used as the primary featuresin developing the model.
We also examined ICD codes that were diagnosed several times
over a period of 1 year. This is important as patients with HAEtend to have repeated occurrences of swelling in various regionsof the body [1], which can lead to the diagnosis of stomachaches
and edemas. We counted the number of patients who had been
diagnosed with stomachaches or edemas between 2 and 4 times
per year and found a substantial difference between both groups.
Considering that the medical record entry may overlap multiple
times when changing the record types, we conducted theremoval of duplicates based on the date and ICD code for eachpatient. Thereafter, we labeled a group of ICD codes related toabdominal pain or edemas and counted the number of times
they were assigned in a 1-year span window for each patient
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incidences of stomachaches and 3 or more incidences of edema
per year as part of the main features of our model. The table ofthe explanatory variables is provided in Multimedia Appendix
1.
Model Building
The number of patients in the HAE group was extremely small
compared with that in the control + D84.1 groups; thus, to avoid
overfitting, we used bootstrap sampling [10,11] to create themodel. A generalized linear model [12] with regularization
terms [13,14] was adopted. We used
for the linkfunction to create a logistic model that would indicate thelikelihood of the patient belonging to the HAE group. We choselogistic regression for evaluation as it allows for regression withregularization and is relatively easy to use for evaluating andinterpreting feature importance by checking the coefficients.
The estimation of the partial regression coefficients wascalculated by the maximum likelihood method, which estimatesparameters (known as maximum likelihood estimates) thatmaximize the likelihood of the given observed values. The
regularization parameter λ was set to 1 to ensure that it was
Lasso regularization.
We used 25% of the data from the HAE group and another 25%
from the control + D84.1 groups to train the model, which wasthen used to predict the remaining 75% of each group. Thismodeling process was performed 20 times with different random
seeds. The average predicted value was calculated as the final
output for all the patients. In each trial, the sample used astraining data did not have a predicted value and was excluded
from the average value calculation (Figure 3). Upon applying
the regularization using Lasso regression, the number ofsubstantial features was sorted out during each calculation bymathematically adjusting the coefficients of some variables to
0. The number of sorted features varied with an average of 10;
notably, different features were selected every time.

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Figure 3. Training data extraction and prediction calculation of the constructed model. HAE: hereditary angioedema.

Evaluation Method and Threshold Setting
After obtaining the final value for each participant, we
performed Welch t test on the 2 distributions to confirm thatthe 2 groups had different means. Subsequently, we defined thethreshold value that yielded the most balanced classification
accuracy using the receiver operating characteristic (ROC)
curve. ROC curves help visualize the entire scenario oftrade-offs between sensitivity and precision across a set of cutoff
points. The volumes of the HAE and control + D84.1 groups
were not equal; therefore, it was important to check the balance
between sensitivity and precision rather than the accuracy itself.

Model Application to Japanese EMR Data
Data Extraction and Model Application
For the validation step using Japanese data, data were extracted
from a data warehouse (DWH), which collects medical datafrom the EMR of the KUHP. Patient IDs in the DWH are
pseudonymized. The medical data were obtained for a total of702,213 patients, among which 22 had a history of HAE, 47
had a confirmed diagnosis of HAE, and 123 had a suspected
diagnosis of HAE. The data for model validation included those
associated with patients from all these groups (patients using

drugs for HAE, patients with confirmed HAE, and patientssuspected to have HAE). This was done because physicians
may have suspected HAE for some patients if their symptoms
were similar to those of patients with the condition. Therefore,
these 3 types of patients were considered as the patients with
HAE in the study (HAE group; Figure 4).
To adapt the model to Japanese data, we used the standard
disease name codes widely used in Japan, as defined by the
Medical Information System Development Center (MEDIS-DC)[15], instead of the ICD code. Although the ICD code is thebasic classification code for diagnosis, the standard diseasename codes have more subdivisions compared with the ICD
code, and hence, they can provide a more precise clinicaldiagnosis. We converted the ICD codes using the standard
disease name code master for ICD-10 [16].
Patient data extracted from the DWH were transferred to theGoogle Cloud Platform server (a virtual private cloudenvironment) hosted at KUHP. The AI model and statisticalprograms were stored in a container and sent to the server. We
then accessed the server through a virtual private network, which
could only be accessed by the authors of this study. The model
was applied to all patient data on this server.

Figure 4. Flowchart depicting the different patient groups obtained from the KUHP data set. HAE: hereditary angioedema.

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Ethical Considerations
This study was approved by the Ethics Committee of the Kyoto
University Hospital (approval number R3750). In this study,
we used pseudonymized information that had already beenprocessed, thus individual informed consent was not required.
The pseudonymized medical data is made available for academic
research in accordance with KUHP’s privacy policy. Information
regarding each study is publicly disclosed on the institution’s
website, where patients are informed of their right to opt out
along with the opt-out procedure.

Results
Evaluation of the Initial Model
Welch t test indicated that the 2 patient groups did not have thesame mean values, as suggested by the P value of 2.2e-16.

Furthermore, the area under the ROC curve was 86.4%, which
was obtained when only the HAE group was set as true and all
the other groups as false. The best accuracy threshold of thisROC curve was calculated as 39%, with an accuracy of 99.6%.
This is because the volume of the control + D84.1 group waslarger than that of the HAE group. The true-positive (sensitivity)of this threshold was only 10.6%, with a precision of 54.3%.
As we aimed to identify patients likely to have HAE, wesearched for a different threshold that could improve thesensitivity while keeping the precision at an acceptable level.
Considering the fact that the prediction of the HAE group had
and, 0.075-0.125 could be a goodthreshold candidate. We confirmed the sensitivity and precision
for the thresholds of 0.075, 0.1, and 0.125 to determine the mostbalanced threshold, as shown in Table 2.

Table 2. Cross-tabulation was calculated at 3 different threshold values using all data groups for a detailed evaluation of different scaled precisions
and group sensitivities.
Control group

D84.1
group

D84.1
and

HAE group Score (%)(not D84.1)

HAEa
100%
1% scalescale converged

1% scaleprecision

100%
Sensitivity
scale pre- 1bcisionSensitivity

30

27.1

2.0

61.5

52.5

d

2c

Suspection statistic (threshold=0.1)
Not suspected, nSuspected, n

4,279,500 42,795
4400

44

1312

55

209

88

6

—

—

—

—

Suspection statistic (threshold=0.075)
Not suspected, n

4,277,900 42,779

1266

52

28

23.9

1.6

63.6

55.3

Suspected, n

6000

255

91

8

—

—

—

—

60

Suspection statistic (threshold=0.125)a

Not suspected, n

4,280,600 42,806

1343

62

33

28.5

2.4

56.6

46.9

Suspected, n

3300

178

81

3

—

—

—

—

33

HAE: hereditary angioedema.

b

Excluding “not D84.1” patients.

cIncluding “not D84.1” patients.

d

Not applicable.

The threshold value of 0.1 had a sensitivity of 52.5% andprecision of 27.1%, indicating that 1 out of 2 known HAE group
participants can be correctly detected, and 1 out of 4 detectedparticipants should correctly belong to the HAE group. If we
exclude the HAE group participants who were not diagnosed
with D84.1, the sensitivity was 61.5%. This result was calculated
based on 1% of the sample size of the original control group;
thus, by multiplying the number of all participants from thecontrol group by 100, we obtained a 100% scale precision of
2%. This was 2 times better than the 1% precision goal set atthe beginning of the study. This means that based on this model,
1 out of 50 suspected patients is highly likely to have HAE.
Considering that HAE prevalence is estimated to be 1 in 50,000
people, we can expect to find undiagnosed patients with HAEquickly and efficiently using this model output.
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From a conservative standpoint, the threshold value of 0.1 seems
optimal. However, to identify more potential patients with HAE,
it might be better to apply the 0.075 threshold, which has asensitivity of 55.3% and a precision of 23.9%. If we recalculate
the 100% scale precision in the same manner as described above,
we obtain 1.6%. This means we can still achieve our goal of
1% precision while improving the sensitivity.
In addition, we need to consider the fact that the ratio of
suspected patients in the US dataset can be calculated to beapproximately 0.09% with a 0.1 threshold and 0.15% with athreshold of 0.075. If this model is to be used on a much smaller
volume dataset compared with the US dataset, there is anapproximately 2 times higher risk of obtaining zero suspectedpatients with a 0.1 threshold than with the 0.075 threshold.

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Application of the Model to Japanese EMR Data
To verify the performance of this model using Japanese data,
it was applied to patient data obtained from KUHP, and theoutput of potential patients with HAE was obtained based on
the selected threshold. The diagnostic histories of these patientswere stored at a single university hospital. Compared with thedataset used to build the original model, the variation andcoverage of the entire diagnostic history were assumed to berelatively low. Therefore, we adopted a threshold value of 0.075
in this validation study to aggressively identify patients with
HAE. We considered the HAE group (Figure 4) as the correct
data for this validation.
As shown in Table 3, 65 of 173 patients with HAE were detectedusing this model, indicating a sensitivity of 37.6%.

Table 3. Cross-tabulation with precision and sensitivity scores of Kyoto University Hospital results.
Control group

HAEa group
Prescripted

Score (threshold=0.075)
Prescripted andDiagnosed

Precision (%)

Sensitivity (%)

3.2

31.8

diagnosed
Suspection statistic (threshold=0.075)

a

Not suspected

701,8292

13

93

Suspected

211

1

6

58

HAE: hereditary angioedema.

Some patients in the HAE group did not have a diagnostic
history specific to HAE (eg, abdominal pain, swelling, or edema)
within the KUHP data. Their common symptoms might havebeen treated by their primary doctors or clinics and not at thisuniversity hospital. Furthermore, because HAE is a hereditarydisorder, some patients may have been diagnosed through family
testing. These factors appear to lead to a lower sensitivity score
for the Japanese dataset than that for the US data.

symptoms. Considering this situation, data obtained from asingle university may have some difficulty with model
performance. Although the sensitivity score was lower than thatof the US dataset (37.6%), the precision score reached 23.6%
with a threshold value of 0.075. This implies that our model
has a high possibility of identifying patients with undiagnosed
HAE in Japan.

The precision score was 23.6%, which is more than 14 times
higher than that of the initial model. As mentioned in the
Introduction section, only 20% of patients in Japan are
diagnosed with HAE, which means that 80% of patients with
HAE are undiagnosed. Therefore, the 211 patients from thecontrol group who were suspected to have HAE in our model
may be undiagnosed patients with HAE.

Our study had several limitations. Generally, because HAE isa rare disease, patient group data (correct answer data in machinelearning) are quite small. In addition, the variance in eachpatient’s features was larger than that in common diseases. We
also suggest possible limitations and countermeasures.

Discussion
Principal Findings
In this study, we developed an AI model for screening patientswith HAE and validated its performance using 2 methods.

Limitations

Family History
In our basic analysis of the HAE group, we found that somepatients in the HAE group had a lower diagnostic history thanothers. We suspected that these patients had been diagnosed
with HAE based on their family histories. Because our model
uses the diagnostic history to calculate the probability, thesecases are potentially difficult to detect.

First, a large patient dataset was selected to build a model
containing patient-level linked claims and EMR data from theUnited States. The advantage of this dataset is that it contains
a long-term prescription and diagnostic history across multiple
medical institutes. The diagnostic characteristics of patientswith HAE were determined by analyzing the dataset. Based onthese characteristics, we constructed a generalized linear modelwith regularization terms. At a threshold of 0.1, the sensitivity
score was 52.5% and the precision score was 27.1% if patientswith possible HAE were included in the correct answer group.
When these were excluded from the correct answers, thesensitivity score was 61.5%.

US Patient Data Consists of Data From Multiple
Hospitals

We then applied this model to Japanese EMR data. Thisvalidation was conducted at a single university hospital using
DWH data. Generally, patients often visit local hospitals andrarely visit university hospitals if they present with common

Since the HAE diagnosis rate was low, it is likely that there
were more patients with HAE in the control + D84.1 groups.
In our approach, we assigned the HAE group a prescription
history of HAE drugs to keep the model conservative.

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hospitals will allow tracking of the records of a single patient
across these hospitals and provide a more detailed medical
history. For validation in the Japanese dataset, we could onlyuse data from a single university hospital, which may be oneof the reasons for the low sensitivity.

Potential Patients With HAE Might Be Included in theControl + D84.1 Groups

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Possible Difference in Diagnostic Tendency Between
the United States and Japan
If there are differences in how doctors make diagnostics between
countries, we may need to customize the model or threshold toadapt it to Japan and other countries.

Comparison With Previous Work
Few previous studies have focused on screening patients forrare diseases based on diagnostic histories such as medical
claims. Nonetheless, some studies have focused on a few rarediseases. For example, a previous study used AI models basedon diagnostic history to identify patients with Pompe disease[17]. In this study, 104 patients were flagged by the model tohave the disease, but only 19 were determined by specialists tohave a high likelihood of having Pompe disease, rendering aprecision score of 18.27% [17]. In comparison, our modelrecorded a precision of 23.6%. Screening for rare diseases is
extremely difficult compared with other common diseases, forwhich abundant data exist; however, our results indicate that
AI models can show high performance for screening rarediseases.

Conclusions and Future Directions
Considering the prevalence of HAE (1/50,000), the screening
performance of this model was 1,000 times greater than that
achieved through random searching using US data. Owing totheir prevalence and recognition rates, identifying undiagnosed
patients with rare diseases is an arduous task. This study
suggests that patient screening for HAE may become

Yamashita et al
significantly more efficient if this AI model is used. Thisapproach is particularly valuable for the diagnosis and treatmentof rare diseases.
In addition, during the validation phase using the Japanese data,
the model was effective at a single university hospital. Although
only the diagnosis codes recorded in the EMR were available,
the model could detect patients with typical symptoms of HAE.
The performance of the model can likely be improved further
if this model is applied to the data from city hospitals or medical
claims, which contain diagnostic histories of patients in multiple
medical institutions. This can provide more comprehensive
information on the symptoms and diagnostic histories of eachpatient.
In this study, only patients with a diagnostic history of HAE
within the dataset were defined as correct answers. By providing
a diagnosis rate, these data may include patients with undetected
HAE. The model performance cannot be strictly calculated insuch situations. Therefore, further studies are needed todetermine whether patients with undiagnosed HAE should be
included in the predicted group. This is because identifying
undiagnosed patients with HAE is a critical issue, especially inJapan; we will implement a prospective clinical study using our
AI model.
The constructed model may help researchers, physicians, andother health care professionals identify undiagnosed HAE cases.
Eventually, if this strategy can identify undiagnosed patientsand provide them with proper treatment, their quality of life
will likely be improved.

Acknowledgments
We acknowledge the support received from DISCOVERY (Diagnostic Consortium to Advance the Ecosystem for Hereditary
Angioedema), which covered the publication costs of the study.

Conflicts of Interest
None declared.

Multimedia Appendix 1
Table of the explanatory variables.
[PNG File , 147 KB-Multimedia Appendix 1]

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Abbreviations
AI: artificial intelligence
DISCOVERY: Diagnostic Consortium to Advance the Ecosystem for Hereditary Angioedema
DWH: data warehouse
EMR: electronic medical record
HAE: hereditary angioedema
ICD: International Classification of Diseases
KUHP: Kyoto University Hospital
MEDIS-DC: Medical Information System Development Center
ROC: receiver operating characteristic

Edited by C Lovis; submitted 26.04.24; peer-reviewed by T Takemura, R Zhu, J Wu; comments to author 25.05.24; revised version
received 13.06.24; accepted 06.08.24; published 13.09.24
Please cite as:
Yamashita K, Nomoto Y, Hirose T, Yutani A, Okada A, Watanabe N, Suzuki K, Senzaki M, Kuroda T
Early Diagnosis of Hereditary Angioedema in Japan Based on a US Medical Dataset: Algorithm Development and Validation
JMIR Med Inform 2024;12:e59858
URL: https://medinform.jmir.org/2024/1/e59858
doi: 10.2196/59858
PMID:©Kouhei Yamashita, Yuji Nomoto, Tomoya Hirose, Akira Yutani, Akira Okada, Nayu Watanabe, Ken Suzuki, Munenori Senzaki,
Tomohiro Kuroda. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 13.09.2024. This is an
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