# Logistic Regression Analysis of Clinical Characteristics for

Authors: Shuaixing Guo
Journal: Emergency Medicine International
 (2023)

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## Abstract

Background. This study aimed to investigate the predictive
value of general clinical data, blood test indexes, and ventilation function
test indexes on the severity of chronic obstructive pulmonary disease (COPD).
Methods. A total of 141 clinical characteristics of COPD
patients admitted to our hospital were collected. A mild-to-moderate group and a
severe group were classified depending on the severity of COPD, and their
baseline data were compared. The predictive factors of severe COPD were analyzed
by univariate and multivariate logistic regression, and the nomogram model of
severe COPD was constructed. The clinical variables, including gender, height,
weight, body mass index (BMI), age, course, diabetes, hypertension, smoking
history, WBC, NEUT, lymphocyte count (LY), MONO, eosinophil count (EOS), PLT,
mean platelet volume (MPV), platelet distribution width (PDW), partial pressure
of oxygen (PaO2), and PaCO2, were collected.
Results. There were 67 mild-to-moderate COPD patients and
74 severe COPD patients in this study cohort. Severe COPD had a higher white
blood cell count (WBC), neutrophil count (NEUT), monocyte count (MONO), platelet
count (PLT), neutrophil to lymphocyte ratio (NLR), and a lower partial pressure
of carbon dioxide (PaCO2). Univariate logistic regression analysis
showed that WBC, NEUT, MONO, PLT, and NLR were contributing factors of severe
COPD, while PaCO2 was an unfavorable factor of severe COPD. Enter,
forward, backward, and stepwise multivariate logistic regression analyses all
showed that NEUT and PLT were independent contributing factors to severe COPD.
Moreover, the nomogram model had good predictive ability, with an area under the
curve (AUC) of the receiver operating characteristic (ROC) curve being 0.881.
Good calibration and clinical utility were validated through the calibration
plot and the decision curve analysis (DCA) plot, respectively.
Conclusion. The severity of COPD was correlated with NEUT
and PLT, and the nomogram model based on these factors had good predictive
performance.


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## Body

## 1. Introduction

Chronic obstructive pulmonary disease (COPD), one of the most common respiratory
diseases, is characterized by persistent airflow limitation and multiple
complications [1]. Its high morbidity,
hospitalization, disability, and mortality rates impose a serious economic burden on
families and society [2]. According to the
2010 Global Burden of Disease Study, COPD was estimated to be the third leading
cause of life expectancy loss in China [3].
The latest statistics from the World Health Organization show that moderate or
severe COPD affects approximately 65 million people worldwide and that COPD will be
the third leading cause of death worldwide by 2030. Correct assessment of disease
severity and optimal treatment are essential for better clinical and socioeconomic
outcomes for COPD patients [4].The Global Initiative for Chronic Obstructive Lung Disease (GOLD) guidelines state
that forced expiratory volume in one second (FEV1) and forced vital capacity (FVC)
can be used as valid indicators of lung function [5]. Based on these two indicators, the condition can be classified into
four classes. However, pulmonary function testing is a test that relies on
patient-physician cooperation, and test results depend on measurement technique and
personal factors. A related study has shown that nearly half of the pulmonary
function tests have unreliable data due to failure to complete the test effectively,
causing some disturbance in treatment [6].
Therefore, there is an urgent clinical need to find indicators for the assessment of
COPD severity.Recent studies have found that C-reactive protein (CRP), procalcitonin (PCT),
interleukin-6 (IL-6), IL-8, tumor necrosis factor-α
(TNF-α), and other inflammatory indicators are all
associated with the development of COPD [7,
8]. However, each of these indicators has
its own advantages and disadvantages. For example, CRP and PCT assays are economical
and convenient but susceptible to a variety of factors. IL-6, IL-8, and
TNF-α are highly sensitive but more expensive to detect.
As emerging inflammatory indicators, the neutrophil-to-lymphocyte ratio (NLR),
platelet-to-lymphocyte ratio (PLR), and lymphocyte-to-monocyte ratio (LMR) are
derived from the complete blood count and all are related to the degree of
inflammation and clinical symptoms of COPD [9,
10]. Elevated NLR levels have been
reported in thyroid conditions [11],
irritable bowel disease [12], COVID-19
infection [13], diabetes mellitus [14], and thyroiditis [15]. However, the use of blood count indicators and their
derivatives in the classification of COPD severity has rarely been reported.To accurately classify and effectively treat COPD patients, many scholars have
investigated machine learning algorithms to assist clinical decision-making [16, 17].
This study proposes a method for severity classification assessment and risk
prediction of COPD patients’ conditions using common clinical information
when lung function tests are not available and to assist physicians in patient
classification based on the severity of different COPD patients.


## 2. Methods

### 2.1. Clinical Data Collection

141 COPD patients treated in our hospital were included, with 67 patients having
mild-to-moderate and 74 patients having severe. The severity of COPD was
clinically assessed in these patients. The clinical variables were collected,
including gender, height, weight, body mass index (BMI), age, course, diabetes,
hypertension, smoking history, WBC, NEUT, lymphocyte count (LY), MONO,
eosinophil count (EOS), PLT, mean platelet volume (MPV), platelet distribution
width (PDW), partial pressure of oxygen (PaO2), and PaCO2.
Besides, three inflammation indicators, including NLR, platelets and lymphocytes
ratio (PLR), and lymphocytes to monocytes ratio (LMR), were calculated. All
patients signed the consent form, and this study has been approved by the
Ethical Committee of the University of Chinese Academy of Sciences Shenzhen
Hospital.


### 2.2. Statistical Analysis

R4.2.0 was used for data processing. Categorical data were expressed as
frequencies and percentages and compared by the chi-squared test between groups.
Measurement data were tested for normality, and then normally distributed
measurement data were expressed as mean ± standard deviation
and compared by at-test between groups. Nonnormally
distributed measurement data were expressed as median (interquartile range) and
compared by the Wilcoxon rank sum test between groups. The difference was
considered statistically significant at P<0.05. A logistic regression analysis was used to examine the
influencing factors predicting COPD severity. Significant predictive factors in
the univariate logistic regression analysis were selected for the multivariate
logistic regression analysis with enter, forward, backward, or stepwise methods,
respectively. Then, a nomogram model was developed based on independent
predictive factors with the rms package of R. The discrimination of the nomogram
model was estimated by the receiver operating characteristic (ROC) curve. The
calibration was validated by a calibration plot using the bootstrap method with
50 repetitions using the caret package. The clinical utility was analyzed by a
decision curve analysis (DCA) plot using the rmda package. For internal
validation, 5-fold cross-validation was applied.


## 2.1. Clinical Data Collection

141 COPD patients treated in our hospital were included, with 67 patients having
mild-to-moderate and 74 patients having severe. The severity of COPD was
clinically assessed in these patients. The clinical variables were collected,
including gender, height, weight, body mass index (BMI), age, course, diabetes,
hypertension, smoking history, WBC, NEUT, lymphocyte count (LY), MONO,
eosinophil count (EOS), PLT, mean platelet volume (MPV), platelet distribution
width (PDW), partial pressure of oxygen (PaO2), and PaCO2.
Besides, three inflammation indicators, including NLR, platelets and lymphocytes
ratio (PLR), and lymphocytes to monocytes ratio (LMR), were calculated. All
patients signed the consent form, and this study has been approved by the
Ethical Committee of the University of Chinese Academy of Sciences Shenzhen
Hospital.


## 2.2. Statistical Analysis

R4.2.0 was used for data processing. Categorical data were expressed as
frequencies and percentages and compared by the chi-squared test between groups.
Measurement data were tested for normality, and then normally distributed
measurement data were expressed as mean ± standard deviation
and compared by at-test between groups. Nonnormally
distributed measurement data were expressed as median (interquartile range) and
compared by the Wilcoxon rank sum test between groups. The difference was
considered statistically significant at P<0.05. A logistic regression analysis was used to examine the
influencing factors predicting COPD severity. Significant predictive factors in
the univariate logistic regression analysis were selected for the multivariate
logistic regression analysis with enter, forward, backward, or stepwise methods,
respectively. Then, a nomogram model was developed based on independent
predictive factors with the rms package of R. The discrimination of the nomogram
model was estimated by the receiver operating characteristic (ROC) curve. The
calibration was validated by a calibration plot using the bootstrap method with
50 repetitions using the caret package. The clinical utility was analyzed by a
decision curve analysis (DCA) plot using the rmda package. For internal
validation, 5-fold cross-validation was applied.


## 3. Results

### 3.1. Baseline Data Comparison

This study included 141 COPD patients, with 67 having mild-to-moderate COPD and
74 having severe COPD (Table1). In
severe COPD, WBC (Figure 1(a)), NEUT
(Figure 1(b)), MONO (Figure 1(c)), PLT (Figure 1(d)), and NLR (Figure 1(e)) were higher than those in mild-to-moderate COPD, while
PaCO2 (Figure 1(f)) was
lower than that in mild-to-moderate COPD.Table 1
Comparison of clinical characteristics between the mild-to-moderate group
and the severe group in COPD patients.
CharacteristicsMild-to-moderateSeverepn6774Gender,n
(%)Female16 (11.3%)11 (7.8%)0.252Male51 (36.2%)63 (44.7%)Diabetes,n
(%)No59 (41.8%)70 (49.6%)0.277Yes8 (5.7%)4 (2.8%)Hypertension,n
(%)No39 (27.7%)38 (27%)0.517Yes28 (19.9%)36 (25.5%)Smoking history,n
(%)No24 (17%)25 (17.7%)0.939Yes43 (30.5%)49 (34.8%)Height (cm), median (IQR)167 (161.5, 170)168 (164.25, 171)0.161Weight (kg), mean ± SD58.94 ± 7.458.29 ± 9.50.656BMI(kg/m2), median (IQR)21.67 (19.95, 22.76)20.99 (19.39, 22.68)0.131Age (year), median (IQR)78 (70, 82)74 (65.25, 81)0.145Course (year), median (IQR)3 (0, 10)3 (0.25, 8.75)0.745WBC (109/L), median (IQR)6.64 (5.56, 7.38)10.19 (8.32, 11.67)<0.001∗∗∗NEUT (109/L), median (IQR)4.65 (3.62, 5.48)7.96 (6.02, 9.82)<0.001∗∗∗LY (109/L), median (IQR)1.02 (0.66, 1.56)1.19 (0.77, 1.63)0.347MONO (109/L), median (IQR)0.51 (0.41, 0.61)0.62 (0.46, 0.82)0.006∗∗EOS (109/L), median (IQR)0.09 (0.03, 0.22)0.12 (0.02, 0.25)0.988PLT (109/L), median (IQR)181 (148, 218)234 (180.75, 302)<0.001∗∗∗MPV (fL), mean ± SD9.15 ± 0.989.06 ± 1.170.630PDW (%), median (IQR)16 (15.7, 16.3)16 (15.7, 16.3)0.828NLR, median (IQR)4.21 (2.53, 6.15)6.34 (3.56, 13.62)<0.001∗∗∗PLR, median (IQR)167.71 (116.07, 255.48)205.2 (136.29, 305.16)0.086LMR, median (IQR)1.88 (1.51, 3.39)1.96 (1.25, 3.01)0.331PaO2 (mmHg), median (IQR)78 (67.05, 95.4)74.7 (63.68, 95.55)0.668PaCO2 (mmHg), median (IQR)60.1 (47.55, 76.65)48.25 (42.85, 59.45)0.002∗∗∗∗=0.01, ∗∗∗=<0.001.Figure 1
The difference in clinical characteristics, including WBC (a), NEUT (b),
MONO (c), PLT (d), NLR (e), and PaCO2 (f), between
mild-to-moderate and severe COPD groups.
(a)(b)(c)(d)(e)(f)


### 3.2. Logistic Regression Analysis of Influencing Factors with Severity in
COPD Patients

Through univariate logistic regression analysis on all clinical variables, we
identified WBC, NEUT, MONO, PLT, NLR, and PaCO2 as influencing
factors of severe COPD (Table 2).
Furthermore, through multivariate logistic regression analysis with enter
(Figure 2), forward (Figure 3), backward (Figure 4), and stepwise (Figure 5) methods, we identified NEUT and PLT as independent predictive
factors.Table 2
Results of univariate logistic regression analysis on influencing factors
of severity in COPD patients.
VariablesβSEzpOR (95% CI)Gender−0.5860.435−1.3480.1780.55655 (0.23744, 1.30451)Height0.040.0281.4270.1541.04085 (0.98515, 1.0997)Weight−0.0090.020−0.4490.6530.99112 (0.95327, 1.03047)BMI−0.080.062−1.2850.1990.92327 (0.81739, 1.04287)Age−0.0260.018−1.4360.1510.9746 (0.94095, 1.00944)Course0.0080.0310.2410.8091.00755 (0.94788, 1.07097)Diabetes−0.8640.637−1.3560.1750.42143 (0.12084, 1.46978)Hypertension0.2770.3400.8160.4141.31955 (0.67796, 2.56829)Smoking history0.090.3540.2540.8001.09395 (0.54658, 2.18949)WBC0.5410.1045.2180.000∗∗∗1.71844 (1.40223, 2.10597)NEUT0.6990.1285.4790.000∗∗∗2.01129 (1.56646, 2.58243)LY0.2660.2231.1940.2321.30453 (0.84329, 2.01806)MONO1.7050.6552.6030.009∗∗5.50173 (1.52347, 19.86855)EOS0.3410.7200.4740.6361.40635 (0.34318, 5.76325)PLT0.0120.0034.0730.000∗∗∗1.01214 (1.00628, 1.01803)MPV−0.0760.157−0.4850.6280.92687 (0.68203, 1.25961)PDW0.0320.1180.2720.7861.03255 (0.81963, 1.30079)NLR0.1180.0373.1680.002∗∗1.12514 (1.04598, 1.2103)PLR0.0020.0011.3220.1861.0016 (0.99923, 1.00397)LMR−0.1280.099−1.2960.1950.87989 (0.72511, 1.06771)PaO2−0.0070.010−0.6820.4950.99315 (0.97372, 1.01297)PaCO2−0.030.010−2.9110.004∗∗0.97077 (0.95157, 0.99035)∗∗=0.01, ∗∗∗=<0.001.Figure 2
Multivariate logistic regression analysis with enter method.Figure 3
Multivariate logistic regression analysis with forward method.Figure 4
Multivariate logistic regression analysis with backward method.Figure 5
Multivariate logistic regression analysis with stepwise method.


### 3.3. Nomogram Model for Predicting COPD Severity

Based on NEUT and PLT, we constructed a nomogram model for predicting COPD
severity (Figure6). The AUC of the ROC
curve was 0.881, indicating good discrimination of the nomogram model (Figure
7(a)). Besides, the predicted
probability was close to the observed probability in the calibration plot,
suggesting good calibration of the nomogram model (Figure 7(b)). Furthermore, the DCA curve implied good clinical
utility of the nomogram model (Figure 7(c)). For internal validation, 5-fold cross-validation was applied,
and the mean AUC of the training sets was 0.87492 (Table 3).Figure 6
Nomogram model based on independent predictive factors for COPD
severity.Figure 7
The performance of the nomogram model. (a) ROC curve. (b) Calibration
curve. (c) DCA curve.
(a)(b)(c)Table 3
5-fold cross-validation assessing the prediction capability of our
model.
FoldCox-SnellR2NagelkerkeR2AccuracyPrecisionRecallF_measureAUC10.837540.841520.896550.894740.944440.918920.9494920.892340.895800.785710.800000.666670.727270.8177130.902700.906080.750000.846150.687500.758620.8125040.862020.865320.857140.909090.769230.833330.8974450.893510.896870.821430.812500.866670.838710.89744Mean0.877620.881120.822170.852500.786900.815370.87492


## 3.1. Baseline Data Comparison

This study included 141 COPD patients, with 67 having mild-to-moderate COPD and
74 having severe COPD (Table1). In
severe COPD, WBC (Figure 1(a)), NEUT
(Figure 1(b)), MONO (Figure 1(c)), PLT (Figure 1(d)), and NLR (Figure 1(e)) were higher than those in mild-to-moderate COPD, while
PaCO2 (Figure 1(f)) was
lower than that in mild-to-moderate COPD.Table 1
Comparison of clinical characteristics between the mild-to-moderate group
and the severe group in COPD patients.
CharacteristicsMild-to-moderateSeverepn6774Gender,n
(%)Female16 (11.3%)11 (7.8%)0.252Male51 (36.2%)63 (44.7%)Diabetes,n
(%)No59 (41.8%)70 (49.6%)0.277Yes8 (5.7%)4 (2.8%)Hypertension,n
(%)No39 (27.7%)38 (27%)0.517Yes28 (19.9%)36 (25.5%)Smoking history,n
(%)No24 (17%)25 (17.7%)0.939Yes43 (30.5%)49 (34.8%)Height (cm), median (IQR)167 (161.5, 170)168 (164.25, 171)0.161Weight (kg), mean ± SD58.94 ± 7.458.29 ± 9.50.656BMI(kg/m2), median (IQR)21.67 (19.95, 22.76)20.99 (19.39, 22.68)0.131Age (year), median (IQR)78 (70, 82)74 (65.25, 81)0.145Course (year), median (IQR)3 (0, 10)3 (0.25, 8.75)0.745WBC (109/L), median (IQR)6.64 (5.56, 7.38)10.19 (8.32, 11.67)<0.001∗∗∗NEUT (109/L), median (IQR)4.65 (3.62, 5.48)7.96 (6.02, 9.82)<0.001∗∗∗LY (109/L), median (IQR)1.02 (0.66, 1.56)1.19 (0.77, 1.63)0.347MONO (109/L), median (IQR)0.51 (0.41, 0.61)0.62 (0.46, 0.82)0.006∗∗EOS (109/L), median (IQR)0.09 (0.03, 0.22)0.12 (0.02, 0.25)0.988PLT (109/L), median (IQR)181 (148, 218)234 (180.75, 302)<0.001∗∗∗MPV (fL), mean ± SD9.15 ± 0.989.06 ± 1.170.630PDW (%), median (IQR)16 (15.7, 16.3)16 (15.7, 16.3)0.828NLR, median (IQR)4.21 (2.53, 6.15)6.34 (3.56, 13.62)<0.001∗∗∗PLR, median (IQR)167.71 (116.07, 255.48)205.2 (136.29, 305.16)0.086LMR, median (IQR)1.88 (1.51, 3.39)1.96 (1.25, 3.01)0.331PaO2 (mmHg), median (IQR)78 (67.05, 95.4)74.7 (63.68, 95.55)0.668PaCO2 (mmHg), median (IQR)60.1 (47.55, 76.65)48.25 (42.85, 59.45)0.002∗∗∗∗=0.01, ∗∗∗=<0.001.Figure 1
The difference in clinical characteristics, including WBC (a), NEUT (b),
MONO (c), PLT (d), NLR (e), and PaCO2 (f), between
mild-to-moderate and severe COPD groups.
(a)(b)(c)(d)(e)(f)


## 3.2. Logistic Regression Analysis of Influencing Factors with Severity in
COPD Patients

Through univariate logistic regression analysis on all clinical variables, we
identified WBC, NEUT, MONO, PLT, NLR, and PaCO2 as influencing
factors of severe COPD (Table 2).
Furthermore, through multivariate logistic regression analysis with enter
(Figure 2), forward (Figure 3), backward (Figure 4), and stepwise (Figure 5) methods, we identified NEUT and PLT as independent predictive
factors.Table 2
Results of univariate logistic regression analysis on influencing factors
of severity in COPD patients.
VariablesβSEzpOR (95% CI)Gender−0.5860.435−1.3480.1780.55655 (0.23744, 1.30451)Height0.040.0281.4270.1541.04085 (0.98515, 1.0997)Weight−0.0090.020−0.4490.6530.99112 (0.95327, 1.03047)BMI−0.080.062−1.2850.1990.92327 (0.81739, 1.04287)Age−0.0260.018−1.4360.1510.9746 (0.94095, 1.00944)Course0.0080.0310.2410.8091.00755 (0.94788, 1.07097)Diabetes−0.8640.637−1.3560.1750.42143 (0.12084, 1.46978)Hypertension0.2770.3400.8160.4141.31955 (0.67796, 2.56829)Smoking history0.090.3540.2540.8001.09395 (0.54658, 2.18949)WBC0.5410.1045.2180.000∗∗∗1.71844 (1.40223, 2.10597)NEUT0.6990.1285.4790.000∗∗∗2.01129 (1.56646, 2.58243)LY0.2660.2231.1940.2321.30453 (0.84329, 2.01806)MONO1.7050.6552.6030.009∗∗5.50173 (1.52347, 19.86855)EOS0.3410.7200.4740.6361.40635 (0.34318, 5.76325)PLT0.0120.0034.0730.000∗∗∗1.01214 (1.00628, 1.01803)MPV−0.0760.157−0.4850.6280.92687 (0.68203, 1.25961)PDW0.0320.1180.2720.7861.03255 (0.81963, 1.30079)NLR0.1180.0373.1680.002∗∗1.12514 (1.04598, 1.2103)PLR0.0020.0011.3220.1861.0016 (0.99923, 1.00397)LMR−0.1280.099−1.2960.1950.87989 (0.72511, 1.06771)PaO2−0.0070.010−0.6820.4950.99315 (0.97372, 1.01297)PaCO2−0.030.010−2.9110.004∗∗0.97077 (0.95157, 0.99035)∗∗=0.01, ∗∗∗=<0.001.Figure 2
Multivariate logistic regression analysis with enter method.Figure 3
Multivariate logistic regression analysis with forward method.Figure 4
Multivariate logistic regression analysis with backward method.Figure 5
Multivariate logistic regression analysis with stepwise method.


## 3.3. Nomogram Model for Predicting COPD Severity

Based on NEUT and PLT, we constructed a nomogram model for predicting COPD
severity (Figure6). The AUC of the ROC
curve was 0.881, indicating good discrimination of the nomogram model (Figure
7(a)). Besides, the predicted
probability was close to the observed probability in the calibration plot,
suggesting good calibration of the nomogram model (Figure 7(b)). Furthermore, the DCA curve implied good clinical
utility of the nomogram model (Figure 7(c)). For internal validation, 5-fold cross-validation was applied,
and the mean AUC of the training sets was 0.87492 (Table 3).Figure 6
Nomogram model based on independent predictive factors for COPD
severity.Figure 7
The performance of the nomogram model. (a) ROC curve. (b) Calibration
curve. (c) DCA curve.
(a)(b)(c)Table 3
5-fold cross-validation assessing the prediction capability of our
model.
FoldCox-SnellR2NagelkerkeR2AccuracyPrecisionRecallF_measureAUC10.837540.841520.896550.894740.944440.918920.9494920.892340.895800.785710.800000.666670.727270.8177130.902700.906080.750000.846150.687500.758620.8125040.862020.865320.857140.909090.769230.833330.8974450.893510.896870.821430.812500.866670.838710.89744Mean0.877620.881120.822170.852500.786900.815370.87492


## 4. Discussion

There is some difficulty in completing normal lung function tests for severe COPD
patients, and there is significant heterogeneity in clinical manifestations and
disease progression between patients with different severity levels, so it is
important to classify COPD patients and target therapy. Machine learning provides a
powerful tool for the classification and prediction of severity in COPD patients. In
this study, we identified 2 independent risk factors for severe COPD, including NEUT
and PLT, by univariate and multifactorial logistic regression. Based on these, the
nomogram model had good performance.Although several biochemical markers have been investigated as predictors of COPD
outcome, their measurement is usually time- and resource-intensive [18]. Relatively simple biomarkers of
inflammation calculated from routine complete blood count tests may also predict
COPD progression and outcome [19]. Chronic
inflammation is an important pathogenesis of COPD, involving a complex interaction
of various immune-related cells (including neutrophils and lymphocytes), which may
lead to persistent airway damage and lung parenchymal destruction, which in turn
decreases lung function and immune function. In the long run, COPD patients are
prone to acute exacerbations of their disease due to various external triggers. NEUT
is a risk indicator for mortality in COPD patients [20]. A variety of activated immune cells, mainly NEUs, result in the
release of reactive oxygen species, causing a cascade of inflammatory responses
[21]. In addition, activated neutrophils
can produce not only other important inflammatory mediators such as proteases,
matrix metalloproteinases, and myeloperoxidase [22], leading to lung parenchymal destruction and emphysematous changes,
but also cytokines, enzymes, adhesion molecules, and growth factors, contributing to
the recruitment of inflammatory cells to the airways [23]. The abovementioned pathological process leads to an
increased local and systemic inflammatory response, which aggravates lung tissue and
vascular damage and even induces respiratory failure in severe cases. Besides, PLT
is reported as a diagnostic marker for the development of COPD [24]. In this study, we found that the NEUT and
PLT levels gradually increase as COPD disease worsens and are risk factors for COPD
severity.The present study has some limitations. First, this study only analyzed the
relationship between general clinical data, routine blood indicators,
PaO2, PaCO2, and COPD severity; further analysis of the
relationship between other test indicators and COPD severity is still needed.
Second, this study is a single-center retrospective study, and the results can only
indicate that high NEUTs and PLTs are risk factors for severe COPD. A more extensive
prospective, multicenter clinical trial with a more detailed stratification of the
study population is necessary to further confirm the value of NEUTs and PLTs in
COPD.In summary, both NEUT and PLT are independent risk factors for severe COPD, and their
combined application has a high predictive value for COPD severity.



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