Risk research of acute exacerbation in patients with chronic obstructive pulmonary disease based on interpretable machine learning model

doi:10.3877/cma.j.issn.1674-6902.2025.03.007

Abstract

Abstract:

Objective

To explore the risk of acute exacerbation in patients with chronic obstructive pulmonary disease （COPD） based on interpretable machine learning model.

Methods

A total of 80 patients with COPD in stable phase admitted to our hospital from January 2021 to December 2024 were selected. Patients were divided into a observation group 23 cases and an control group 57 cases based on whether they experienced acute exacerbations. LASSO regression was used to screen for the best predictors of acute exacerbations. Four machine learning algorithms-logistic regression （logistic regression，LR），K-nearest neighbors classification（K-Nearest Neighbor Classification，KNN），and extreme gradient boosting （eXtreme Gradient Boosting，xGBoost）-were employed to develop predictive models and evaluate their performance. The SHAP method was used to analyze the interpretability of the optimal model and record the prognosis of patients.

Results

Univariate analysis showed that there were statistically significant differences between the observation group and the control group in age，body mass index，COPD course，smoking，FEV₁/FVC，FEV1%pred，procalcitonin，fibrinogen，heparin binding protein，and IL-6 （P＜0.05）. The regression results show that the risk factors affecting acute exacerbations in COPD patients include IL-6，age，heparin-binding protein，and body mass index. Among the four ML models constructed based on these four characteristics，Model AUC of xgboost model was the highest at 0.979，with an accuracy rate of 91.70% and a precision rate of 98.00%. The SHAP results indicate that the top four contributing features are IL-6，age，heparin-binding protein，and body mass index. During the follow-up period，68（85.00%） of the 80 patients survived and 12 died（15.00%）.

Conclusion

This study developed an efficient and interpretable machine learning model to predict acute exacerbation of COPD patients，which is helpful for clinical identification of high-risk patients with acute exacerbation of COPD.

Key words: Acute exacerbation chronic obstructive pulmonary disease, Machine learning, Risk analysis

Yueqiu Shen, Menglin Cao, Meijie Xu, Mindan Wu, Kaiyi Wu, Linjuan Lu. Risk research of acute exacerbation in patients with chronic obstructive pulmonary disease based on interpretable machine learning model[J]. Chinese Journal of Lung Diseases(Electronic Edition), 2025, 18(03): 380-384.

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https://zhfbjbzz.cma-cmc.com.cn/EN/Y2025/V18/I03/380

Figures/Tables 3

References 25

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临床资料	观察组 (n=23)	对照组 (n=57)	t/χ²	P 值
收缩压 [mmHg,(x̄ ± s)]	128.65±10.98	127.98±11.02	0.247	0.806
舒张压 [mmHg,(x̄ ± s)]	75.42±8.69	74.93±8.94	0.226	0.822
随机血糖 [mmol/L,(x̄ ± s)]	6.10±1.57	6.09±1.63	0.026	0.980
胸部 CT 特征 [n(%)]
肺气肿	4(17.39)	8(14.04)	0.145	0.704
支气管管壁增厚	6(26.09)	12(21.05)	0.238	0.626
FEV₁/FVC [%,(x̄ ± s)]	50.65±10.42	56.26±10.33	2.185	0.035
FEV1%pred	54.67±7.98	59.37±8.66	2.326	0.025
白细胞 [×10⁹/L,(x̄ ± s)]	6.64±2.18	5.98±1.64	1.310	0.200
血红蛋白 [g/L,(x̄ ± s)]	128.65±22.65	130.98±24.67	0.406	0.687
血小板 [×10⁹/L,(x̄ ± s)]	184.26±35.26	186.19±34.69	0.223	0.825
降钙素原 [ng/ml,(x̄ ± s)]	0.78±0.21	0.64±0.19	2.772	0.009
纤维蛋白原 [g/L,(x̄ ± s)]	5.26±1.48	4.33±1.33	2.617	0.013
C 反应蛋白 [mg/L,(x̄ ± s)]	10.89±4.12	9.64±2.14	1.382	0.179
肝素结合蛋白 [ng/ml,(x̄ ± s)]	85.13±24.16	70.26±24.67	2.476	0.017
IL-6[ng/ml,(x̄ ± s)]	34.17±4.73	30.23±2.48	3.790	0.001
PaO₂ [mmHg,(x̄ ± s)]	87.26±20.35	85.97±19.78	0.259	0.797
PaCO₂ [mmHg,(x̄ ± s)]	37.26±4.68	35.98±6.88	0.959	0.342

临床资料	观察组 (n=23)	对照组 (n=57)	t/χ²	P 值
收缩压 [mmHg,(x̄ ± s)]	128.65±10.98	127.98±11.02	0.247	0.806
舒张压 [mmHg,(x̄ ± s)]	75.42±8.69	74.93±8.94	0.226	0.822
随机血糖 [mmol/L,(x̄ ± s)]	6.10±1.57	6.09±1.63	0.026	0.980
胸部 CT 特征 [n(%)]
肺气肿	4(17.39)	8(14.04)	0.145	0.704
支气管管壁增厚	6(26.09)	12(21.05)	0.238	0.626
FEV₁/FVC [%,(x̄ ± s)]	50.65±10.42	56.26±10.33	2.185	0.035
FEV1%pred	54.67±7.98	59.37±8.66	2.326	0.025
白细胞 [×10⁹/L,(x̄ ± s)]	6.64±2.18	5.98±1.64	1.310	0.200
血红蛋白 [g/L,(x̄ ± s)]	128.65±22.65	130.98±24.67	0.406	0.687
血小板 [×10⁹/L,(x̄ ± s)]	184.26±35.26	186.19±34.69	0.223	0.825
降钙素原 [ng/ml,(x̄ ± s)]	0.78±0.21	0.64±0.19	2.772	0.009
纤维蛋白原 [g/L,(x̄ ± s)]	5.26±1.48	4.33±1.33	2.617	0.013
C 反应蛋白 [mg/L,(x̄ ± s)]	10.89±4.12	9.64±2.14	1.382	0.179
肝素结合蛋白 [ng/ml,(x̄ ± s)]	85.13±24.16	70.26±24.67	2.476	0.017
IL-6[ng/ml,(x̄ ± s)]	34.17±4.73	30.23±2.48	3.790	0.001
PaO₂ [mmHg,(x̄ ± s)]	87.26±20.35	85.97±19.78	0.259	0.797
PaCO₂ [mmHg,(x̄ ± s)]	37.26±4.68	35.98±6.88	0.959	0.342

模型	AUC	准确率	召回率	精确率	F1 分数
LR	0.854	76.80	76.80	76.60	0.765
SVM	0.745	75.00	75.00	76.00	0.737
KNN	0.889	82.10	82.10	83.10	0.815
XGboost	0.979	91.70	91.70	98.00	0.957

模型	AUC	准确率	召回率	精确率	F1 分数
LR	0.854	76.80	76.80	76.60	0.765
SVM	0.745	75.00	75.00	76.00	0.737
KNN	0.889	82.10	82.10	83.10	0.815
XGboost	0.979	91.70	91.70	98.00	0.957

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