5-year all-cause mortality survival analysis and interpretable study in patients with coronary artery disease combined with chronic heart failure

ZHANG Weichang; TIAN Jing; YANG Hong; HAN Qinghua; ZHANG Yanbo

doi:10.16462/j.cnki.zhjbkz.2023.04.001

Volume 27 Issue 4

Apr. 2023

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ZHANG Weichang, TIAN Jing, YANG Hong, HAN Qinghua, ZHANG Yanbo. 5-year all-cause mortality survival analysis and interpretable study in patients with coronary artery disease combined with chronic heart failure[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2023, 27(4): 373-378. doi: 10.16462/j.cnki.zhjbkz.2023.04.001

Citation:

ZHANG Weichang, TIAN Jing, YANG Hong, HAN Qinghua, ZHANG Yanbo. 5-year all-cause mortality survival analysis and interpretable study in patients with coronary artery disease combined with chronic heart failure[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2023, 27(4): 373-378. doi: 10.16462/j.cnki.zhjbkz.2023.04.001

Citation:

ZHANG Weichang, TIAN Jing, YANG Hong, HAN Qinghua, ZHANG Yanbo. 5-year all-cause mortality survival analysis and interpretable study in patients with coronary artery disease combined with chronic heart failure[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2023, 27(4): 373-378. doi: 10.16462/j.cnki.zhjbkz.2023.04.001

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5-year all-cause mortality survival analysis and interpretable study in patients with coronary artery disease combined with chronic heart failure

doi: 10.16462/j.cnki.zhjbkz.2023.04.001

ZHANG Weichang¹,
TIAN Jing²,
YANG Hong¹,
HAN Qinghua^{2
,
,},
ZHANG Yanbo^{1, 2
,
,}

1.
Department of Biostatistics, School of Public Health, Shanxi Medical University, Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, Taiyuan 030000, China
2.
Department of Cardiology, First Hospital of Shanxi Medical University, Taiyuan 030000, China
3.
School of Health Services and Management, Shanxi University of Chinese Medicine, Jinzhong 030619, China

Funds:

National Natural Science Foundation of China 81872714

National Natural Science Foundation of China 82173631

Shanxi Provincial Key Laboratory 201805D111006

More Information

Corresponding author: HAN Qinghua, E-mail: syhqh@sohu.com; ZHANG Yanbo, E-mail: sxmuzyb@126.com
Received Date: 2022-05-06
Rev Recd Date: 2023-01-09

Available Online: 2023-04-28

Publish Date: 2023-04-10

Abstract

Abstract

Objective To investigate the risk factors for all-cause mortality in patients with coronary heart disease combined with chronic heart failure by using machine learning algorithms and shapley additive explanations (SHAP). Methods A total of 2 648 patients diagnosed with coronary heart disease combined with chronic heart failure in two tertiary hospitals in Shanxi Province were selected for the study. The Cox, random survival forests (RSF), and XGBoost models were constructed using the variables screened by XGBoost; SHAP was applied to analyze the model interpretability. Results The prediction model created using XGBoost had the highest predictive performance with a concordance index (C-index) of 0.902 (0.900-0.915). The model showed that higher age, NTproBNP, systolic blood pressure, and creatinine were associated with a higher risk of death. Diabetes, central nervous system disorders, and statins significantly influenced patient prognosis. Conclusions The survival analysis prediction model constructed by XGBoost can more accurately assess the poor prognosis of patients and, in combination with SHAP, can provide a clear interpretation of individualized risk prediction for patients. It is helpful to assist doctors in personalizing clinical treatment for patients.
- XGBoost,
- Coronary heart disease,
- Chronic heart failure,
- Survival analysis,
- Interpretable

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References(21)

References

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