Prediction of Atrial Fibrillation in Hospitalized Elderly Patients With Coronary Heart Disease and Type 2 Diabetes Mellitus Using Machine Learning: A Multicenter Retrospective Study

Frontiers in Public Health

article

Qian Xu¹  Yan Peng⁴  Juntao Tan⁵  Wenlong Zhao¹  Meijie Yang¹  Jie Tian² 

[1] College of Medical Informatics, Chongqing Medical University;Medical Data Science Academy, Chongqing Medical University;Collection Development Department of Library, Chongqing Medical University;Department of Cardiology, University-Town Hospital of Chongqing Medical University;Operation Management Office, Affiliated Banan Hospital of Chongqing Medical University;Department of Cardiology, Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Children's Hospital of Chongqing Medical University;Chongqing Key Laboratory of Pediatrics

关键词: coronary heart disease;    type 2 diabetes mellitus;    atrial fibrillation;    machine learning;    prediction models;   

DOI  :  10.3389/fpubh.2022.842104

学科分类：社会科学、人文和艺术（综合）

来源: Frontiers

【 摘 要 】

Background The objective of this study was to use machine learning algorithms to construct predictive models for atrial fibrillation (AF) in elderly patients with coronary heart disease (CHD) and type 2 diabetes mellitus (T2DM). Methods The diagnosis and treatment data of elderly patients with CHD and T2DM, who were treated in four tertiary hospitals in Chongqing, China from 2015 to 2021, were collected. Five machine learning algorithms: logistic regression, logistic regression+least absolute shrinkage and selection operator, classified regression tree (CART), random forest (RF) and extreme gradient lifting (XGBoost) were used to construct the prediction models. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy were used as the comparison measures between different models. Results A total of 3,858 elderly patients with CHD and T2DM were included. In the internal validation cohort, XGBoost had the highest AUC (0.743) and sensitivity (0.833), and RF had the highest specificity (0.753) and accuracy (0.735). In the external verification, RF had the highest AUC (0.726) and sensitivity (0.686), and CART had the highest specificity (0.925) and accuracy (0.841). Total bilirubin, triglycerides and uric acid were the three most important predictors of AF. Conclusion The risk prediction models of AF in elderly patients with CHD and T2DM based on machine learning algorithms had high diagnostic value. The prediction models constructed by RF and XGBoost were more effective. The results of this study can provide reference for the clinical prevention and treatment of AF.

【 授权许可】

CC BY