Predicting Acute Onset of Heart Failure Complicating Acute Coronary Syndrome: An Explainable Machine Learning Approach.
Hao Ren, Yu Sun, Chenyu Xu, Ming Fang, Zhongzhi Xu, Fengshi Jing, Weilan Wang, Gary Tse, Qingpeng Zhang, Weibin Cheng, Wen Jin
Author Information
Hao Ren: Institute for Healthcare Artificial Intelligence, Guangdong Second Provincial General Hospital, Guangzhou, China.
Yu Sun: Department of Cardiac Intensive Care Unit, Cardiovascular Hospital, Guangdong Second Provincial General Hospital, Guangzhou, China.
Chenyu Xu: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China.
Ming Fang: Department of Cardiac Intensive Care Unit, Cardiovascular Hospital, Guangdong Second Provincial General Hospital, Guangzhou, China.
Zhongzhi Xu: School of Public Health, Sun Yat-Sen University, Guangzhou, China.
Fengshi Jing: Institute for Healthcare Artificial Intelligence, Guangdong Second Provincial General Hospital, Guangzhou, China; UNC Project-China, UNC Global, School of Medicine, University of North Carolina at Chapel Hill, NC.
Weilan Wang: School of Data Science, City University of Hong Kong, Hong Kong SAR, China.
Gary Tse: Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin, China; Kent and Medway Medical School, Canterbury, Kent, UK.
Qingpeng Zhang: School of Data Science, City University of Hong Kong, Hong Kong SAR, China.
Weibin Cheng: Institute for Healthcare Artificial Intelligence, Guangdong Second Provincial General Hospital, Guangzhou, China; School of Data Science, City University of Hong Kong, Hong Kong SAR, China. Electronic address: chwb817@gmail.com.
Wen Jin: Department of Cardiac Intensive Care Unit, Cardiovascular Hospital, Guangdong Second Provincial General Hospital, Guangzhou, China. Electronic address: jinw@gd2h.org.cn.
Patients with acute coronary syndrome (ACS) are at high risk of heart failure (HF). Early prediction and management of HF among ACS patients are essential to provide timely and cost-effective care. The aim of this study is to train and evaluate a machine learning model to predict the acute onset of HF subsequent to ACS. A total of 1,028 patients with ACS admitted to Guangdong Second Provincial General Hospital between October 2019 and May 2022 were included in this study. 128 clinical features were ranked using Shapley additive exPlanations (SHAP) values and the top 20% of features were selected for building a balanced random forest (BRF) model. We compared the discriminatory capability of BRF with linear logistic regression (LLR). In the hold-out test set, the BRF model predicted subsequent HF with areas under the curve (AUC) of 0.76 (95% CI: 0.75-0.77), sensitivity of 0.97 (95% CI: 0.96-0.97), positive predictive value (PPV) of 0.73 (95% CI: 0.72-0.74), negative predictive value (NPV) of 0.63 (95% CI: 0.60-0.66), and accuracy of 0.73 (95% CI: 0.72-0.73), respectively. BRF outperforms linear logistic regression by 15.6% in AUC, 3.0% in sensitivity, and 60.8% in NPV. End-to-end machine learning approaches can predict the acute onset of HF following ACS with high prediction accuracy. This proof-of-concept study has the potential to substantially advance the management of ACS patients by utilizing the machine learning model as a triage tool to automatically identify clinically significant patients allowing for prioritization of interventions.
