Predicting acute onset of heart failure complicating acute coronary syndrome: an explainable machine learning approach

Journal article


Ren, Hao, Sun, Yu, Xu, Chenyu, Fang, Ming, Xu, Zhongzhi, Jing, Fengshi, Wang, Weilan, Tse, Gary, Zhang, Qingpeng, Cheng, Weibin and Jin, Wen 2022. Predicting acute onset of heart failure complicating acute coronary syndrome: an explainable machine learning approach. Current Problems in Cardiology. 48 (2), p. 101480. https://doi.org/S0146-2806(22)00377-2
AuthorsRen, Hao, Sun, Yu, Xu, Chenyu, Fang, Ming, Xu, Zhongzhi, Jing, Fengshi, Wang, Weilan, Tse, Gary, Zhang, Qingpeng, Cheng, Weibin and Jin, Wen
AbstractPatients 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 heart failure 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 LLR 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 heart failure 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. [Abstract copyright: Copyright © 2022. Published by Elsevier Inc.]
KeywordsPrediction; Heart failure; Acute coronary syndrome; Machine learning
Year2022
JournalCurrent Problems in Cardiology
Journal citation48 (2), p. 101480
PublisherElsevier
ISSN1535-6280
Digital Object Identifier (DOI)https://doi.org/S0146-2806(22)00377-2
https://doi.org/10.1016/j.cpcardiol.2022.101480
Official URLhttps://www.sciencedirect.com/science/article/pii/S0146280622003772
Publication dates
Online03 Nov 2022
Publication process dates
Accepted31 Oct 2022
Deposited24 Nov 2022
Output statusPublished
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