Predicting Acute Onset of Heart Failure Complicating Acute Coronary Syndrome: An Explainable Machine Learning Approach

医学 急性冠脉综合征 逻辑回归 心力衰竭 内科学 预测值 心脏病学 曲线下面积 置信区间 机器学习 接收机工作特性 重症监护医学 心肌梗塞 计算机科学
作者
Hao Ren,Yu Sun,Chenyu Xu,Ming Fang,Zhongzhi Xu,Fengshi Jing,Weilan Wang,Gary Tse,Qingpeng Zhang,Weibin Cheng,Wen Jin
出处
期刊:Current Problems in Cardiology [Elsevier]
卷期号:48 (2): 101480-101480 被引量:14
标识
DOI:10.1016/j.cpcardiol.2022.101480
摘要

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.
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