Department of Laboratory Medicine, First Hospital of Jilin University, Changchun, China.
Information center, First Hospital of Jilin University, Changchun, China.
BMC Med Inform Decis Mak. 2023 Aug 24;23(1):165. doi: 10.1186/s12911-023-02240-1.
Heart failure (HF) is one of the common adverse cardiovascular events after acute myocardial infarction (AMI), but the predictive efficacy of numerous machine learning (ML) built models is unclear. This study aimed to build an optimal model to predict the occurrence of HF in AMI patients by comparing seven ML algorithms.
Cohort 1 included AMI patients from 2018 to 2019 divided into HF and control groups. All first routine test data of the study subjects were collected as the features to be selected for the model, and seven ML algorithms with screenable features were evaluated. Cohort 2 contains AMI patients from 2020 to 2021 to establish an early warning model with external validation. ROC curve and DCA curve to analyze the diagnostic efficacy and clinical benefit of the model respectively.
The best performer among the seven ML algorithms was XgBoost, and the features of XgBoost algorithm for troponin I, triglycerides, urine red blood cell count, γ-glutamyl transpeptidase, glucose, urine specific gravity, prothrombin time, prealbumin, and urea were ranked high in importance. The AUC of the HF-Lab9 prediction model built by the XgBoost algorithm was 0.966 and had good clinical benefits.
This study screened the optimal ML algorithm as XgBoost and developed the model HF-Lab9 will improve the accuracy of clinicians in assessing the occurrence of HF after AMI and provide a reference for the selection of subsequent model-building algorithms.
心力衰竭（HF）是急性心肌梗死（AMI）后常见的心血管不良事件之一，但众多机器学习（ML）构建模型的预测效果尚不清楚。本研究旨在通过比较 7 种 ML 算法，构建一个最优模型来预测 AMI 患者 HF 的发生。
队列 1 纳入了 2018 年至 2019 年的 AMI 患者，分为 HF 组和对照组。收集研究对象的所有首次常规检查数据作为特征，用于筛选模型，并评估具有筛选特征的 7 种 ML 算法。队列 2 纳入了 2020 年至 2021 年的 AMI 患者，用于建立外部验证的预警模型。分别通过 ROC 曲线和 DCA 曲线分析模型的诊断效果和临床获益。
7 种 ML 算法中表现最佳的是 XgBoost，XgBoost 算法的特征重要性排名较高的有肌钙蛋白 I、甘油三酯、尿红细胞计数、γ-谷氨酰转肽酶、血糖、尿比重、凝血酶原时间、前白蛋白和尿素。XgBoost 算法构建的 HF-Lab9 预测模型的 AUC 为 0.966，具有良好的临床获益。
本研究筛选出最优的 ML 算法为 XgBoost，并开发了模型 HF-Lab9，将提高临床医生评估 AMI 后 HF 发生的准确性，并为后续模型构建算法的选择提供参考。
