Xu Zhaohui, Hu Yinqin, Shao Xinyi, Shi Tianyun, Yang Jiahui, Wan Qiqi, Liu Yongming
Department of Cardiovascular Disease, ShuGuang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China,
Department of Cardiovascular Disease, ShuGuang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Cardiology. 2025;150(1):79-97. doi: 10.1159/000538639. Epub 2024 Apr 22.
Heart failure (HF) is a major global public health concern. The application of machine learning (ML) to identify individuals at high risk and enable early intervention is a promising approach for improving HF prognosis. We aim to systematically evaluate the performance and value of ML models for predicting HF prognosis.
PubMed, Web of Science, Scopus, and Embase online databases were searched up to April 30, 2023, to identify studies on the use of ML models to predict HF prognosis. HF prognosis primarily encompasses readmission and mortality. The meta-analysis was conducted by MedCalc software. Subgroup analyses include grouping based on types of ML models, time intervals, sample sizes, the number of predictive variables, validation methods, whether to conduct hyperparameter optimization and calibration, data set partitioning methods.
A total of 31 studies were included. The most common ML models were random forest, boosting, support vector machine, neural network. The area under the receiver operating characteristic curve (AUC) for predicting HF readmission was 0.675 (95% CI: 0.651-0.699, p < 0.001), and the AUC for predicting HF mortality was 0.790 (95% CI: 0.765-0.816, p < 0.001). Subgroup analyses revealed that models with the prediction time interval of 1 year, sample sizes ≥10,000, the number of predictive variables ≥100, external validation, hyperparameter tuning, calibration adjustment, and data set partitioning using 10-fold cross-validation exhibited favorable performance within their respective subgroups.
The performance of ML models in predicting HF readmission is relatively poor, while its performance in predicting HF mortality is moderate. The quality of the relevant studies is generally low, it is essential to enhance the predictive capabilities of ML models through targeted improvements in practical applications.
心力衰竭（HF）是一个重大的全球公共卫生问题。应用机器学习（ML）来识别高危个体并实现早期干预是改善HF预后的一种有前景的方法。我们旨在系统评估ML模型预测HF预后的性能和价值。
检索了截至2023年4月30日的PubMed、Web of Science、Scopus和Embase在线数据库，以识别关于使用ML模型预测HF预后的研究。HF预后主要包括再入院和死亡率。使用MedCalc软件进行荟萃分析。亚组分析包括基于ML模型类型、时间间隔、样本量、预测变量数量、验证方法、是否进行超参数优化和校准、数据集划分方法进行分组。
共纳入31项研究。最常见的ML模型是随机森林、提升算法、支持向量机、神经网络。预测HF再入院的受试者工作特征曲线下面积（AUC）为0.675（95%CI：0.651 - 0.699，p < 0.001），预测HF死亡率的AUC为0.790（95%CI：0.765 - 0.816，p < 0.001）。亚组分析显示，预测时间间隔为1年、样本量≥10,000、预测变量数量≥100、外部验证、超参数调整、校准调整以及使用10折交叉验证进行数据集划分的模型在各自亚组中表现出良好的性能。
ML模型预测HF再入院的性能相对较差，而其预测HF死亡率的性能中等。相关研究的质量普遍较低，有必要通过在实际应用中的针对性改进来提高ML模型的预测能力。
