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Meta-Analysis
. 2025 Jan;48(1):e70071.
doi: 10.1002/clc.70071.

Predictive Value of Machine Learning for the Risk of In-Hospital Death in Patients With Heart Failure: A Systematic Review and Meta-Analysis

Liyuan Yan  1 , Jinlong Zhang  2 , Le Chen  1 , Zongcheng Zhu  3 , Xiaodong Sheng  1 , Guanqun Zheng  1 , Jiamin Yuan  3
Affiliations
Meta-Analysis

Predictive Value of Machine Learning for the Risk of In-Hospital Death in Patients With Heart Failure: A Systematic Review and Meta-Analysis

Liyuan Yan et al. Clin Cardiol. 2025 Jan.

Abstract

Background: The efficiency of machine learning (ML) based predictive models in predicting in-hospital mortality for heart failure (HF) patients is a topic of debate. In this context, this study's objective is to conduct a meta-analysis to compare and assess existing prognostic models designed for predicting in-hospital mortality in HF patients.

Methods: A systematic search of databases was conducted, including PubMed, Embase, Web of Science, and Cochrane Library up to January 2023. To ensure comprehensiveness, we performed an additional search in June 2023. The Prediction Model Risk of Bias Assessment Tool was employed to assess the validity and reliability of ML models.

Results: Our analysis incorporated 28 studies involving a total of 106 predictive models based on 14 different ML techniques. In the training data set, these models showed a combined C-index of 0.781, sensitivity of 0.56, and specificity of 0.94. In the validation data set, the models exhibited a combined C-index of 0.758, sensitivity of 0.57, and specificity of 0.84. Logistic regression (LR) was the most frequently used ML algorithm. LR models in the training set had a combined C-index of 0.795, sensitivity of 0.63, and specificity of 0.85, and these measures for LR models in the validation set were 0.751, 0.66, and 0.79, respectively.

Conclusions: Our study indicates that although ML is increasingly being leveraged to predict in-hospital mortality for HF patients, the predictive performance remains suboptimal. Although these models have relatively high C-index and specificity, their ability to predict positive events is limited, as indicated by their low sensitivity.

Keywords: heart failure; in‐hospital death; machine learning; meta‐analysis; prediction model.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
PRISMA (Preferred Reporting Items for Systematic Reviews and Meta‐Analyses) flow diagram for study selection.
Figure 2
Figure 2
Risk of bias assessment with PROBAST (Prediction Model Risk of Bias Assessment Tool) of 28 meta‐analyzed studies containing 106 models.
See this image and copyright information in PMC

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