Predictive Value of Machine Learning for Poststroke Mortality Risk: Systematic Review and Meta-Analysis

Clinical Snapshot

Key Findings

• P Value: Not reported for primary outcomes

• Effect Size: External validation C-index: 0.727 (in-hospital), 0.847 (out-of-hospital)

• Primary Outcome: C-index for mortality prediction accuracy

• Nnt Or Sensitivity: Sensitivity: 0.64 (in-hospital), 0.71 (out-of-hospital); Specificity: 0.74 (in-hospital), 0.76 (out-of-hospital)

• Confidence Interval: In-hospital: 95% CI 0.677-0.781; Out-of-hospital: 95% CI 0.808-0.887

Clinical Application

Moderate feasibility - requires external validation and consideration of local healthcare context before implementation Relevant to Australian stroke units and rehabilitation services; would need validation against Australian stroke registry data and integration with existing clinical pathways under RACGP stroke management guidelines Adult stroke patients in hospital and community settings requiring mortality risk stratification

Abstract

BACKGROUND: People with stroke face a high mortality risk, and an accurate prediction model is essential to the guidance of clinical decision-making in this population. Recently, with growing attention paid to machine learning (ML) in stroke care, some researchers have investigated the effectiveness of ML in predicting the mortality risk in stroke. However, systematic evidence is still lacking for its effectiveness. OBJECTIVE: This systematic review aims to evaluate the value of ML in predicting the stroke mortality risk. The findings are expected to offer an evidence-based basis for developing and assessing clinical risk prediction tools. METHODS: A search was made in Cochrane Library, PubMed, Embase, and Web of Science up to June 23, 2025, and studies that reported a complete performance of ML in predicting stroke mortality were included. Studies with only risk factors analyzed were excluded. The risk of bias of the included studies was assessed using PROBAST (Prediction model Risk of Bias Assessment Tool). Pooled risk ratios with 95% CIs and prediction intervals (PIs) were derived using the Hartung-Knapp-Sidik-Jonkman method under a random-effects model. Subgroup analyses were also conducted by model type, stroke type, patient source, and treatment background. Moreover, a metaregression was conducted on the C-index for out-of-hospital mortality at different time points to explore the influence of time factors on the model's predictive performance. RESULTS: Sixty-eight studies were included (23 predicting in-hospital mortality and 45 predicting out-of-hospital mortality), describing the development of 75 prediction models and 43 external validations. The follow-up period was 1 month to 15 years. For predicting in-hospital mortality, the external validation set had a pooled C-index of 0.727 (95% CI 0.677-0.781, 95% PI 0.521-1.000), with sensitivity and specificity of 0.64 (95% CI 0.57-0.70) and 0.74 (95% CI 0.70-0.77), respectively. For predicting out-of-hospital mortality, the pooled C-index was 0.847 (95% CI 0.808-0.887, 95% PI 0.750-0.956) in the external validation set, with sensitivity and specificity of 0.71 (95% CI 0.55-0.82) and 0.76 (95% CI 0.74-0.78), respectively. Comparatively, the overall pooled C-indexes were 0.788 (95% CI 0.766-0.810, 95% PI 0.621-0.999) and 0.812 (95% CI 0.798-0.826, 95% PI 0.693-0.952), respectively. The metaregression revealed a gradual decline in the predictive performance of the overall model and logistic regression model alone, whereas a random forest model maintained sustained performance. Age, National Institutes of Health Stroke Scale score, and stroke-related complications were the most frequently used variables for modeling. CONCLUSIONS: This is the first meta-analysis to demonstrate that ML-based prediction of stroke mortality is feasible. The performance of ML supports its role as an auxiliary tool for identifying high-risk populations, thereby optimizing clinical monitoring and resource allocation. However, due to substantial heterogeneity and a relatively high risk of bias in available studies, caution is warranted in real-world application. The effectiveness of ML may vary across settings, and external validation is recommended before broader implementation. TRIAL REGISTRATION: PROSPERO CRD420251086321; https://www.crd.york.ac.uk/PROSPERO/view/CRD420251086321.

References

1. 1.Chen, Y., Ou, Z., Deng, Y., Luo, A., Li, X., Yang, Y., Yang, Q., Wu, X., & Zhou, M. (2026). Predictive value of machine learning for poststroke mortality risk: Systematic review and meta-analysis. Journal of Medical Internet Research, 28(4), e83821. https://doi.org/10.2196/83821