Key result
ACS in-hospital mortality models show good discrimination with AUCs up to ~0.96 despite high bias.
Why the study?
To systematically evaluate in-hospital mortality risk prediction models for patients with ACS to provide insights for their construction, application, and optimization.
Systematic Review
Effect estimate: AUC 0.79-0.96
Existing in-hospital mortality risk prediction models for ACS show good discriminative ability (AUC 0.79-0.96) but suffer from high risk of bias and methodological flaws, highlighting the need for adherence to PROBAST and TRIPOD guidelines in future model development.
High bias limits current ACS in-hospital mortality models for routine use; confirms need for PROBAST/TRIPOD-compliant development and validation.
Objective: To systematically evaluate in-hospital mortality risk prediction models for patients with acute coronary syndrome (ACS) and provide valuable insights and references for the construction, application, and optimization of these models. Methods: A comprehensive search was conducted in five databases, including CNKI, Wanfang, PubMed, Web of Science, and Embase, from inception to November 2024. Researchers screened the literature, extracted relevant data, and assessed the quality of the prediction models using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Extracted data included study design, data sources, outcome definitions, sample size, predictive factors, model development, and performance. Results: A total of 18 studies involving 44 prediction models were included. The area under the receiver operating characteristic curve (AUC) or C-index of these models ranged from 0.79 to 0.96. Overall, the included prediction models demonstrated a high risk of bias, primarily due to issues such as unreported missing data, methodological flaws in model construction, and a lack of model performance evaluation. Conclusion: The construction of in-hospital mortality risk prediction models for patients with ACS is still in the developmental stage. Future development and validation of prediction models should adhere to the PROBAST and TRIPOD guidelines to establish models with strong predictive performance and high generalizability. Systematic Review Registration: PROSPERO CRD42024567755.
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Jian et al. (2025) conducted a systematic review in Acute coronary syndrome. In-hospital mortality risk prediction models was evaluated on Model discrimination for in-hospital mortality (AUC or C-index) (AUC 0.79-0.96). In-hospital mortality risk prediction models for patients with acute coronary syndrome demonstrated good discriminative ability with AUCs ranging from 0.79 to 0.96, despite a high overall risk of bias.
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