In-hospital mortality risk prediction models for patients with acute coronary syndrome: a systematic review and...

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.

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.

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.

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.

PROSPERO CRD42024567755.