The predictive value of machine learning for mortality risk in patients with acute coronary syndromes: a systematic review and meta-analysis

European Journal of Medical Research

Research

Xiaoxiao Zhang¹  Luxin Xu¹  Jia Liu¹  Huanlin Wu¹  Xi Wang¹  Peng Ren² 

[1] Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China;School of Life Science and Engineering, Southwest University of Science and Technology, Mianyang, China;

关键词: Acute coronary syndromes;    Mortality;    Predictive models;    Machine learning;    Meta-analysis;    Systematic review;   

DOI  :  10.1186/s40001-023-01027-4

 received in 2023-01-12, accepted in 2023-01-20,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundAcute coronary syndromes (ACS) are the leading cause of global death. Optimizing mortality risk prediction and early identification of high-risk patients is essential for developing targeted prevention strategies. Many researchers have built machine learning (ML) models to predict the mortality risk in ACS patients. Our meta-analysis aimed to evaluate the predictive value of various ML models in predicting death in ACS patients at different times.MethodsPubMed, Embase, Web of Science, and Cochrane Library were searched systematically from database establishment to March 12, 2022 for studies developing or validating at least one ML predictive model for death in ACS patients. We used PROBAST to assess the risk of bias in the reported predictive models and a random-effects model to assess the pooled C-index and accuracy of these models.ResultsFifty papers were included, involving 216 ML prediction models, 119 of which were externally validated. The combined C-index of the ML models in the validation cohort predicting the in-hospital mortality, 30-day mortality, 3- or 6-month mortality, and 1 year or above mortality in ACS patients were 0.8633 (95% CI 0.8467–0.8802), 0.8296 (95% CI 0.8134–0.8462), 0.8205 (95% CI 0.7881–0.8541), and 0.8197 (95% CI 0.8042–0.8354), respectively, with the corresponding combined accuracy of 0.8569 (95% CI 0.8411–0.8715), 0.8282 (95% CI 0.7922–0.8591), 0.7303 (95% CI 0.7184–0.7418), and 0.7837 (95% CI 0.7455–0.8175), indicating that the ML models were relatively excellent in predicting ACS mortality at different times. Furthermore, common predictors of death in ML models included age, sex, systolic blood pressure, serum creatinine, Killip class, heart rate, diastolic blood pressure, blood glucose, and hemoglobin.ConclusionsThe ML models had excellent predictive power for mortality in ACS, and the methodologies may need to be addressed before they can be used in clinical practice.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

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