In European heart journal. Quality of care & clinical outcomes
BACKGROUND : Cardiovascular disease (CVD) risk prediction is important in guiding the intensity of therapy in CVD prevention. Whilst current risk prediction algorithms use traditional statistical approaches, machine learning (ML) presents an alternative method that may improve risk prediction accuracy. This systematic review and meta-analysis aimed to investigate whether ML algorithms demonstrate greater performance compared to traditional risk scores in CVD risk prognostication.
METHODS : MEDLINE, EMBASE, CENTRAL and SCOPUS Web of Science Core collection were searched for studies comparing ML models to traditional risk scores for CV risk prediction between the years 2000 and 2021. We included studies which assessed both ML and traditional risk scores in adult (>18 years old) primary prevention populations. We assessed risk of bias using the Prediction model Risk of Bias Assessment Tool (PROBAST) tool. Only studies which provided a measure of discrimination (i.e. C-statistics with 95% confidence intervals) were included in the meta-analysis.
RESULTS : Sixteen studies were included in the review and meta-analysis (3 302 515 individuals). All study designs were retrospective cohort studies. Three of 16 studies externally validated their models, and 11 reported calibration metrics. Eleven studies demonstrated a high risk of bias. The summary c-statistics (95% CI) of the top performing ML models and traditional risk scores were 0.773 (95%CI: 0.740-0.806) and 0.759 (95%CI: 0.726-0.792) respectively. The difference in c-statistic was 0.0139 (95%CI 0.0139-0.140), P < 0.0001.
CONCLUSION : ML models outperformed traditional risk scores in discrimination of CVD risk prognostication. Integration of ML algorithms into electronic healthcare systems in primary care could improve identification of patients at high risk of subsequent CV events and hence increase opportunities for CVD prevention. It is uncertain whether they can be implemented in clinical settings. Future implementation research is needed to examine how ML models may be utilised for primary prevention.This review was registered with PROSPERO (CRD42020220811).
Liu Weber, Laranjo Liliana, Klimis Harry, Chiang Jason, Yue Jason, Marschner Simone, Quiroz Juan C, Jorm Louisa, Chow Clara K
2023-Mar-03
Cardiovascular disease risk prediction, Machine learning, Risk prediction algorithms
