Key result
Machine learning outperforms Framingham Risk Score for CVD prediction in ~79% of studies, but lacks clear clinical impact.
Why the study?
Do machine learning algorithms improve cardiovascular disease risk prediction compared to the Framingham Risk Score in healthy adults?
Systematic Review (n=29)
Do machine learning algorithms improve cardiovascular disease risk prediction compared to the Framingham Risk Score in healthy adults?
While machine learning algorithms demonstrate the statistical capacity to improve cardiovascular risk prediction over the Framingham Risk Score, current models often lack clinical validity and may amplify overdiagnosis.
Machine learning models should not yet replace Framingham scoring in practice; leaves open the need for clinically validated outcome trials.
OBJECTIVE: To examine whether the outputs of machine learning algorithms designed to predict risk of cardiovascular disease (CVD) address known deficiencies of the Framingham Risk Score (FRS) and improve risk estimates. METHODS: For this critical review, Medline, Embase and IEEE were searched from inception to 1 January 2025. Included were studies describing machine learning algorithms designed to specifically compare output of cardiovascular risk assessment with the FRS. Commentaries, letters, unpublished work or non-peer-reviewed papers were excluded.Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, two reviewers screened titles and abstracts independently, then populated a purpose-built data extraction form. A subsequent qualitative thematic analysis focused on algorithms' strengths, added value, potential harms, unintended consequences and equity implications.The main outcome assessed was whether, among healthy adults, the algorithm improved CVD risk prediction relative to the FRS. RESULTS: Of 707 studies retrieved, 29 met inclusion criteria. 23 reported improved predictive ability relative to the FRS. Most datasets and/or medical records used included sociodemographic predictors of CVD not included among FRS inputs. Some added costly diagnostic tests like CT angiography to FRS screening indicators. When they were defined, inputs and outcomes such as hypertension or myocardial infarction did not always adhere to FRS values. Statistical significance was generally taken as a proxy for clinical significance. Some algorithms overestimated the number at risk compared with the FRS without discussing whether that larger proportion might be at risk of overdiagnosis rather than CVD, while a few decreased the proportion found to be at risk. CONCLUSIONS: Use of artificial intelligence to improve accuracy of risk assessment for CVD demonstrates the technological capacity to merge known sociodemographic predictors with biologic variables and examine non-linear interactions among these. Still needed to achieve patient benefit is clinical insight, adherence to screening principles and cost-benefit assessment of inputs selected.
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Phillips et al. (2026) conducted a systematic review in Cardiovascular disease risk prediction (n=29). Machine learning algorithms vs. Framingham Risk Score was evaluated on Improved CVD risk prediction relative to the FRS. Machine learning algorithms reported improved cardiovascular disease risk prediction relative to the Framingham Risk Score in 23 of 29 studies, though often lacking clear clinical significance.
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