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Cited by 177Machine Learning in HealthcareArtificial Intelligence in HealthcareHealth Promotion and Cardiovascular Prevention

Machine learning based prediction models for cardiovascular disease risk using electronic health records data: systematic review and meta-analysis

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Liu, Tianyi, Krentz, Andrew, Lu, Lei, Curcin, Vasa·2025·European Heart Journal - Digital Health
DOI 10.1093/ehjdh/ztae080PMC PMC11750195
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Abstract

Cardiovascular disease (CVD) remains a major cause of mortality in the UK, prompting the need for improved risk predictive models for primary prevention. Machine learning (ML) models utilizing electronic health records (EHRs) offer potential enhancements over traditional risk scores like QRISK3 and ASCVD. To systematically evaluate and compare the efficacy of ML models against conventional CVD risk prediction algorithms using EHR data for medium to long-term (5–10 years) CVD risk prediction. A systematic review and random-effect meta-analysis were conducted according to preferred reporting items for systematic reviews and meta-analyses guidelines, assessing studies from 2010 to 2024. We retrieved 32 ML models and 26 conventional statistical models from 20 selected studies, focusing on performance metrics such as area under the curve (AUC) and heterogeneity across models. ML models, particularly random forest and deep learning, demonstrated superior performance, with the highest recorded pooled AUCs of 0.865 (95% CI: 0.812–0.917) and 0.847 (95% CI: 0.766–0.927), respectively. These significantly outperformed the conventional risk score of 0.765 (95% CI: 0.734–0.796). However, significant heterogeneity (I² > 99%) and potential publication bias were noted across the studies. While ML models show enhanced calibration for CVD risk, substantial variability and methodological concerns limit their current clinical applicability. Future research should address these issues by enhancing methodological transparency and standardization to improve the reliability and utility of these models in clinical settings. This study highlights the advanced capabilities of ML models in CVD risk prediction and emphasizes the need for rigorous validation to facilitate their integration into clinical practice.

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  • A recent meta-analysis reported a pooled AUC of 0.753 across AI-based cardiovascular prediction models.

    “Neural networks and gradient boosting approaches have shown promising performance in several cohorts, and a recent meta-analysis reported a pooled AUC of 0.753 across AI-based cardiovascular prediction models (8).”

    From AI-driven cardiovascular risk prediction in patients with diabetes: bridging algorithmic innovation to equitable clinical application (2026) · cites this paper as [8]

    The Results > Meta-analysis section (Table 5; page not provided) reports a pooled AUC of 0.815 for the 32 machine-learning models, not 0.753. It reports 0.765 for conventional models. (LLM, conf=0.90)

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  • Neural networks and gradient boosting machines exhibit superior discriminative ability over traditional methods in predicting cardiovascular complications in diabetes.

    “Numerous studies have demonstrated that algorithms such as neural networks and gradient boosting machines exhibit superior discriminative ability over traditional methods in predicting cardiovascular complications in diabetes (6–8).”

    From AI-driven cardiovascular risk prediction in patients with diabetes: bridging algorithmic innovation to equitable clinical application (2026) · cites this paper as [8]

    The Results (Characteristics of included studies; page not specified) report that deep-learning models, which include neural networks, and gradient-boosting models were often the best-performing models; the meta-analysis also finds better discrimination for ML models overall than conventional baselines. But the Methods (Eligibility criteria; page not specified) explicitly exclude studies focused on patients with specific high-risk conditions such as diabetes. The review therefore does not establish this advantage for predicting cardiovascular complications in diabetes. (LLM, conf=0.92)

    Experimental — accuracy not yet measured

  • Machine learning models can improve discrimination or reclassification in derivation datasets, especially when longitudinal or high-dimensional EHR features are available.

    “Rather than focusing on isolated “best-case” results, the more consistent message across the literature is that machine learning models can improve discrimination or reclassification in derivation datasets, especially when longitudinal or high-dimensional EHR features are available (8, 11–13).”

    From AI-driven cardiovascular risk prediction in patients with diabetes: bridging algorithmic innovation to equitable clinical application (2026) · cites this paper as [8]

    The Meta-analysis reports higher pooled discrimination for random forest and deep learning models than for conventional risk scores, and the Characteristics of included studies says most studies used internal training/test splits or cross-validation. That section notes that four studies reported net reclassification improvement, but does not establish that reclassification improved; the paper also does not show that gains were especially pronounced with longitudinal or high-dimensional EHR features. Page numbers are not available in our copy. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • Even models that perform exceptionally well on development datasets often show substantially lower or uncertain performance in external settings.

    “As a consequence, even models that perform exceptionally well on development datasets often show substantially lower or uncertain performance in external settings, highlighting a critical limitation in generalizability (7, 8).”

    From AI-driven cardiovascular risk prediction in patients with diabetes: bridging algorithmic innovation to equitable clinical application (2026) · cites this paper as [8]

    The Results (Characteristics of included studies; page unavailable) report that only three studies used spatial external validation, leaving performance in external settings uncertain. The Meta-analysis reports strong pooled discrimination, but our copy does not establish that models performing well during development often perform substantially worse externally. (LLM, conf=0.83)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Liu, T., Krentz, A., Lu, L., & Curcin, V.
Affiliations
  • King's College London (GB)
  • School of Life Course & Population Sciences, King's College London , SE1 1UL London ,
Corresponding author
Liu, Tianyi
ORCID
Venue
European Heart Journal - Digital Health
Publisher
Oxford University Press (OUP)
Published
2024-10-27
Volume
6
Issue
1
Pages
7-22
ISSN
2634-3916 (ISSN-L)
DOI
10.1093/ehjdh/ztae080
PMC
PMC11750195
OpenAlex
W4403799240
Cited by
177
References
7
Citations by year
2026: 113 · 2025: 64
FWCI
6.08
Type
journal article
Language
en
Open access
Licence
Locations
  • European Heart Journal - Digital Health (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Research Portal (King's College London) (submittedVersion, cc-by)
Funders
  • King’s Health Partners Digital Health Hub (EP/X030628/1)
  • NIHR GSTT BRC and Metadvice
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Health Promotion and Cardiovascular Prevention
Keywords
cardiovascular risk prediction, machine learning, electronic health records, random forest, deep learning, QRISK3, ASCVD risk score, primary prevention, model calibration, prediction model validation, area under the curve

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