ReviewEuropean heart journal. Digital health2025
Machine learning based prediction models for cardiovascular disease risk using electronic health records data: systematic review and meta-analysis.
Review in European heart journal. Digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07539532 (Value of Some Risk Scores in Predicting Cardiovascular Events After Gastrointestinal Surgery), which is not on this map. Cited by 63 papers, 4 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Value of Some Risk Scores in Predicting Cardiovascular Events After Gastrointestinal Surgery
Who cites it
63 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Future promise, current clinical ambiguity: a systematic review of machine learning algorithm outputs predicting risk of cardiovascular disease.Open heart · 2026Pooled it
- Quality and performance of machine learning versus logistic regression for predicting IVIG resistance in Kawasaki disease: a PROBAST+AI systematic comparison.BMC medical research methodology · 2026Pooled it
- Artificial Intelligence for Cardiovascular Risk Prediction: An Umbrella Review of Applications and Translational Challenges.Vascular health and risk management · 2026Pooled it
- Machine learning for personalized risk assessment of HIV, syphilis, gonorrhoea and chlamydia: A systematic review and meta-analysis.International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases · 2025Pooled it
- A next-generation prediction risk model for acute myocardial infarction: Derivation and validation in a multi-centre cohort.International journal of cardiology. Cardiovascular risk and prevention · 2026Article
- Precision Lipid Management in the Era of Biotechnology and Artificial Intelligence: From Gene Editing to Smart Drug Delivery.Therapeutic innovation & regulatory science · 2026Review
- Risk Stratification in Cardiovascular Medicine for Prognostic Assessment and Therapeutic Decision-Making: From Atrial Fibrillation to the Broader Disease Spectrum.Journal of arrhythmia · 2026Review
- Artificial Intelligence and Genomic Data Analysis: New Frontiers in Precision Medicine.International journal of molecular sciences · 2026Review
- Machine learning and traditional regression approaches to predict risk of cardiovascular events in patients with acute coronary syndrome.BMC medical informatics and decision making · 2026Observational
- Wearable Devices and Machine Learning in Cardiovascular Monitoring: Current Evidence and Future Directions for Precision Medicine.Journal of personalized medicine · 2026Review
- Centroid Regression for Preoperative Risk Assessment of Acute Type A Aortic Dissection Based on Multivariate Clinical Data.Journal of clinical medicine · 2026Article
- Article
- Advances in cardiac devices and bioelectronics augmented with artificial intelligence.The Journal of physiology · 2026Review
- Artificial intelligence in nephrology: predicting CKD progression and personalizing treatment.International urology and nephrology · 2026Review
- Systematic reviews of medical machine learning: limitations of pooling AUCs.European heart journal. Digital health · 2026Article
- Beyond Area Under the Receiver Operating Characteristic Curve: Evaluating Predictive Performance Metrics Under Class Imbalance in Real-World Clinical Data.JMIR formative research · 2026Article
- A machine learning approach predicts improvement of physical exercise capacity based on pulse wave analysis in coronary artery disease patients.Journal of sport and health science · 2026Article
- Article
- Machine learning-based prediction of myocardial ischemia-reperfusion injury in patients with ST-segment elevation myocardial infarction: the STEMI-MIRI prediction model.BMC cardiovascular disorders · 2026Article
- Review
3 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.