SynthesisFrontiers in cardiovascular medicine2022
Comparing Machine Learning Models and Statistical Models for Predicting Heart Failure Events: A Systematic Review and Meta-Analysis.
Synthesis in Frontiers in cardiovascular medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 6 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.
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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.
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Who cites it
28 citing papers in PubMed, 6 syntheses or guidelines pooled it, 47 citations in OpenAlex.
- Prognostic models in populations with heart failure: a systematic review and meta-analysis.Systematic reviews · 2026Pooled it
- Comparing machine learning models and traditional approaches for predicting obstructive coronary artery disease: a systematic review and meta-analysis.Frontiers in cardiovascular medicine · 2026Pooled it
- Comparison of machine learning methods versus traditional Cox regression for survival prediction in cancer using real-world data: a systematic literature review and meta-analysis.BMC medical research methodology · 2025Pooled it
- Machine Learning Algorithms Versus Classical Regression Models in Pre-Eclampsia Prediction: A Systematic Review.Current hypertension reports · 2024Pooled it
- Using machine learning methods to predict all-cause somatic hospitalizations in adults: A systematic review.PloS one · 2024Pooled it
- An Assessment of the Predictive Performance of Current Machine Learning-Based Breast Cancer Risk Prediction Models: Systematic Review.JMIR public health and surveillance · 2022Pooled it
- Performance of Statistical and Machine Learning Risk Prediction Models for Advanced Breast Cancer.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2026Article
- Article
- Diagnostic and prognostic biomarkers in heart failure and myeloproliferative neoplasms: clinical applications and risk scores - a narrative review.Cardio-oncology (London, England) · 2026Review
- Machine learning model for predicting improvement in left ventricular systolic function in patients with heart failure and reduced ejection fraction.International journal of cardiology. Heart & vasculature · 2026Article
- Development and Internal Validation of a Machine Learning Model for Predicting Long-Term Opioid Therapy After Hip Fracture Surgery in Older, Opioid-Naïve Adults.European journal of pain (London, England) · 2026Article
- Development and external validation of an explainable machine learning model for in-hospital mortality risk stratification in intensive care unit patients with heart failure.Frontiers in cardiovascular medicine · 2026Article
- A dual-validated machine learning model for predicting 3-year mortality in atypical pulmonary carcinoid, a rare neuroendocrine tumor.Frontiers in endocrinology · 2026Article
- High-dimensional machine learning models for prediction of heart failure in more than 400 000 men and women from the UK Biobank.European heart journal. Digital health · 2025Article
- Predicting In-Hospital Cardiac Arrest Using Machine Learning Models: Protocol for a Scoping Review.JMIR research protocols · 2025Article
- Evaluation of changes in prediction modelling in biomedicine using systematic reviews.BMC medical research methodology · 2025Article
- Predicting death or readmission following heart failure hospitalisation: the VancOuver CoastAL Acute Heart Failure (VOCAL-AHF) registry.Open heart · 2025Article
- Risk classification for long-term mortality among patients with acute heart failure: China PEACE 4YMortality.ESC heart failure · 2025Article
- Elucidating predictors of preoperative acute heart failure in older people with hip fractures through machine learning and SHAP analysis: a retrospective cohort study.BMC geriatrics · 2025Article
- Artificial Intelligence in Emergency Medicine: A Primer for the Nonexpert.Journal of the American College of Emergency Physicians open · 2025Article
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: To compare the performance, clinical feasibility, and reliability of statistical and machine learning (ML) models in predicting heart failure (HF) events. Background: Although ML models have been proposed to revolutionize medicine, their promise in predicting HF events has not been investigated in detail. Methods: A systematic search was performed on Medline, Web of Science, and IEEE Xplore for studies published between January 1, 2011 to July 14, 2021 that developed or validated at least one statistical or ML model that could predict all-cause mortality or all-cause readmission of HF patients. Prediction Model Risk of Bias Assessment Tool was used to assess the risk of bias, and random effect model was used to evaluate the pooled c-statistics of included models. Result: Two-hundred and two statistical model studies and 78 ML model studies were included from the retrieved papers. The pooled c-index of statistical models in predicting all-cause mortality, ML models in predicting all-cause mortality, statistical models in predicting all-cause readmission, ML models in predicting all-cause readmission were 0.733 (95% confidence interval 0.724-0.742), 0.777 (0.752-0.803), 0.678 (0.651-0.706), and 0.660 (0.633-0.686), respectively, indicating that ML models did not show consistent superiority compared to statistical models. The head-to-head comparison revealed similar results. Meanwhile, the immoderate use of predictors limited the feasibility of ML models. The risk of bias analysis indicated that ML models' technical pitfalls were more serious than statistical models'. Furthermore, the efficacy of ML models among different HF subgroups is still unclear. Conclusions: ML models did not achieve a significant advantage in predicting events, and their clinical feasibility and reliability were worse.
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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.