ArticleEuropean heart journal. Digital health2025
Machine learning approaches for risk prediction after percutaneous coronary intervention: a systematic review and meta-analysis.
Article 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. Cited by 17 papers, 4 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Machine Learning for Predicting Venous Thromboembolism After Joint Arthroplasty: Systematic Review of Clinical Applicability and Model Performance.JMIR medical informatics · 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
- Comparing the Performance of Machine Learning Models and Conventional Risk Scores for Predicting Major Adverse Cardiovascular Cerebrovascular Events After Percutaneous Coronary Intervention in Patients With Acute Myocardial Infarction: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Pooled it
- Machine Learning With Genetic and Clinical Data to Predict Ischemic Outcomes After PCI.Clinical and translational science · 2026Trial
- Development and External Validation of a Machine Learning-Based Bleeding Risk Score for Patients Undergoing Percutaneous Coronary Intervention.Circulation reports · 2026Article
- Toward Reliable Coronary Heart Disease Prediction: Integrating Multi-source Data with Ensemble Machine Learning.Journal of imaging informatics in medicine · 2026Article
- Another Score in the Flood, or a Needed Step Toward East Asian Precision?Korean circulation journal · 2026Article
- Interpretable machine learning for predicting in-hospital bleeding after PCI in patients with diabetes and acute coronary syndrome: a retrospective cohort study.BMC cardiovascular disorders · 2026Article
- Artificial intelligence in the cath lab: Bridging predictive models and real-time procedural decision support.EXCLI journal · 2026Review
- Article
- Development and validation of a machine learning predictive model for one-month post-revascularization angina in patients who had undergone PCI or CABG.Frontiers in cardiovascular medicine · 2026Article
- Predicting Left Ventricular Ejection Fraction Recovery After Percutaneous Coronary Intervention in Patients With Chronic Coronary Syndrome by Using Interpretable Machine Learning Models: Retrospective Study.JMIR medical informatics · 2025Article
- Multimodal machine learning for 5-year mortality prediction after percutaneous coronary intervention.Scientific reports · 2025Article
- Machine Learning Approach on Predictive Model Establishment for In-Hospital Mortality in Acute Myocardial Infarction Patients Post-Percutaneous Coronary Intervention: Solutions for Databases With Dimensionality Reduction and Class Imbalance.Reviews in cardiovascular medicine · 2025Article
- From marginal gains to clinical utility: machine learning-based percutaneous coronary intervention risk prediction models.European heart journal. Digital health · 2025Article
- Federated multimodal AI for precision-equitable diabetes care.Frontiers in digital health · 2025Review
Corrections and comments
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aims: Accurate prediction of clinical outcomes following percutaneous coronary intervention (PCI) is essential for mitigating risk and peri-procedural planning. Traditional risk models have demonstrated a modest predictive value. Machine learning (ML) models offer an alternative risk stratification that may provide improved predictive accuracy. Methods and results: This study was reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies and Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis guidelines. PubMed, EMBASE, Web of Science, and Cochrane databases were searched until 1 November 2023 for studies comparing ML models with traditional statistical methods for event prediction after PCI. The primary outcome was comparative discrimination measured by Conclusion: Machine learning models marginally outperformed traditional risk scores in the discrimination of MACE and major bleeding following PCI. While integration of ML algorithms into electronic healthcare systems has been hypothesized to improve peri-procedural risk stratification, immediate implementation in the clinical setting remains uncertain. Further research is required to overcome methodological and validation limitations.
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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.