ArticleCardiovascular diagnosis and therapy2021
Machine learning-based long-term outcome prediction in patients undergoing percutaneous coronary intervention.
Article in Cardiovascular diagnosis and therapy, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.
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Who cites it
13 citing papers in PubMed, 2 syntheses or guidelines pooled it, 24 citations in OpenAlex.
- Risk Prediction Models for Hospital Readmission After Percutaneous Coronary Intervention: A Systematic Review and Meta-Analysis.Reviews in cardiovascular medicine · 2025Pooled it
- Comparison of machine learning models with conventional statistical methods for prediction of percutaneous coronary intervention outcomes: a systematic review and meta-analysis.BMC cardiovascular disorders · 2025Pooled it
- Development and validation of an interpretable machine learning model for predicting 5-year major adverse cardiovascular events in patients with coronary artery disease.BMC cardiovascular disorders · 2026Article
- Machine learning performance for a small dataset: random oversampling improves data imbalances and fairness.BMC medical research methodology · 2026Article
- Multimodal machine learning for 5-year mortality prediction after percutaneous coronary intervention.Scientific reports · 2025Article
- Applied machine learning to predict 1-year major adverse cardiovascular events in elderly patients after percutaneous coronary intervention.BMC medical informatics and decision making · 2025Article
- Artificial Intelligence in Risk Stratification and Outcome Prediction for Transcatheter Aortic Valve Replacement: A Systematic Review and Meta-Analysis.Journal of personalized medicine · 2025Review
- Artificial Intelligence in Cardiovascular Imaging and Interventional Cardiology: Emerging Trends and Clinical Implications.Journal of the Society for Cardiovascular Angiography & Interventions · 2025Review
- Machine learning-driven prediction of medical expenses in triple-vessel PCI patients using feature selection.BMC health services research · 2025Article
- Using the Super Learner algorithm to predict risk of major adverse cardiovascular events after percutaneous coronary intervention in patients with myocardial infarction.BMC medical research methodology · 2024Article
- Development and validation of a predictive models for predicting the cardiac events within one year for patients underwent percutaneous coronary intervention procedure at IJN.BMC cardiovascular disorders · 2023Article
- Artificial Intelligence-Based Methods for Precision Cardiovascular Medicine.Journal of personalized medicine · 2023Review
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Corrections and comments
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Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTraditional prognostic risk assessment in patients with coronary artery disease undergoing percutaneous coronary intervention (PCI) is based on a limited selection of clinical and imaging findings. Machine learning (ML) can consider a higher number and complexity of variables and may be useful for characterising cardiovascular risk, predicting outcomes, and identifying biomarkers in large population studies.
methodsWe prospectively enrolled 9,680 consecutive patients with coronary artery disease who underwent PCI at our institution between January 2013 and December 2013. Clinical features were selected and used to train 6 different ML models (support vector machine, decision tree, random forest, gradient boosting decision tree, neural network, and logistic regression) to predict cardiovascular outcomes, 10-fold cross-validation to evaluate the performance of models.
resultsDuring the 5-year follow-up, 467 (4.82%) patients died. Eighty-seven risk baseline measurements were used to train ML models. Compared with the other models, the random forest model (RF-PCI) exhibited the best performance on predicting all-cause mortality (area under the receiver operating characteristic curve: 0.71±0.04). Calibration plots demonstrated a slight overprediction for patients using the RF-PCI model (Hosmer-Lemeshow test: P>0.05). The top 15 features related to PCI candidates' long-term prognosis, among which 11 were laboratory measures.
conclusionsML models improved the prediction of long-term all-cause mortality in patients with coronary artery disease before PCI. The performance of the RF model was better than that of the other models, providing a meaningful stratification.
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