ArticleScientific reports2022
Machine learning models for prediction of adverse events after percutaneous coronary intervention.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it, 44 citations in OpenAlex.
- 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
- A Survey on Perspectives Toward Artificial Intelligence Among Italian Interventional Cardiologists.Journal of clinical medicine · 2026Article
- Predicting Mortality After Percutaneous Coronary Intervention in a Multiethnic Southeast Asian Population: Insights From Machine Learning.Journal of cardiovascular translational research · 2026Article
- Percutaneous Coronary Intervention and vascular access complications: A contemporary review.NPJ cardiovascular health · 2026Review
- Deep Learning and Cardiovascular Diseases: An Updated Narrative Review.Journal of clinical medicine · 2026Review
- Machine learning performance for a small dataset: random oversampling improves data imbalances and fairness.BMC medical research methodology · 2026Article
- 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
- Artificial Intelligence in Patient Blood Management: A Systematic Review of Predictive, Diagnostic, and Decision Support Applications.Journal of clinical medicine · 2025Review
- A machine learning-driven early warning system for cryptocaryoniasis in marine aquaculture.Parasites & vectors · 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 Coronary Artery Interventions: Preprocedural Planning and Procedural Assistance.Journal of the Society for Cardiovascular Angiography & Interventions · 2025Review
- Harnessing Artificial Intelligence for Innovation in Interventional Cardiovascular Care.Journal of the Society for Cardiovascular Angiography & Interventions · 2025Review
- Prediction of contrast-associated acute kidney injury with machine-learning in patients undergoing contrast-enhanced computed tomography in emergency department.Scientific reports · 2025Article
- Machine Learning Applications in Acute Coronary Syndrome: Diagnosis, Outcomes and Management.Advances in therapy · 2025Review
- Machine learning approaches for risk prediction after percutaneous coronary intervention: a systematic review and meta-analysis.European heart journal. Digital health · 2025Article
- Development of Automated Triggers in Ambulatory Settings in Brazil: Protocol for a Machine Learning-Based Design Thinking Study.JMIR research protocols · 2024Article
- Contemporary Risk Models for In-Hospital and 30-Day Mortality After Percutaneous Coronary Intervention.Current cardiology reports · 2024Review
- 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
- Predictive modeling for acute kidney injury after percutaneous coronary intervention in patients with acute coronary syndrome: a machine learning approach.European journal of medical research · 2024Article
- Contrastive learning with transformer for adverse endpoint prediction in patients on DAPT post-coronary stent implantation.Frontiers in cardiovascular medicine · 2024Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
An accurate prediction of major adverse events after percutaneous coronary intervention (PCI) improves clinical decisions and specific interventions. To determine whether machine learning (ML) techniques predict peri-PCI adverse events [acute kidney injury (AKI), bleeding, and in-hospital mortality] with better discrimination or calibration than the National Cardiovascular Data Registry (NCDR-CathPCI) risk scores, we developed logistic regression and gradient descent boosting (XGBoost) models for each outcome using data from a prospective, all-comer, multicenter registry that enrolled consecutive coronary artery disease patients undergoing PCI in Japan between 2008 and 2020. The NCDR-CathPCI risk scores demonstrated good discrimination for each outcome (C-statistics of 0.82, 0.76, and 0.95 for AKI, bleeding, and in-hospital mortality) with considerable calibration. Compared with the NCDR-CathPCI risk scores, the XGBoost models modestly improved discrimination for AKI and bleeding (C-statistics of 0.84 in AKI, and 0.79 in bleeding) but not for in-hospital mortality (C-statistics of 0.96). The calibration plot demonstrated that the XGBoost model overestimated the risk for in-hospital mortality in low-risk patients. All of the original NCDR-CathPCI risk scores for adverse periprocedural events showed adequate discrimination and calibration within our cohort. When using the ML-based technique, however, the improvement in the overall risk prediction was minimal.
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