ArticleBMC cardiovascular disorders2021
Risk factors associated with major adverse cardiac and cerebrovascular events following percutaneous coronary intervention: a 10-year follow-up comparing random survival forest and Cox proportional-hazards model.
Article in BMC cardiovascular disorders, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
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15 citing papers in PubMed, 2 syntheses or guidelines pooled it, 22 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 Systematic Review of Artificial Intelligence Models for Time-to-Event Outcome Applied in Cardiovascular Disease Risk Prediction.Journal of medical systems · 2024Pooled it
- Comparison of the Cox Proportional Hazards Model and Random Survival Forest Algorithm for Predicting Patient-Specific Survival Probabilities in Clinical Trial Data.Biometrical journal. Biometrische Zeitschrift · 2026Article
- Machine learning-based prognostic model for local control prediction in lung cancer patients undergoing stereotactic body radiation therapy: a preliminary study.European journal of medical research · 2026Article
- Factors associated with survival of tuberculosis patients in Southeast Iran: comparison of stepwise cox regression, survival tree, and random survival forest.BMC public health · 2025Article
- Machine Learning Applications in Acute Coronary Syndrome: Diagnosis, Outcomes and Management.Advances in therapy · 2025Review
- Consequences of low estimated glomerular filtration rate either before or early after kidney donation.American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons · 2024Article
- Triglyceride-glucose index predicts major adverse cardiovascular events in patients with chronic kidney disease.International urology and nephrology · 2024Article
- Risk factors affecting patients survival with colorectal cancer in Morocco: survival analysis using an interpretable machine learning approach.Scientific reports · 2024Article
- Mortality prediction and influencing factors for intensive care unit patients with acute tubular necrosis: random survival forest and cox regression analysis.Frontiers in pharmacology · 2024Article
- Use of survival support vector machine combined with random survival forest to predict the survival of nasopharyngeal carcinoma patients.Translational cancer research · 2023Article
- Intelligent prediction of major adverse cardiovascular events (MACCE) following percutaneous coronary intervention using ANFIS-PSO model.BMC cardiovascular disorders · 2022Article
- Prediction of prognosis in elderly patients with sepsis based on machine learning (random survival forest).BMC emergency medicine · 2022Article
- Current and Future Applications of Artificial Intelligence in Coronary Artery Disease.Healthcare (Basel, Switzerland) · 2022Review
- Artificial intelligence predictive system of individual survival rate for lung adenocarcinoma.Computational and structural biotechnology journal · 2022Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
backgroundDue to the limited number of studies with long term follow-up of patients undergoing Percutaneous Coronary Intervention (PCI), we investigated the occurrence of Major Adverse Cardiac and Cerebrovascular Events (MACCE) during 10 years of follow-up after coronary angioplasty using Random Survival Forest (RSF) and Cox proportional hazards models.
methodsThe current retrospective cohort study was performed on 220 patients (69 women and 151 men) undergoing coronary angioplasty from March 2009 to March 2012 in Farchshian Medical Center in Hamadan city, Iran. Survival time (month) as the response variable was considered from the date of angioplasty to the main endpoint or the end of the follow-up period (September 2019). To identify the factors influencing the occurrence of MACCE, the performance of Cox and RSF models were investigated in terms of C index, Integrated Brier Score (IBS) and prediction error criteria.
resultsNinety-six patients (43.7%) experienced MACCE by the end of the follow-up period, and the median survival time was estimated to be 98 months. Survival decreased from 99% during the first year to 39% at 10 years' follow-up. By applying the Cox model, the predictors were identified as follows: age (HR = 1.03, 95% CI 1.01-1.05), diabetes (HR = 2.17, 95% CI 1.29-3.66), smoking (HR = 2.41, 95% CI 1.46-3.98), and stent length (HR = 1.74, 95% CI 1.11-2.75). The predictive performance was slightly better by the RSF model (IBS of 0.124 vs. 0.135, C index of 0.648 vs. 0.626 and out-of-bag error rate of 0.352 vs. 0.374 for RSF). In addition to age, diabetes, smoking, and stent length, RSF also included coronary artery disease (acute or chronic) and hyperlipidemia as the most important variables.
conclusionMachine-learning prediction models such as RSF showed better performance than the Cox proportional hazards model for the prediction of MACCE during long-term follow-up after PCI.
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