Maryam Farhadian1, Sahar Dehdar Karsidani2, Azadeh Mozayanimonfared3, Hossein Mahjub4. 1. Research Center for Health Sciences, Department of Biostatistics, School of Public Health, Hamadan University of Medical Sciences, P.O. Box 4171-65175, Hamadan, Iran. 2. Department of Biostatistics, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran. 3. Department of Cardiology, Medical School, Hamadan University of Medical Sciences, Hamadan, Iran. 4. Research Center for Health Sciences, Department of Biostatistics, School of Public Health, Hamadan University of Medical Sciences, P.O. Box 4171-65175, Hamadan, Iran. h_mahjub@yahoo.com.
Abstract
BACKGROUND: Due 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. METHODS: The 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. RESULTS: Ninety-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. CONCLUSION: Machine-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.
BACKGROUND: Due 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. METHODS: The 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. RESULTS: Ninety-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. CONCLUSION: Machine-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.
Entities:
Keywords:
CABG; Major adverse cardiac event; Percutaneous coronary intervention; Random survival forest
Authors: Emanuele Meliga; Hector Manuel Garcia-Garcia; Marco Valgimigli; Alaide Chieffo; Giuseppe Biondi-Zoccai; Andrew O Maree; Stephen Cook; Lindsay Reardon; Claudio Moretti; Stefano De Servi; Igor F Palacios; Stephen Windecker; Antonio Colombo; Ron van Domburg; Imad Sheiban; Patrick W Serruys Journal: J Am Coll Cardiol Date: 2008-06-10 Impact factor: 24.094