Carlos Alberto Sancio Junior, Fabiane Letícia de Freitas, Gabrielle Barbosa Borgomoni, Daniella de Lima Pes, Pedro Horigoshi Reis, Pedro Gabriel Melo de Barros E Silva, Marcelo Arruda Nakazone, Marcos Gradim Tiveron, Valquiria Pelisser Campagnucci, Luiz Augusto Lisboa, Luís Alberto Oliveira Dallan, Fabio Biscegli Jatene, Omar Asdrúbal Vilca Mejia
{"title":"Preoperative Predictors of Hospital Readmission within 5 Years Following CABG: Cohort Analysis of the REPLICCAR II Database.","authors":"Carlos Alberto Sancio Junior, Fabiane Letícia de Freitas, Gabrielle Barbosa Borgomoni, Daniella de Lima Pes, Pedro Horigoshi Reis, Pedro Gabriel Melo de Barros E Silva, Marcelo Arruda Nakazone, Marcos Gradim Tiveron, Valquiria Pelisser Campagnucci, Luiz Augusto Lisboa, Luís Alberto Oliveira Dallan, Fabio Biscegli Jatene, Omar Asdrúbal Vilca Mejia","doi":"10.36660/abc.20240420","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Reducing hospital readmissions following coronary artery bypass grafting (CABG) surgeries is essential to optimizing medium- and long-term patient outcomes.</p><p><strong>Objective: </strong>To analyze preoperative predictors associated with all-cause and cardiac readmissions within 5 years following CABG.</p><p><strong>Methods: </strong>We analyzed 1,387 patients who underwent CABG between June 2017 and July 2019 using data from the multicenter REPLICCAR II registry. Follow-up was carried out by telephone interviews using a questionnaire structured in the REDCap platform. Statistical analysis included univariate and multivariate methods, with Cox regression and internal validation through calibration and discrimination tests. A significance level of 5% was applied.</p><p><strong>Results: </strong>The cumulative incidence of all-cause readmission was 27.69%, with a mean follow-up of 4.3 years and a mean time to readmission of 2.4 years. Multivariate regression analysis indicated the following predictors of higher all-cause readmission risk: lower body mass index (HR=0.97, p=0.032), history of myocardial infarction (HR=1.27, p=0.024), diabetes mellitus (HR=1.35, p=0.004), renal failure (HR=1.62, p=0.004), and higher STS score (HR=1.22, p<0.001). A moderate correlation was observed between readmission and mortality (Rho=0.55).</p><p><strong>Conclusions: </strong>This analysis demonstrates that lower body mass index, history of myocardial infarction, diabetes mellitus, renal failure, and elevated STS scores are significant predictors of increased hospital readmission risk following CABG.</p>","PeriodicalId":93887,"journal":{"name":"Arquivos brasileiros de cardiologia","volume":"122 2","pages":"e20240420"},"PeriodicalIF":0.0000,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Arquivos brasileiros de cardiologia","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.36660/abc.20240420","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Reducing hospital readmissions following coronary artery bypass grafting (CABG) surgeries is essential to optimizing medium- and long-term patient outcomes.
Objective: To analyze preoperative predictors associated with all-cause and cardiac readmissions within 5 years following CABG.
Methods: We analyzed 1,387 patients who underwent CABG between June 2017 and July 2019 using data from the multicenter REPLICCAR II registry. Follow-up was carried out by telephone interviews using a questionnaire structured in the REDCap platform. Statistical analysis included univariate and multivariate methods, with Cox regression and internal validation through calibration and discrimination tests. A significance level of 5% was applied.
Results: The cumulative incidence of all-cause readmission was 27.69%, with a mean follow-up of 4.3 years and a mean time to readmission of 2.4 years. Multivariate regression analysis indicated the following predictors of higher all-cause readmission risk: lower body mass index (HR=0.97, p=0.032), history of myocardial infarction (HR=1.27, p=0.024), diabetes mellitus (HR=1.35, p=0.004), renal failure (HR=1.62, p=0.004), and higher STS score (HR=1.22, p<0.001). A moderate correlation was observed between readmission and mortality (Rho=0.55).
Conclusions: This analysis demonstrates that lower body mass index, history of myocardial infarction, diabetes mellitus, renal failure, and elevated STS scores are significant predictors of increased hospital readmission risk following CABG.