ArticleArquivos brasileiros de cardiologia2024
Predictors of 30-Day Hospital Readmission Following CABG in a Multicenter Database: A Cross-Sectional Study.
Article in Arquivos brasileiros de cardiologia, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07349901 (Predicting Hospital Readmission for Surgical Patients Using Deep Learning Models With Smart Watch and Smart Ring Sensors Data), which is not on this map. Cited by 3 papers.
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Predicting Hospital Readmission for Surgical Patients Using Deep Learning Models With Smart Watch and Smart Ring Sensors Data
Who cites it
3 citing papers in PubMed.
- Prediction of Rehospitalization in Cardiac Bypass Surgery.Arquivos brasileiros de cardiologia · 2025Article
- Preoperative Predictors of Hospital Readmission within 5 Years Following CABG: Cohort Analysis of the REPLICCAR II Database.Arquivos brasileiros de cardiologia · 2025Article
- Look Who is Coming Back? Enhancing Predictive Models for Hospital Readmission Post-CABG: Insights and Perspectives.Arquivos brasileiros de cardiologia · 2024Article
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13 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundThe analysis of indicators such as hospital readmission rates is crucial for improving the quality of services and management of hospital processes.
objectivesTo identify the variables correlated with hospital readmission up to 30 days following coronary artery bypass grafting (CABG).
methodsCross-sectional cohort study by REPLICCAR II database (N=3,392) from June 2017 to June 2019. Retrospectively, 150 patients were analyzed to identify factors associated with hospital readmission within 30 days post-CABG using univariate and multivariate logistic regression. Analysis was conducted using software R, with a significance level of 0.05 and 95% confidence intervals.
resultsOut of 3,392 patients, 150 (4,42%0 were readmitted within 30 days post-discharge from CABG primarily due to infections (mediastinitis, surgical wounds, and sepsis) accounting for 52 cases (34.66%). Other causes included surgical complications (14/150, 9.33%) and pneumonia (13/150, 8.66%). The multivariate regression model identified an intercept (OR: 1.098, p<0.00001), sleep apnea (OR: 1.117, p=0.0165), cardiac arrhythmia (OR: 1.040, p=0.0712), and intra-aortic balloon pump use (OR: 1.068, p=0.0021) as predictors of the outcome, with an AUC of 0.70.
conclusion4.42% of patients were readmitted post-CABG, mainly due to infections. Factors such as sleep apnea (OR: 1.117, p=0.0165), cardiac arrhythmia (OR: 1.040, p=0.0712), and intra-aortic balloon pump use (OR: 1.068, p=0.0021) were predictors of readmission, with moderate risk discrimination (AUC: 0.70).
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