Interpretable machine learning risk model to predict readmission after coronary artery bypass grafting.
4 affiliations
- 1Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Ga.
- 2Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, Ga.
- 3Department of Computer Science, Georgia State University, Atlanta, Ga.
- 4Division of Cardiothoracic Surgery, Department of Surgery, Emory University School of Medicine, Atlanta, Ga.
Abstract
Objective
Readmission within 30 days of discharge after coronary artery bypass grafting (CABG) continues to be a concern for patients and health care systems in cardiothoracic care. We developed an interpretable machine learning (ML) risk model to evaluate, understand, and integrate preoperative risk factors to predict 30-day readmission after CABG.Methods
The study analyzed a population of patients who had undergone isolated CABG between 2014 and 2023, drawn from a multihospital academic health system. The primary outcome was an index readmission within 30 days of discharge after isolated CABG. The study evaluated 34 patient-specific preoperative variables using the recursive feature elimination with cross-validation method and ML algorithms to evaluate an optimal set of risk factors to predict readmission after CABG. SHapley Additive exPlanations framework was used to understand the contribution of risk factors to model prediction.Results
A total of 8999 patients were identified, and the overall observed 30-day all-cause same system readmission rate was 9.56%, n = 860. The model produced the top 7 preoperative risk factors: hematocrit, Model for End-Stage Liver Disease score, hemoglobin, creatinine, body mass index, white blood cell count, and platelets. The random forest model demonstrated good performance with an area under the receiver operating characteristics curve of 0.74.Conclusions
This study used interpretable ML techniques to identify and interpret an optimal set of preoperative risk factors for 30-day readmission after CABG. The random forest model demonstrated good discrimination in identifying at-risk patients. Individual hospitals can apply the proposed method to their large datasets and assess risk factors that may help identify patients who would experience 30-day readmission after CABG.
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BibTeX
@article{manyam2026interpretable,
title = {{Interpretable machine learning risk model to predict readmission after coronary artery bypass grafting.}},
author = {Rameshbabu Manyam and Conglin Bao and Yixuan He and Sheena Warner and Yanqing Zhang and William Brent Keeling},
journal = {JTCVS open},
year = {2026},
month = aug,
doi = {10.1016/j.xjon.2026.102050},
url = {https://europepmc.org/article/MED/42948447},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Interpretable machine learning risk model to predict readmission after coronary artery bypass grafting.
AU - Rameshbabu Manyam
AU - Conglin Bao
AU - Yixuan He
AU - Sheena Warner
AU - Yanqing Zhang
AU - William Brent Keeling
PY - 2026
DA - 2026/08/07
JO - JTCVS open
DO - 10.1016/j.xjon.2026.102050
UR - https://europepmc.org/article/MED/42948447
AB - Objective Readmission within 30 days of discharge after coronary artery bypass grafting (CABG) continues to be a concern for patients and health care systems in cardiothoracic care. We developed an interpretable machine learning (ML) risk model to evaluate, understand, and integrate preoperative risk factors to predict 30-day readmission after CABG.Methods The study analyzed a population of patients who had undergone isolated CABG between 2014 and 2023, drawn from a multihospital academic health system. The primary outcome was an index readmission within 30 days of discharge after isolated CABG. The study evaluated 34 patient-specific preoperative variables using the recursive feature elimination with cross-validation method and ML algorithms to evaluate an optimal set of risk factors to predict readmission after CABG. SHapley Additive exPlanations framework was used to understand the contribution of risk factors to model prediction.Results A total of 8999 patients were identified, and the overall observed 30-day all-cause same system readmission rate was 9.56%, n = 860. The model produced the top 7 preoperative risk factors: hematocrit, Model for End-Stage Liver Disease score, hemoglobin, creatinine, body mass index, white blood cell count, and platelets. The random forest model demonstrated good performance with an area under the receiver operating characteristics curve of 0.74.Conclusions This study used interpretable ML techniques to identify and interpret an optimal set of preoperative risk factors for 30-day readmission after CABG. The random forest model demonstrated good discrimination in identifying at-risk patients. Individual hospitals can apply the proposed method to their large datasets and assess risk factors that may help identify patients who would experience 30-day readmission after CABG.
SN - 2666-2736
ER -