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Development and validation of a clinical prediction rule for major adverse outcomes in coronary bypass grafting
E B Fortescue1, K Kahn, D W Bates
1Division of General Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts 02115, USA.
Insights
A new clinical prediction rule accurately identifies patients at risk for major adverse outcomes after coronary artery bypass grafting (CABG). This tool helps stratify surgical candidates before the procedure, improving patient selection and care.
Area of Science:
- Cardiology
- Medical Informatics
- Clinical Prediction Modeling
Background:
- Coronary artery bypass grafting (CABG) is a major surgical procedure with potential for serious in-hospital complications.
- Accurate prediction of major adverse outcomes after CABG is crucial for patient management and risk stratification.
Purpose of the Study:
- To develop and internally validate a clinical prediction rule for in-hospital major adverse outcomes following CABG.
- To identify key predictors of adverse events available before the procedure.
Main Methods:
- A retrospective analysis of 9,498 adult patients undergoing CABG at 12 academic medical centers.
- Development and validation of a prediction rule using logistic regression and assessed via receiver-operating characteristic analysis and the Hosmer-Lemeshow statistic.
Main Results:
- A major adverse outcome occurred in 6.5% (derivation) and 7.2% (validation) of patients.
- Sixteen independent predictors were identified, contributing to a risk score that stratified patients into 6 risk levels.
- The rule demonstrated good discrimination and calibration, with probability spreads from 1.7% to 32.3% (derivation) and 2.2% to 22.3% (validation).
Conclusions:
- The developed clinical prediction rule accurately stratifies patients undergoing CABG based on their risk of postoperative major adverse outcomes.
- This tool can aid clinicians in preoperative risk assessment and patient selection for CABG surgery.
Abstract:
In this study, we develop and internally validate a clinical prediction rule for in-hospital major adverse outcomes, defined as death, renal failure, reinfarction, cardiac arrest, cerebrovascular accident, or coma, in patients who underwent coronary artery bypass grafting (CABG). All adult patients (n = 9,498) who underwent a CABG and no other concomitant surgery at 12 academic medical centers from August 1993 to October 1995 were included in the study. We assessed in-hospital major adverse outcomes and their predictors using information on admission, coronary angiography, and postoperative hospital course. Predictor variables were limited to information available before the procedure, and outcome variables were represented only by events that occurred postoperatively. We developed and internally validated a clinical prediction rule for any major adverse outcome after CABG. The rule's ability to discriminate outcomes and its calibration were assessed using receiver-operating characteristic analysis and the Hosmer-Lemeshow goodness-of-fit statistic, respectively. A major adverse outcome occurred in 6.5% of patients in the derivation set and 7.2% in the validation set. Death occurred in 2.5% of patients in the derivation set and 2.2% in the validation set. Sixteen variables were independently correlated with major adverse outcomes, with the risk score value attributed to each risk factor ranging from 2 to 12 points. The rule stratified patients into 6 levels of risk based on the total risk score. The spread in probability between the lowest and highest risk groups of having a major adverse outcome was 1.7% to 32.3% in the derivation set and 2.2% to 22.3% in the validation set. The prediction model performed well in both outcome discrimination and calibration. Thus, this clinical prediction rule allows accurate stratification of potential CABG candidates before surgery according to the risk of experiencing a major adverse outcome postoperatively.
