AI Article Synopsis
- Developed a scoring system to predict early mortality and morbidity in patients undergoing CABG surgery, distinguishing between low and high-risk individuals.
- Used data from 563 patients for development and 969 for validation, with logistic regression analysis identifying key risk factors.
- Found that specific factors like age, gender, and existing health conditions significantly influenced early mortality (1.8% low risk vs. 13.4% high risk) and morbidity (17% low risk vs. 41% high risk), demonstrating the system's effectiveness with ROC areas of 0.81 and 0.73, respectively.
Article Abstract
Objective: A scoring system to predict early mortality and morbidity in CABG, distinguishing low and high risk patients.
Methods: 563 patients (1998) served as development dataset, 969 patients as validation set. Univariate and logistic regression analysis was used to identify risk factors.
Results: Gender, hypertension, pulmonary disease, reoperation, age, operative status and left-ventricular function were predictive variables for early mortality. The area under the ROC curve was 0.81. We identified a low risk, mortality of 1.8% and a high-risk group, mortality of 13.4%. Diabetes, hypertension, kidney and lung disease, reoperation, operative status and left ventricular function were predictive variables for morbidity. The area under the ROC curve was 0.73. We identified a low risk, morbidity of 17%, and a high-risk group, morbidity of 41%.
Conclusion: This scoring system is a simple system identifying a low and high-risk group for morbidity and early mortality.
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| http://dx.doi.org/10.1016/s0967-2109(02)00081-9 | DOI Listing |
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