Preoperative prediction of postoperative morbidity in coronary artery bypass grafting.
BACKGROUND
The risk factors of patients selected for coronary artery bypass grafting have increased in recent years because of the aging population. Prediction of postoperative complications is essential for optimal use of the available resources. The aim of this study was to develop a scoring method for prediction of postoperative morbidity of individual patients undergoing bypass grafting.


METHODS
Data from 386 consecutive patients who underwent coronary artery bypass grafting in a single center were retrospectively collected. The relationship between the preoperative risk factors and the postoperative morbidity was analyzed by the Bayesian approach. Three risk indices (15-factor and seven-factor computed and seven-factor manual models) were developed for the prediction of morbidity. The criterion for morbidity was a prolonged hospital stay postoperatively (> 12 days) because of adverse events.


RESULTS
The best predictive preoperative factors for increased morbidity were emergency operation, diabetes, rhythm other than sinus rhythm on the electrocardiogram or recent myocardial infarction, low ejection fraction (< 0.49), age greater than 70 years, decreased renal function, chronic pulmonary disease, cerebrovascular disease, and obesity. The sensitivity of the scoring methods ranged from 51% to 72% and the specificity, from 77% to 86%.


CONCLUSIONS
The results show that individual patients can be stratified according to postoperative risk for complications on the basis of preoperative information that is available for most patients.
Kurki
T S
TS
Heart Center, Deaconess Hospital, Helsinki, Finland.
Kataja
M
M
D000367:Age Factors; D000368:Aged; D000375:Aging; D001145:Arrhythmias, Cardiac / Q000453:epidemiology; D001499:Bayes Theorem; D002303:Cardiac Output, Low / Q000453:epidemiology; D002561:Cerebrovascular Disorders / Q000453:epidemiology; D001026:Coronary Artery Bypass / Q000009:adverse effects* / Q000706:statistics & numerical data; D003920:Diabetes Mellitus / Q000453:epidemiology; D004562:Electrocardiography; D004630:Emergencies; D005260:Female; D005387:Finland / Q000453:epidemiology; D005544:Forecasting; D006801:Humans; D007902:Length of Stay / Q000706:statistics & numerical data; D008173:Lung Diseases, Obstructive / Q000453:epidemiology; D008297:Male; D009203:Myocardial Infarction / Q000453:epidemiology; D009765:Obesity / Q000453:epidemiology; D011183:Postoperative Complications / Q000453:epidemiology; D051437:Renal Insufficiency / Q000453:epidemiology; D012189:Retrospective Studies; D012307:Risk Factors; D012680:Sensitivity and Specificity