Preoperative predictors of cost in medicare-age patients undergoing coronary artery bypass grafting

• Published in: The Annals of thoracic surgery Vol. 66; no. 3; pp. 740 - 745
• Main Authors: Longo, Kathleen M, Cowen, Mark E, Flaum, Morris A, Valsania, Paola, Schork, M.Anthony, Wagner, Leslie A, Prager, Richard L
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier Inc 01.09.1998
• Subjects: Age Factors; Aged; Aged, 80 and over; Coronary Artery Bypass - economics; Employment; Female; Hospital Bed Capacity, 500 and over; Hospital Costs - statistics & numerical data; Hospitals, Teaching - economics; Humans; Length of Stay; Male; Michigan; Proportional Hazards Models; Retrospective Studies; Statistics, Nonparametric; Survival Analysis; Michigan
• ISSN: 0003-4975; 1552-6259
• DOI: 10.1016/S0003-4975(98)00664-X

Background. Identification of preoperative factors that contribute to the cost of coronary artery bypass grafting could aid in predicting the procedure’s expense. In this study, 30 sociodemographic and clinical preoperative factors were examined with “survival analysis” techniques to determine characteristics related to total hospital cost. Methods. Characteristics of all patients age 65 or older undergoing isolated coronary artery bypass grafting from July 1993 to April 1995 (n = 757) were recorded. Software was developed within the hospital’s Transitions Systems, Inc, database to calculate the outcome variable of total cost. Nonparametric methods were used for the univariate analysis of the data, and the Cox proportional hazards model was used for the multivariable analysis, censoring 25 patients who died in the hospital. Results. Median hospital cost from the day of the operation until discharge was $15,198. Median length of stay after the operation was 6 days. Multivariable analysis revealed that age, preoperative renal failure, history of cerebrovascular accident, low ejection fraction, and surgical urgency were independent predictors of total cost. Conclusions. This study, using an accurate representation of true hospital cost and a modeling technique that accounts for the confounding effect of in-hospital death on cost, provides a template for analysis of cost in other patient groups.