The application of comorbidity indices to predict early postoperative outcomes after laparoscopic roux-en-Y gastric bypass: A nationwide comparative analysis of over 70,000 cases
Background: Patients undergoing laparoscopic Roux-en-Y gastric bypass (LRYGB) often have substantial comorbidities, which must be taken into account to appropriately assess expected postoperative outcomes. The Charlson/Deyo and Elixhauser indices are widely used comorbidity measures, both of which also have revised algorithms based on enhanced ICD-9-CM coding. It is currently unclear which of the existing comorbidity measures best predicts early postoperative outcomes following LRYGB. Methods: Using the Nationwide Inpatient Sample, patients 18 years or older undergoing LRYGB for obesity between 2001 and 2008 were identified. Comorbidities were assessed according to the original and enhanced Charlson/Deyo and Elixhauser indices. Using multivariate logistic regression, the following early postoperative outcomes were assessed: overall postoperative complications, length of hospital stay, and conversion to open surgery. Model performance for the four comorbidity indices was assessed and compared using C-statistics and the Akaike's information criterion (AIC). Results: A total of 70,287 patients were included. Mean age was 43.1 years (SD, 10.8), 81.6 % were female and 60.3 % were White. Both the original and enhanced Elixhauser indices modestly outperformed the Charlson/Deyo in predicting the surgical outcomes. All four models had similar C-statistics, but the original Elixhauser index was associated with the smallest AIC for all of the surgical outcomes. Conclusions: The original Elixhauser index is the best predictor of early postoperative outcomes in our cohort of patients undergoing LRYGB. However, differences between the Charlson/Deyo and Elixhauser indices are modest, and each of these indices provides clinically relevant insight for predicting early postoperative outcomes in this high-risk patient population. © 2013 Springer Science+Business Media New York.
Shin, JH; Worni, M; Castleberry, AW; Pietrobon, R; Omotosho, PA; Silberberg, M; Østbye, T
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