Saturday, August 3, 2013

Predictors of resource utilization in transsphenoidal surgery for Cushing disease

Predictors of resource utilization in transsphenoidal surgery for Cushing disease
Journal of Neurosurgery: Journal of Neurosurgery: Table of Contents

Journal of Neurosurgery, Volume 119, Issue 2, Page 504-511, August 2013.
Object The short-term cost associated with subspecialized surgical care is an increasingly important metric and economic concern. This study sought to determine factors associated with hospital charges in patients undergoing transsphenoidal surgery for Cushing disease in an effort to identify the drivers of resource utilization. Methods The authors analyzed the Nationwide Inpatient Sample (NIS) hospital discharge database from 2007 to 2009 to determine factors that influenced hospital charges in patients who had undergone transsphenoidal surgery for Cushing disease. The NIS discharge database approximates a 20% sample of all inpatient admissions to nonfederal US hospitals. A multistep regression model was developed that adjusted for patient demographics, acuity measures, comorbidities, hospital characteristics, and complications. Results In 116 hospitals, 454 transsphenoidal operations were performed. The mean hospital charge was $48,272 ± $32,060. A multivariate regression model suggested that the primary driver of resource utilization was length of stay (LOS), followed by surgeon volume, hospital characteristics, and postoperative complications. A 1% increase in LOS increased hospital charges by 0.60%. Patient charges were 13% lower when performed by high-volume surgeons compared with low-volume surgeons and 22% lower in large hospitals compared with small hospitals. Hospital charges were 12% lower in cases with no postoperative neurological complications. The proposed model accounted for 46% of hospital charge variance. Conclusions This analysis of hospital charges in transsphenoidal surgery for Cushing disease suggested that LOS, hospital characteristics, surgeon volume, and postoperative complications are important predictors of resource utilization. These findings may suggest opportunities for improvement.

Original Article: http://thejns.org/doi/abs/10.3171/2013.1.JNS121375?ai=ru&mi=0&af=R

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