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. 2013 Aug;119(2):504-11.
doi: 10.3171/2013.1.JNS121375. Epub 2013 Feb 22.

Predictors of resource utilization in transsphenoidal surgery for Cushing disease

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Predictors of resource utilization in transsphenoidal surgery for Cushing disease

Andrew S Little et al. J Neurosurg. 2013 Aug.

Abstract

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.

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