• Mark E Cohen, Clifford Y Ko, Karl Y Bilimoria, Lynn Zhou, Kristopher Huffman, Xue Wang, Yaoming Liu, Kari Kraemer, Xiangju Meng, Ryan Merkow, Warren Chow, Brian Matel, Karen Richards, Amy J Hart, Justin B Dimick, and Bruce L Hall.
• Division of Research and Optimal Patient Care, American College of Surgeons, Chicago, IL 60611-3211, USA. markcohen@facs.org
• J. Am. Coll. Surg. 2013 Aug 1; 217 (2): 336-46.e1.

AbstractThe American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) collects detailed clinical data from participating hospitals using standardized data definitions, analyzes these data, and provides participating hospitals with reports that permit risk-adjusted comparisons with a surgical quality standard. Since its inception, the ACS NSQIP has worked to refine surgical outcomes measurements and enhance statistical methods to improve the reliability and validity of this hospital profiling. From an original focus on controlling for between-hospital differences in patient risk factors with logistic regression, ACS NSQIP has added a variable to better adjust for the complexity and risk profile of surgical procedures (procedure mix adjustment) and stabilized estimates derived from small samples by using a hierarchical model with shrinkage adjustment. New models have been developed focusing on specific surgical procedures (eg, "Procedure Targeted" models), which provide opportunities to incorporate indication and other procedure-specific variables and outcomes to improve risk adjustment. In addition, comparative benchmark reports given to participating hospitals have been expanded considerably to allow more detailed evaluations of performance. Finally, procedures have been developed to estimate surgical risk for individual patients. This article describes the development of, and justification for, these new statistical methods and reporting strategies in ACS NSQIP. Copyright © 2013 American College of Surgeons. Published by Elsevier Inc. All rights reserved.

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