Optimizing ACS NSQIP modeling for evaluation of surgical quality and risk: patient risk adjustment, procedure mix adjustment, shrinkage adjustment, and surgical focus

J Am Coll Surg. 2013 Aug;217(2):336-46.e1. doi: 10.1016/j.jamcollsurg.2013.02.027. Epub 2013 Apr 28.


The 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.

Keywords: ACS; American College of Surgeons; CPT; Current Procedural Terminology; O/E; OR; SAR; VA; VASQIP; Veterans Affairs; Veterans Affairs Surgical Quality Improvement Program; observed to expected ratio; odds ratio; semi-annual report.

MeSH terms

  • Benchmarking / statistics & numerical data*
  • Hospitals / standards*
  • Humans
  • Logistic Models
  • Models, Statistical*
  • Quality Improvement / statistics & numerical data*
  • Risk Adjustment / methods*
  • Risk Adjustment / trends
  • Surgical Procedures, Operative / standards*
  • United States