title: Brief tool to measure risk-adjusted surgical outcomes in resource-limited hospitals.
abstract: OBJECTIVE
  To develop and validate a risk-adjusted tool with fewer than 10 variables to measure surgical outcomes in resource-limited hospitals.
  
  
  METHODS
  All National Surgical Quality Improvement Program (NSQIP) preoperative variables were used to develop models to predict inpatient mortality. The models were built by sequential addition of variables selected based on their area under the receiver operator characteristic curve (AUROC) and externally validated using data based on medical record reviews at 1 hospital outside the data set. SETTING Model development was based on data from the NSQIP from 2005 to 2009. Validation was based on data from 1 nonurban hospital in the United States from 2009 to 2010.
  
  
  METHODS
  A total of 631 449 patients in NSQIP and 239 patients from the validation hospital.
  
  
  METHODS
  The AUROC value for each model.
  
  
  RESULTS
  The AUROC values reached higher than 90% after only 3 variables (American Society of Anesthesiologists class, functional status at time of surgery, and age). The AUROC values increased to 91% with 4 variables but did not increase significantly with additional variables. On validation, the model with the highest AUROC was the same 3-variable model (0.9398).
  
  
  CONCLUSIONS
  Fewer than 6 variables may be necessary to develop a risk-adjusted tool to predict inpatient mortality, reducing the cost of collecting variables by 95%. These variables should be easily collectable in resource-poor settings, including low- and middle-income countries, thus creating the first standardized tool to measure surgical outcomes globally. Research is needed to determine which of these limited-variable models is most appropriate in a variety of clinical settings.
authors.affiliation: Department of Surgery, University of California, San Diego, 200 W Arbor Dr, Ste 8400, San Diego, CA 92103, USA. jaa002@ucsd.edu
authors.forename: Jamie E
authors.initials: JE
authors.lastname: Anderson
authors.forename: Randi
authors.initials: R
authors.lastname: Lassiter
authors.forename: Stephen W
authors.initials: SW
authors.lastname: Bickler
authors.forename: Mark A
authors.initials: MA
authors.lastname: Talamini
authors.forename: David C
authors.initials: DC
authors.lastname: Chang
mesh_terms: D000368:Aged; D019985:Benchmarking / Q000379:methods*; D005260:Female; D006295:Health Resources; D006761:Hospitals; D006801:Humans; D008297:Male; D008875:Middle Aged; D017063:Outcome Assessment, Health Care / Q000379:methods*; D018570:Risk Assessment; D013514:Surgical Procedures, Operative / Q000592:standards*