title: Mortality after noncardiac surgery: prediction from administrative versus clinical data.
abstract: BACKGROUND
  Hospital profiles are increasingly constructed using risk-adjusted clinical data abstracted from patient records.
  
  
  OBJECTIVE
  We sought to compare hospital profiles based on risk adjusted death within 30 days of surgery from administrative versus clinical data in a national cohort of surgical patients.
  
  
  METHODS
  This was a cohort study that included 78,546 major noncardiac operations performed between October 1, 1991 and December 31, 1993 in 44 Veterans Affairs hospitals. Administrative data were used to develop and validate multivariable logistic regression models of 30-day postoperative death for all surgery and 4 surgical specialties (general, orthopedic, thoracic, and vascular). Previously developed and validated clinical models were obtained and reproduced for matching operations using data from the VA National Surgical Quality Improvement Program. Observed-to-expected 30-day mortality ratios for administrative and clinical data were calculated and compared for each hospital.
  
  
  RESULTS
  In multivariable logistic regression models using administrative data, characteristics such as patient age, race, marital status, admission from a nursing home, interhospital transfer, admission on the weekend, weekend surgery, and risk strata consisting of groups of principal and comorbidity diagnoses were predictive of postoperative mortality (P <0.05). Correlations of the clinical and administrative observed-to-expected ratios were 0.75, 0.83, 0.64, 0.78, and 0.86 for all surgery, general, orthopedic, thoracic, and vascular surgery, respectively. When compared with clinical models, administrative models identified outlier hospitals with sensitivity of 73%, specificity of 89%, positive predictive value of 51%, and negative predictive value of 96%.
  
  
  CONCLUSIONS
  Our data suggest that risk adjustment of mortality using administrative data may be useful for screening hospitals for potential quality problems.
authors.affiliation: Houston Center for Quality of Care and Utilization Studies, the Michael E. Debakey Veterans Affairs Medical Center, Houston, Texas 77030, USA. hgordon@bcm.tmc.edu
authors.forename: Howard S
authors.initials: HS
authors.lastname: Gordon
authors.forename: Michael L
authors.initials: ML
authors.lastname: Johnson
authors.forename: Nelda P
authors.initials: NP
authors.lastname: Wray
authors.forename: Nancy J
authors.initials: NJ
authors.lastname: Petersen
authors.forename: William G
authors.initials: WG
authors.lastname: Henderson
authors.forename: Shukri F
authors.initials: SF
authors.lastname: Khuri
authors.forename: Jane M
authors.initials: JM
authors.lastname: Geraci
mesh_terms: D000368:Aged; D015331:Cohort Studies; D005260:Female; D006298:Health Services Administration / Q000706:statistics & numerical data*; D006787:Hospitals, Veterans / Q000706:statistics & numerical data*; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D017063:Outcome Assessment, Health Care; D011787:Quality of Health Care / Q000706:statistics & numerical data; D013514:Surgical Procedures, Operative / Q000401:mortality*; D015996:Survival Rate; D014728:Veterans / Q000706:statistics & numerical data*