title: Identifying patient preoperative risk factors and postoperative adverse events in administrative databases: results from the Department of Veterans Affairs National Surgical Quality Improvement Program.
abstract: BACKGROUND
  The Department of Veterans Affairs (DVA) National Surgical Quality Improvement Program (NSQIP) employs trained nurse data collectors to prospectively gather preoperative patient characteristics and 30-day postoperative outcomes for most major operations in 123 DVA hospitals to provide risk-adjusted outcomes to centers as quality indicators. It has been suggested that routine hospital discharge abstracts contain the same information and would provide accurate and complete data at much lower cost.
  
  
  METHODS
  With preoperative risks and 30-day outcomes recorded by trained data collectors as criteria standards, ICD-9-CM hospital discharge diagnosis codes in the Patient Treatment File (PTF) were tested for sensitivity and positive predictive value. ICD-9-CM codes for 61 preoperative patient characteristics and 21 postoperative adverse events were identified.
  
  
  RESULTS
  Moderately good ICD-9-CM matches of descriptions were found for 37 NSQIP preoperative patient characteristics (61%); good data were available from other automated sources for another 15 (25%). ICD-9-CM coding was available for only 13 (45%) of the top 29 predictor variables. In only three (23%) was sensitivity and in only four (31%) was positive predictive value greater than 0.500. There were ICD-9-CM matches for all 21 NSQIP postoperative adverse events; multiple matches were appropriate for most. Postoperative occurrence was implied in only 41%; same breadth of clinical description in only 23%. In only four (7%) was sensitivity and only two (4%) was positive predictive value greater than 0.500.
  
  
  CONCLUSIONS
  Sensitivity and positive predictive value of administrative data in comparison to NSQIP data were poor. We cannot recommend substitution of administrative data for NSQIP data methods.
authors.affiliation: The Hines VA Midwest Center for Health Services and Policy Research, IL 60141, USA.
authors.forename: William R
authors.initials: WR
authors.lastname: Best
authors.forename: Shukri F
authors.initials: SF
authors.lastname: Khuri
authors.forename: Maureen
authors.initials: M
authors.lastname: Phelan
authors.forename: Kwan
authors.initials: K
authors.lastname: Hur
authors.forename: William G
authors.initials: WG
authors.lastname: Henderson
authors.forename: John G
authors.initials: JG
authors.lastname: Demakis
authors.forename: Jennifer
authors.initials: J
authors.lastname: Daley
mesh_terms: D019985:Benchmarking; D015982:Bias; D015897:Comorbidity; D003625:Data Collection; D016208:Databases, Factual / Q000706:statistics & numerical data*; D006787:Hospitals, Veterans / Q000592:standards*; D006801:Humans; D008499:Medical Records / Q000706:statistics & numerical data; D010351:Patient Discharge / Q000706:statistics & numerical data*; D011183:Postoperative Complications / Q000453:epidemiology*; D011237:Predictive Value of Tests; D019984:Quality Indicators, Health Care; D020379:Risk Adjustment; D012307:Risk Factors; D012680:Sensitivity and Specificity; D012720:Severity of Illness Index; D013514:Surgical Procedures, Operative / Q000009:adverse effects* / Q000401:mortality; D017749:Total Quality Management; D014481:United States / Q000453:epidemiology; D014493:United States Department of Veterans Affairs