title: Preoperative risk factors and surgical complexity are more predictive of costs than postoperative complications: a case study using the National Surgical Quality Improvement Program (NSQIP) database.
abstract: OBJECTIVE
  This single-center study tested the hypothesis that preoperative risk factors and surgical complexity predict more variation in hospital costs than complications.
  
  
  BACKGROUND
  Complications after surgical operations have been shown to significantly increase hospital cost. The impact on complication-related costs of preoperative risk factors is less well known.
  
  
  METHODS
  The National Surgical Quality Improvement Program (NSQIP) preoperative risk factors, surgical complexity, and outcomes, along with hospital costs, were analyzed for a random sample of 5875 patients on 6 surgical services. Operation complexity was assessed by work RVUs (Centers for Medicare and Medicaid Services Resource Based Relative Value Scale). The difference in mean hospital costs associated with all variables was analyzed. Multiple linear regression was used to determine the cost variation associated with all variables separately and combined.
  
  
  RESULTS
  Fifty-one of 60 preoperative risk factors, work RVUs, and 22 of 29 postoperative complications were associated with higher variable direct costs (P < 0.05). Linear regressions showed that risk factors predicted 33% (P < 0.001) of cost variation, work RVUs predicted 23% (P < 0.001), and complications predicted 20% (P < 0.001). Risk factors and work RVUs together predicted 49% of cost variation (P < 0.001) or 16% more than risk factors alone. Adding complications to this combined model modestly increased prediction of costs by 4% for a total of 53% (P < 0.001).
  
  
  CONCLUSIONS
  Preoperative risk factors and surgical complexity are more effective predictors of hospital costs than complications. Preoperative intervention to reduce risk could lead to significant cost savings. Payers and regulatory agencies should risk-adjust hospital cost assessments using clinical information that integrates costs, preoperative risk, complexity of operation, and outcomes.
authors.affiliation: University of Kentucky, Department of Surgery, Lexington, Kentucky 40536, USA. dldave0@email.uky.edu
authors.forename: Daniel L
authors.initials: DL
authors.lastname: Davenport
authors.forename: William G
authors.initials: WG
authors.lastname: Henderson
authors.forename: Shukri F
authors.initials: SF
authors.lastname: Khuri
authors.forename: Robert M
authors.initials: RM
authors.lastname: Mentzer
mesh_terms: D019992:Databases as Topic; D017721:Hospital Costs; D006801:Humans; D007902:Length of Stay / Q000191:economics; D011183:Postoperative Complications / Q000191:economics*; D011237:Predictive Value of Tests; D011787:Quality of Health Care / Q000191:economics*; D012189:Retrospective Studies; D020379:Risk Adjustment; D012307:Risk Factors; D013514:Surgical Procedures, Operative / Q000191:economics / Q000592:standards*