title: Using clinical variables and drug prescription data to control for confounding in outcome comparisons between hospitals.
abstract: BACKGROUND
  Hospital discharge records are an essential source of information when comparing health outcomes among hospitals; however, they contain limited information on acute clinical conditions. Doubts remain as to whether the addition of clinical and drug consumption information would improve the prediction of health outcomes and reduce confounding in inter-hospital comparisons. The objective of the study is to compare the performance of two multivariate risk adjustment models, with and without clinical data and drug prescription information, in terms of their capability to a) predict short-term outcome rates and b) compare hospitals' risk-adjusted outcome rates using two risk-adjustment procedures.
  
  
  METHODS
  Observational, retrospective study based on hospital data collected at the regional level.Two cohorts of patients discharged in 2010 from hospitals located in the Lazio Region, Italy: acute myocardial infarction (AMI) and hip fracture (HF). Multivariate logistic regression models were implemented to predict 30-day mortality (AMI) or 48-hour surgery (HF), adjusting for demographic characteristics and comorbidities plus clinical data and drug prescription information. Risk-adjusted outcome rates were derived at the hospital level.
  
  
  RESULTS
  The addition of clinical data and drug prescription information improved the capability of the models to predict the study outcomes for the two conditions investigated. The discriminatory power of the AMI model increases when the clinical data and drug prescription information are included (c-statistic increases from 0.761 to 0.797); for the HF model the increase was more slight (c-statistic increases from 0.555 to 0.574). Some differences were observed between the hospital-adjusted proportion estimated using the two different models. However, the estimated hospital outcome rates were weakly affected by the introduction of clinical data and drug prescription information.
  
  
  CONCLUSIONS
  The results show that the available clinical variables and drug prescription information were important complements to the hospital discharge data for characterising the acute severity of the patients. However, when these variables were used for adjustment purposes their contribution was negligible. This conclusion might not apply at other locations, in other time periods and for other health conditions if there is heterogeneity in the clinical conditions between hospitals.
authors.affiliation: Department of Epidemiology, Regional Health Service, Lazio Region, Via Santa Costanza 53, Rome, 00198, Italy. p.colais@deplazio.it.
authors.forename: Paola
authors.initials: P
authors.lastname: Colais
authors.forename: Mirko
authors.initials: M
authors.lastname: Di Martino
authors.forename: Danilo
authors.initials: D
authors.lastname: Fusco
authors.forename: Marina
authors.initials: M
authors.lastname: Davoli
authors.forename: Paul
authors.initials: P
authors.lastname: Aylin
authors.forename: Carlo Alberto
authors.initials: CA
authors.lastname: Perucci
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D015897:Comorbidity; D011307:Drug Prescriptions / Q000706:statistics & numerical data*; D006620:Hip Fractures / Q000188:drug therapy* / Q000401:mortality*; D017052:Hospital Mortality; D006801:Humans; D007558:Italy / Q000453:epidemiology; D009203:Myocardial Infarction / Q000188:drug therapy* / Q000401:mortality*; D010351:Patient Discharge; D019984:Quality Indicators, Health Care; D012189:Retrospective Studies; D020379:Risk Adjustment; D012307:Risk Factors