title: Risk-adjusting hospital inpatient mortality using automated inpatient, outpatient, and laboratory databases.
abstract: OBJECTIVE
  To develop a risk-adjustment methodology that maximizes the use of automated physiology and diagnosis data from the time period preceding hospitalization.
  
  
  METHODS
  : Retrospective cohort study using split-validation and logistic regression.
  
  
  METHODS
  Seventeen hospitals in a large integrated health care delivery system.
  
  
  METHODS
  Patients (n = 259,699) hospitalized between January 2002 and June 2005.
  
  
  METHODS
  Inpatient and 30-day mortality.
  
  
  RESULTS
  Inpatient mortality was 3.50%; 30-day mortality was 4.06%. We tested logistic regression models in a randomly chosen derivation dataset consisting of 50% of the records and applied their coefficients to the validation dataset. The final model included sex, age, admission type, admission diagnosis, a Laboratory-based Acute Physiology Score (LAPS), and a COmorbidity Point Score (COPS). The LAPS integrates information from 14 laboratory tests obtained in the 24 hours preceding hospitalization into a single continuous variable. Using Diagnostic Cost Groups software, we categorized patients as having up to 40 different comorbidities based on outpatient and inpatient data from the 12 months preceding hospitalization. The COPS integrates information regarding these 41 comorbidities into a single continuous variable. Our best model for inpatient mortality had a c statistic of 0.88 in the validation dataset, whereas the c statistic for 30-day mortality was 0.86; both models had excellent calibration. Physiologic data accounted for a substantial proportion of the model's predictive ability.
  
  
  CONCLUSIONS
  Efforts to support improvement of hospital outcomes can take advantage of risk-adjustment methods based on automated physiology and diagnosis data that are not confounded by information obtained after hospital admission.
authors.affiliation: Kaiser Permanente Division of Research, Systems Research Initiative and Perinatal Research Unit, Oakland, California 94612, USA. gabriel.escobar@kp.org
authors.forename: Gabriel J
authors.initials: GJ
authors.lastname: Escobar
authors.forename: John D
authors.initials: JD
authors.lastname: Greene
authors.forename: Peter
authors.initials: P
authors.lastname: Scheirer
authors.forename: Marla N
authors.initials: MN
authors.lastname: Gardner
authors.forename: David
authors.initials: D
authors.lastname: Draper
authors.forename: Patricia
authors.initials: P
authors.lastname: Kipnis
mesh_terms: D000368:Aged; D015331:Cohort Studies; D005260:Female; D006739:Hospital Administration; D006751:Hospital Information Systems / Q000458:organization & administration*; D017052:Hospital Mortality; D006801:Humans; D007755:Laboratories, Hospital / Q000458:organization & administration*; D016015:Logistic Models; D008297:Male; D016347:Medical Records Systems, Computerized / Q000458:organization & administration; D008875:Middle Aged; D019984:Quality Indicators, Health Care / Q000458:organization & administration*; D015203:Reproducibility of Results; D012189:Retrospective Studies; D020379:Risk Adjustment / Q000458:organization & administration*; D012720:Severity of Illness Index