title: Predicting risk of hospitalization or death among patients receiving primary care in the Veterans Health Administration.
abstract: BACKGROUND
  Statistical models that identify patients at elevated risk of death or hospitalization have focused on population subsets, such as those with a specific clinical condition or hospitalized patients. Most models have limitations for clinical use. Our objective was to develop models that identified high-risk primary care patients.
  
  
  METHODS
  Using the Primary Care Management Module in the Veterans Health Administration (VHA)'s Corporate Data Warehouse, we identified all patients who were enrolled and assigned to a VHA primary care provider on October 1, 2010. The outcome variable was the occurrence of hospitalization or death during the subsequent 90 days and 1 year. We extracted predictors from 6 categories: sociodemographics, medical conditions, vital signs, prior year use of health services, medications, and laboratory tests and then constructed multinomial logistic regression models to predict outcomes for over 4.6 million patients.
  
  
  RESULTS
  In the predicted 95th risk percentiles, observed 90-day event rates were 19.6%, 6.2%, and 22.6%, respectively, for hospitalization, death, and either hospitalization or death, compared with population averages of 2.7%, 0.7%, and 3.4%, respectively; 1-year event rates were 42.3%, 19.4%, and 51.3%, respectively, compared with population averages of 8.2%, 2.6%, and 10.8%, respectively. The C-statistics for 90-day outcomes were 0.83, 0.86, and 0.81, respectively, for hospitalization, death, and either hospitalization or death and were 0.81, 0.85, and 0.79, respectively, for 1-year outcomes.
  
  
  CONCLUSIONS
  Prediction models using electronic clinical data accurately identified patients with elevated risk for hospitalization or death. This information can enhance the coordination of care for patients with complex clinical conditions.
authors.affiliation: VA Puget Sound Health Care System, Seattle, WA 98101, USA.
authors.forename: Li
authors.initials: L
authors.lastname: Wang
authors.forename: Brian
authors.initials: B
authors.lastname: Porter
authors.forename: Charles
authors.initials: C
authors.lastname: Maynard
authors.forename: Ginger
authors.initials: G
authors.lastname: Evans
authors.forename: Christopher
authors.initials: C
authors.lastname: Bryson
authors.forename: Haili
authors.initials: H
authors.lastname: Sun
authors.forename: Indra
authors.initials: I
authors.lastname: Gupta
authors.forename: Elliott
authors.initials: E
authors.lastname: Lowy
authors.forename: Mary
authors.initials: M
authors.lastname: McDonell
authors.forename: Kathleen
authors.initials: K
authors.lastname: Frisbee
authors.forename: Christopher
authors.initials: C
authors.lastname: Nielson
authors.forename: Fred
authors.initials: F
authors.lastname: Kirkland
authors.forename: Stephan D
authors.initials: SD
authors.lastname: Fihn
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D057286:Electronic Health Records; D005260:Female; D005544:Forecasting; D017052:Hospital Mortality / Q000639:trends*; D006760:Hospitalization / Q000706:statistics & numerical data*; D006787:Hospitals, Veterans; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D011320:Primary Health Care / Q000706:statistics & numerical data*; D012189:Retrospective Studies; D018570:Risk Assessment; D016019:Survival Analysis; D014481:United States; D014493:United States Department of Veterans Affairs / Q000706:statistics & numerical data*; D014728:Veterans / Q000706:statistics & numerical data*; D055815:Young Adult