title: Choosing a model to predict hospital admission: an observational study of new variants of predictive models for case finding.
abstract: OBJECTIVE
  To test the performance of new variants of models to identify people at risk of an emergency hospital admission. We compared (1) the impact of using alternative data sources (hospital inpatient, A&E, outpatient and general practitioner (GP) electronic medical records) (2) the effects of local calibration on the performance of the models and (3) the choice of population denominators.
  
  
  METHODS
  Multivariate logistic regressions using person-level data adding each data set sequentially to test value of additional variables and denominators.
  
  
  METHODS
  5 Primary Care Trusts within England.
  
  
  METHODS
  1 836 099 people aged 18-95 registered with GPs on 31 July 2009.
  
  
  METHODS
  Models to predict hospital admission and readmission were compared in terms of the positive predictive value and sensitivity for various risk strata and with the receiver operating curve C statistic.
  
  
  RESULTS
  The addition of each data set showed moderate improvement in the number of patients identified with little or no loss of positive predictive value. However, even with inclusion of GP electronic medical record information, the algorithms identified only a small number of patients with no emergency hospital admissions in the previous 2 years. The model pooled across all sites performed almost as well as the models calibrated to local data from just one site. Using population denominators from GP registers led to better case finding.
  
  
  CONCLUSIONS
  These models provide a basis for wider application in the National Health Service. Each of the models examined produces reasonably robust performance and offers some predictive value. The addition of more complex data adds some value, but we were unable to conclude that pooled models performed less well than those in individual sites. Choices about model should be linked to the intervention design. Characteristics of patients identified by the algorithms provide useful information in the design/costing of intervention strategies to improve care coordination/outcomes for these patients.
authors: Robert F Wagner Graduate School of Public Service, New York University, New York, New York, USA.
authors: John
authors: J
authors: Billings
authors: Theo
authors: T
authors: Georghiou
authors: Ian
authors: I
authors: Blunt
authors: Martin
authors: M
authors: Bardsley
references: BMJ. 2006 Aug 12;333(7563):327
references: 16815882
references: BMJ. 2011 Nov 22;343:d6608
references: 22110252
references: J Urban Health. 2009 Mar;86(2):230-41
references: 19082899
references: J Chronic Dis. 1987;40(5):373-83
references: 3558716
references: Health Aff (Millwood). 2007 Nov-Dec;26(6):1643-54
references: 17978384
references: J Public Health (Oxf). 2013 Dec;35(4):616-23
references: 23440706
references: JAMA. 2011 Oct 19;306(15):1688-98
references: 22009101
references: JAMA. 2013 Jun 26;309(24):2572-8
references: 23797716