title: Development and validation of a model for predicting emergency admissions over the next year (PEONY): a UK historical cohort study.
abstract: BACKGROUND
  Current international health policy has emphasized the importance of managing long-term conditions in the community with the aim of preventing emergency hospitalizations. Previous algorithms and rules have been developed but are limited to those older than 65 years and generally only for readmission. Our aim was to develop an algorithm to predict emergency hospital admissions in the whole population of those 40 years or older.
  
  
  METHODS
  The design was a historical cohort observational study from 1996 to 2004 with at least 1 year of follow-up and split-half validation, set in the population of Tayside, Scotland (n = 410 000). Participants were 40 years or older with a 3-year history of prescribed drugs and hospital admissions. The main outcome measure was first emergency hospital admission in the following year, analyzed using logistic regression.
  
  
  RESULTS
  A total of 186 523 subjects 40 years or older were identified at baseline. A derivation data set (n = 90 522) yielded 6793 participants (7.5%) who experienced an emergency hospital admission in the following year. Strong predictors of admissions were age; being male; high social deprivation; previously prescribed analgesics, antibacterials, nitrates, and diuretics; the number of respiratory medications; and the number of previous admissions and previous total bed-days. Discriminatory power was good (c statistic, 0.80) and split-half validation gave good calibration, especially for the highest decile of risk.
  
  
  CONCLUSIONS
  A population-derived algorithm provided the first easy-to-use algorithm, to our knowledge, to predict future emergency admissions in all individuals 40 years or older. The model can be implemented at individual patient level as well as family practice level to target case management.
authors.affiliation: Epidemiology and Biostatistics, Tayside Centre for General Practice, Community Health Sciences, University of Dundee, Dundee, Scotland. p.t.donnan@chs.dundee.ac.uk
authors.forename: Peter T
authors.initials: PT
authors.lastname: Donnan
authors.forename: David W T
authors.initials: DW
authors.lastname: Dorward
authors.forename: Bill
authors.initials: B
authors.lastname: Mutch
authors.forename: Andrew D
authors.initials: AD
authors.lastname: Morris
mesh_terms: D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D015331:Cohort Studies; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data / Q000639:trends; D004638:Emergency Treatment / Q000706:statistics & numerical data*; D005260:Female; D005500:Follow-Up Studies; D006760:Hospitalization / Q000706:statistics & numerical data*; D006801:Humans; D015994:Incidence; D016014:Linear Models; D008297:Male; D008875:Middle Aged; D017146:Models, Organizational; D010343:Patient Admission / Q000706:statistics & numerical data*; D010359:Patient Readmission / Q000706:statistics & numerical data; D011237:Predictive Value of Tests; D012307:Risk Factors; D012680:Sensitivity and Specificity; D012737:Sex Factors; D006113:United Kingdom