title: Risk prediction models to predict emergency hospital admission in community-dwelling adults: a systematic review.
abstract: BACKGROUND
  Risk prediction models have been developed to identify those at increased risk for emergency admissions, which could facilitate targeted interventions in primary care to prevent these events.
  
  
  OBJECTIVE
  Systematic review of validated risk prediction models for predicting emergency hospital admissions in community-dwelling adults.
  
  
  METHODS
  A systematic literature review and narrative analysis was conducted. Inclusion criteria were as follows;
  
  
  METHODS
  community-dwelling adults (aged 18 years and above); Risk: risk prediction models, not contingent on an index hospital admission, with a derivation and ≥1 validation cohort;
  
  
  METHODS
  emergency hospital admission (defined as unplanned overnight stay in hospital);
  
  
  METHODS
  retrospective or prospective cohort studies.
  
  
  RESULTS
  Of 18,983 records reviewed, 27 unique risk prediction models met the inclusion criteria. Eleven were developed in the United States, 11 in the United Kingdom, 3 in Italy, 1 in Spain, and 1 in Canada. Nine models were derived using self-report data, and the remainder (n=18) used routine administrative or clinical record data. Total study sample sizes ranged from 96 to 4.7 million participants. Predictor variables most frequently included in models were: (1) named medical diagnoses (n=23); (2) age (n=23); (3) prior emergency admission (n=22); and (4) sex (n=18). Eleven models included nonmedical factors, such as functional status and social supports. Regarding predictive accuracy, models developed using administrative or clinical record data tended to perform better than those developed using self-report data (c statistics 0.63-0.83 vs. 0.61-0.74, respectively). Six models reported c statistics of >0.8, indicating good performance. All 6 included variables for prior health care utilization, multimorbidity or polypharmacy, and named medical diagnoses or prescribed medications. Three predicted admissions regarded as being ambulatory care sensitive.
  
  
  CONCLUSIONS
  This study suggests that risk models developed using administrative or clinical record data tend to perform better. In applying a risk prediction model to a new population, careful consideration needs to be given to the purpose of its use and local factors.
authors.affiliation: *HRB Centre for Primary Care Research, Royal College of Surgeons in Ireland, Dublin 2 †School of Medicine, University of Limerick, Limerick ‡Department of Pharmacology and Therapeutics, St James's Hospital, Dublin 8, Ireland.
authors.forename: Emma
authors.initials: E
authors.lastname: Wallace
authors.forename: Ellen
authors.initials: E
authors.lastname: Stuart
authors.forename: Niall
authors.initials: N
authors.lastname: Vaughan
authors.forename: Kathleen
authors.initials: K
authors.lastname: Bennett
authors.forename: Tom
authors.initials: T
authors.lastname: Fahey
authors.forename: Susan M
authors.initials: SM
authors.lastname: Smith
mesh_terms: D000328:Adult; D000367:Age Factors; D000368:Aged; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data*; D005260:Female; D006296:Health Services / Q000706:statistics & numerical data; D006302:Health Services Research; D006304:Health Status; D006801:Humans; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D010343:Patient Admission / Q000706:statistics & numerical data*; D012111:Residence Characteristics / Q000706:statistics & numerical data*; D018570:Risk Assessment; D012307:Risk Factors; D012737:Sex Factors; D012944:Social Support; D012959:Socioeconomic Factors; D055815:Young Adult