title: Risk Factors for Emergency Department Short Time Readmission in Stratified Population.
abstract: BACKGROUND
  Emergency department (ED) readmissions are considered an indicator of healthcare quality that is particularly relevant in older adults. The primary objective of this study was to identify key factors for predicting patients returning to the ED within 30 days of being discharged.
  
  
  METHODS
  We analysed patients who attended our ED in June 2014, stratified into four groups based on the Kaiser pyramid. We collected data on more than 100 variables per case including demographic and clinical characteristics and drug treatments. We identified the variables with the highest discriminating power to predict ED readmission and constructed classifiers using machine learning methods to provide predictions.
  
  
  RESULTS
  Classifier performance distinguishing between patients who were and were not readmitted (within 30 days), in terms of average accuracy (AC). The variables with the greatest discriminating power were age, comorbidity, reasons for consultation, social factors, and drug treatments.
  
  
  CONCLUSIONS
  It is possible to predict readmissions in stratified groups with high accuracy and to identify the most important factors influencing the event. Therefore, it will be possible to develop interventions to improve the quality of care provided to ED patients.
authors.affiliation: Emergency Department, Álava University Hospital, 01010 Vitoria, Spain ; Biomedical Research Networking Center in Mental Health (CIBERSAM), 10001 Madrid, Spain ; Faculty of Medicine, University of the Basque Country (UPV/EHU), 01010 Vitoria, Spain.
authors.forename: Ariadna
authors.initials: A
authors.lastname: Besga
authors.affiliation: Computational Intelligence Group (GIC), UPV/EHU, 20018 San Sebastián, Spain ; ACPySS, 20018 San Sebastián, Spain.
authors.forename: Borja
authors.initials: B
authors.lastname: Ayerdi
authors.affiliation: Management, Álava University Hospital, 01010 Vitoria, Spain.
authors.forename: Guillermo
authors.initials: G
authors.lastname: Alcalde
authors.affiliation: Management, Álava University Hospital, 01010 Vitoria, Spain.
authors.forename: Alberto
authors.initials: A
authors.lastname: Manzano
authors.affiliation: Emergency Department, Álava University Hospital, 01010 Vitoria, Spain.
authors.forename: Pedro
authors.initials: P
authors.lastname: Lopetegui
authors.affiliation: Computational Intelligence Group (GIC), UPV/EHU, 20018 San Sebastián, Spain ; ACPySS, 20018 San Sebastián, Spain.
authors.forename: Manuel
authors.initials: M
authors.lastname: Graña
authors.affiliation: Biomedical Research Networking Center in Mental Health (CIBERSAM), 10001 Madrid, Spain ; Faculty of Medicine, University of the Basque Country (UPV/EHU), 01010 Vitoria, Spain ; Department of Psychiatry, Álava University Hospital, 01010 Vitoria, Spain.
authors.forename: Ana
authors.initials: A
authors.lastname: González-Pinto
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D015897:Comorbidity; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data*; D005260:Female; D006801:Humans; D007902:Length of Stay / Q000706:statistics & numerical data; D008297:Male; D010351:Patient Discharge / Q000706:statistics & numerical data; D010359:Patient Readmission / Q000706:statistics & numerical data*; D011787:Quality of Health Care / Q000706:statistics & numerical data; D018570:Risk Assessment / Q000379:methods; D012307:Risk Factors; D013997:Time Factors