title: Predicting Patients at Risk for 3-Day Postdischarge Readmissions, ED Visits, and Deaths.
abstract: BACKGROUND
  Transitional care interventions can be utilized to reduce post-hospital discharge adverse events (AEs). However, no methodology exists to effectively identify high-risk patients of any disease across multiple hospital sites and patient populations for short-term postdischarge AEs.
  
  
  OBJECTIVE
  To develop and validate a 3-day (72 h) AEs prediction model using electronic health records data available at the time of an indexed discharge.
  
  
  METHODS
  Retrospective cohort study of admissions between June 2012 and June 2014.
  
  
  METHODS
  All adult inpatient admissions (excluding in-hospital deaths) from a large multicenter hospital system.
  
  
  METHODS
  All-cause 3-day unplanned readmissions, emergency department (ED) visits, and deaths (REDD). The REDD model was developed using clinical, administrative, and socioeconomic data, with data preprocessing steps and stacked classification. Patients were divided randomly into training (66.7%), and testing (33.3%) cohorts to avoid overfitting.
  
  
  RESULTS
  The derivation cohort comprised of 64,252 admissions, of which 2782 (4.3%) admissions resulted in 3-day AEs and 13,372 (20.8%) in 30-day AEs. The c-statistic (also known as area under the receiver operating characteristic curve) of 3-day REDD model was 0.671 and 0.664 for the derivation and validation cohort, respectively. The c-statistic of 30-day REDD model was 0.713 and 0.711 for the derivation and validation cohort, respectively.
  
  
  CONCLUSIONS
  The 3-day REDD model predicts high-risk patients with fair discriminative power. The discriminative power of the 30-day REDD model is also better than the previously reported models under similar settings. The 3-day REDD model has been implemented and is being used to identify patients at risk for AEs.
authors.affiliation: *Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, University Park †Geisinger Health System, Danville, PA.
authors.forename: Deepak
authors.initials: D
authors.lastname: Agrawal
authors.forename: Cheng-Bang
authors.initials: CB
authors.lastname: Chen
authors.forename: Ronald W
authors.initials: RW
authors.lastname: Dravenstott
authors.forename: Christopher T B
authors.initials: CT
authors.lastname: Strömblad
authors.forename: John Andrew
authors.initials: JA
authors.lastname: Schmid
authors.forename: Jonathan D
authors.initials: JD
authors.lastname: Darer
authors.forename: Priyantha
authors.initials: P
authors.lastname: Devapriya
authors.forename: Soundar
authors.initials: S
authors.lastname: Kumara
mesh_terms: D004636:Emergency Service, Hospital / Q000706:statistics & numerical data*; D005260:Female; D006801:Humans; D007902:Length of Stay / Q000706:statistics & numerical data; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D009026:Mortality; D010351:Patient Discharge / Q000706:statistics & numerical data; D010359:Patient Readmission / Q000706:statistics & numerical data*; D010414:Pennsylvania / Q000453:epidemiology; D012189:Retrospective Studies; D012307:Risk Factors; D012959:Socioeconomic Factors