title: Readmissions and death after ICU discharge: development and validation of two predictive models.
abstract: BACKGROUND
  Early discharge from the ICU is desirable because it shortens time in the ICU and reduces care costs, but can also increase the likelihood of ICU readmission and post-discharge unanticipated death if patients are discharged before they are stable. We postulated that, using eICU® Research Institute (eRI) data from >400 ICUs, we could develop robust models predictive of post-discharge death and readmission that may be incorporated into future clinical information systems (CIS) to assist ICU discharge planning.
  
  
  METHODS
  Retrospective, multi-center, exploratory cohort study of ICU survivors within the eRI database between 1/1/2007 and 3/31/2011.
  
  
  METHODS
  DNR or care limitations at ICU discharge and discharge to location external to hospital. Patients were randomized (2∶1) to development and validation cohorts. Multivariable logistic regression was performed on a broad range of variables including: patient demographics, ICU admission diagnosis, admission severity of illness, laboratory values and physiologic variables present during the last 24 hours of the ICU stay. Multiple imputation was used to address missing data. The primary outcomes were the area under the receiver operator characteristic curves (auROC) in the validation cohorts for the models predicting readmission and death within 48 hours of ICU discharge.
  
  
  RESULTS
  469,976 and 234,987 patients representing 219 hospitals were in the development and validation cohorts. Early ICU readmission and death was experienced by 2.54% and 0.92% of all patients, respectively. The relationship between predictors and outcomes (death vs readmission) differed, justifying the need for separate models. The models for early readmission and death produced auROCs of 0.71 and 0.92, respectively. Both models calibrated well across risk groups.
  
  
  CONCLUSIONS
  Our models for death and readmission after ICU discharge showed good to excellent discrimination and good calibration. Although prospective validation is warranted, we speculate that these models may have value in assisting clinicians with ICU discharge planning.
authors.affiliation: Research & Product Innovation, Philips Healthcare, Baltimore, Maryland, USA. omar.badawi@philips.com
authors.forename: Omar
authors.initials: O
authors.lastname: Badawi
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Breslow
mesh_terms: D000328:Adult; D000368:Aged; D003661:Decision Support Techniques; D005544:Forecasting; D006801:Humans; D007362:Intensive Care Units; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D009026:Mortality / Q000639:trends; D010351:Patient Discharge; D010359:Patient Readmission / Q000639:trends*; D012189:Retrospective Studies