title: Early detection of impending physiologic deterioration among patients who are not in intensive care: development of predictive models using data from an automated electronic medical record.
abstract: BACKGROUND
  Ward patients who experience unplanned transfer to intensive care units have excess morbidity and mortality.
  
  
  OBJECTIVE
  To develop a predictive model for prediction of unplanned transfer from the medical-surgical ward to intensive care (or death on the ward in a patient who was "full code") using data from a comprehensive inpatient electronic medical record (EMR).
  
  
  METHODS
  Retrospective case-control study; unit of analysis was a 12-hour patient shift. Shifts where a patient experienced an unplanned transfer were event shifts; shifts without a transfer were comparison shifts. Hospitalization records were transformed into 12-hour shift records, with 10 randomly selected comparison shifts identified for each event shift. Analysis employed logistic regression and split validation.
  
  
  METHODS
  Integrated healthcare delivery system in Northern California.
  
  
  METHODS
  Hospitalized adults at 14 hospitals with comprehensive inpatient EMRs.
  
  
  METHODS
  Predictors included vital signs, laboratory test results, severity of illness scores, longitudinal chronic illness burden scores, transpired hospital length of stay, and care directives. Patients were also given a retrospective, electronically (not manually assigned) Modified Early Warning Score, or MEWS(re). Outcomes were transfer to the intensive care unit (ICU) from the ward or transitional care unit, or death outside the ICU among patients who were "full code".
  
  
  RESULTS
  We identified 4,036 events and 39,782 comparison shifts from a cohort of 102,422 patients' hospitalizations. The MEWS(re) had a c-statistic of 0.709 in the derivation and 0.698 in the validation dataset; corresponding values for the EMR-based model were 0.845 and 0.775.
  
  
  CONCLUSIONS
  Using these algorithms requires hospitals with comprehensive inpatient EMRs and longitudinal data.
  
  
  CONCLUSIONS
  EMR-based detection of impending deterioration outside the ICU is feasible in integrated healthcare delivery systems.
authors.affiliation: Hospital Operations Research, Division of Research, Kaiser Permanente Medical Care Program, Oakland, California 94612, USA. gabriel.escobar@kp.org
authors.forename: Gabriel J
authors.initials: GJ
authors.lastname: Escobar
authors.forename: Juan Carlos
authors.initials: JC
authors.lastname: LaGuardia
authors.forename: Benjamin J
authors.initials: BJ
authors.lastname: Turk
authors.forename: Arona
authors.initials: A
authors.lastname: Ragins
authors.forename: Patricia
authors.initials: P
authors.lastname: Kipnis
authors.forename: David
authors.initials: D
authors.lastname: Draper
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001331:Automation / Q000379:methods; D016022:Case-Control Studies; D003422:Critical Care; D042241:Early Diagnosis; D057286:Electronic Health Records / Q000706:statistics & numerical data* / Q000639:trends*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D011237:Predictive Value of Tests; D012189:Retrospective Studies; D012720:Severity of Illness Index; D013223:Statistics as Topic / Q000379:methods