title: Multicenter development and validation of a risk stratification tool for ward patients.
abstract: BACKGROUND
  Most ward risk scores were created using subjective opinion in individual hospitals and only use vital signs.
  
  
  OBJECTIVE
  To develop and validate a risk score using commonly collected electronic health record data.
  
  
  METHODS
  All patients hospitalized on the wards in five hospitals were included in this observational cohort study. Discrete-time survival analysis was used to predict the combined outcome of cardiac arrest (CA), intensive care unit (ICU) transfer, or death on the wards. Laboratory results, vital signs, and demographics were used as predictor variables. The model was developed in the first 60% of the data at each hospital and then validated in the remaining 40%. The final model was compared with the Modified Early Warning Score (MEWS) using the area under the receiver operating characteristic curve and the net reclassification index (NRI).
  
  
  RESULTS
  A total of 269,999 patient admissions were included, with 424 CAs, 13,188 ICU transfers, and 2,840 deaths occurring during the study period. The derived model was more accurate than the MEWS in the validation dataset for all outcomes (area under the receiver operating characteristic curve, 0.83 vs. 0.71 for CA; 0.75 vs. 0.68 for ICU transfer; 0.93 vs. 0.88 for death; and 0.77 vs. 0.70 for the combined outcome; P value < 0.01 for all comparisons). This accuracy improvement was seen across all hospitals. The NRI for the electronic Cardiac Arrest Risk Triage compared with the MEWS was 0.28 (0.18-0.38), with a positive NRI of 0.19 (0.09-0.29) and a negative NRI of 0.09 (0.09-0.09).
  
  
  CONCLUSIONS
  We developed an accurate ward risk stratification tool using commonly collected electronic health record variables in a large multicenter dataset. Further study is needed to determine whether implementation in real-time would improve patient outcomes.
authors.affiliation: 1 Department of Medicine and.
authors.forename: Matthew M
authors.initials: MM
authors.lastname: Churpek
authors.forename: Trevor C
authors.initials: TC
authors.lastname: Yuen
authors.forename: Christopher
authors.initials: C
authors.lastname: Winslow
authors.forename: Ari A
authors.initials: AA
authors.lastname: Robicsek
authors.forename: David O
authors.initials: DO
authors.lastname: Meltzer
authors.forename: Robert D
authors.initials: RD
authors.lastname: Gibbons
authors.forename: Dana P
authors.initials: DP
authors.lastname: Edelson
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D015331:Cohort Studies; D061827:Dimensional Measurement Accuracy; D042241:Early Diagnosis; D057286:Electronic Health Records; D005260:Female; D006323:Heart Arrest / Q000401:mortality*; D057209:Hospital Rapid Response Team / Q000706:statistics & numerical data; D006801:Humans; D007297:Inpatients / Q000706:statistics & numerical data*; D007362:Intensive Care Units / Q000706:statistics & numerical data*; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D010360:Patient Transfer / Q000706:statistics & numerical data*; D018570:Risk Assessment / Q000379:methods* / Q000592:standards*; D016019:Survival Analysis
keywords: decision support techniques; early diagnosis; heart arrest; hospital rapid response team; statistical models
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