title: The value of vital sign trends for detecting clinical deterioration on the wards.
abstract: OBJECTIVE
  Early detection of clinical deterioration on the wards may improve outcomes, and most early warning scores only utilize a patient's current vital signs. The added value of vital sign trends over time is poorly characterized. We investigated whether adding trends improves accuracy and which methods are optimal for modelling trends.
  
  
  METHODS
  Patients admitted to five hospitals over a five-year period were included in this observational cohort study, with 60% of the data used for model derivation and 40% for validation. Vital signs were utilized to predict the combined outcome of cardiac arrest, intensive care unit transfer, and death. The accuracy of models utilizing both the current value and different trend methods were compared using the area under the receiver operating characteristic curve (AUC).
  
  
  RESULTS
  A total of 269,999 patient admissions were included, which resulted in 16,452 outcomes. Overall, trends increased accuracy compared to a model containing only current vital signs (AUC 0.78 vs. 0.74; p<0.001). The methods that resulted in the greatest average increase in accuracy were the vital sign slope (AUC improvement 0.013) and minimum value (AUC improvement 0.012), while the change from the previous value resulted in an average worsening of the AUC (change in AUC -0.002). The AUC increased most for systolic blood pressure when trends were added (AUC improvement 0.05).
  
  
  CONCLUSIONS
  Vital sign trends increased the accuracy of models designed to detect critical illness on the wards. Our findings have important implications for clinicians at the bedside and for the development of early warning scores.
authors.affiliation: Department of Medicine, University of Chicago, Chicago, IL, United States. Electronic address: matthew.churpek@uchospitals.edu.
authors.forename: Matthew M
authors.initials: MM
authors.lastname: Churpek
authors.affiliation: Department of Medicine, University of Chicago, Chicago, IL, United States.
authors.forename: Richa
authors.initials: R
authors.lastname: Adhikari
authors.affiliation: Department of Medicine, University of Chicago, Chicago, IL, United States.
authors.forename: Dana P
authors.initials: DP
authors.lastname: Edelson
mesh_terms: D016638:Critical Illness; D042241:Early Diagnosis; D005260:Female; D005500:Follow-Up Studies; D006323:Heart Arrest / Q000175:diagnosis* / Q000453:epidemiology / Q000628:therapy; D057209:Hospital Rapid Response Team / Q000592:standards*; D006801:Humans; D015994:Incidence; D007362:Intensive Care Units; D008297:Male; D008875:Middle Aged; D012372:ROC Curve; D012189:Retrospective Studies; D018570:Risk Assessment / Q000379:methods*; D015996:Survival Rate / Q000639:trends; D013997:Time Factors; D014481:United States / Q000453:epidemiology; D055986:Vital Signs
keywords: Decision support techniques; Early diagnosis; Heart arrest; Hospital rapid response team; Physiologic monitoring; Vital signs