title: Computer Modelling Using Prehospital Vitals Predicts Transfusion and Mortality.
abstract: Test computer-assisted modeling techniques using prehospital vital signs of injured patients to predict emergency transfusion requirements, number of intensive care days, and mortality, compared to vital signs alone.
  
  
  
  This single-center retrospective analysis of 17,988 trauma patients used vital signs data collected between 2006 and 2012 to predict which patients would receive transfusion, require 3 or more days of intensive care, or die. Standard transmitted prehospital vital signs (heart rate, blood pressure, shock index, and respiratory rate) were used to create a regression model (PH-VS) that was internally validated and evaluated using area under the receiver operating curve (AUROC). Transfusion records were matched with blood bank records. Documentation of death and duration of intensive care were obtained from the trauma registry.
  
  
  
  During the course of their hospital stay, 720 of the 17,988 patients in the study population died (4%), 2,266 (12.6%) required at least a 3-day stay in the intensive care unit (ICU), 1,171 (6.5%) required transfusions, and 210 (1.2%) received massive transfusions. The PH-VS model significantly outperformed any individual vital sign across all outcomes (average AUROC = 0.82), The PH-VS model correctly predicted that 512 of 777 (65.9%) and 580 of 931 (62.3%) patients in the study population would receive transfusions within the first 2 and 6 hours of admission, respectively.
  
  
  
  The predictive ability of individual vital signs to predict outcomes is significantly enhanced with the model. This could support prehospital triage by enhancing decision makers' ability to match critically injured patients with appropriate resources with minimal delays.
authors.forename: Zachary D W
authors.initials: ZD
authors.lastname: Dezman
authors.forename: Eric
authors.initials: E
authors.lastname: Hu
authors.forename: Peter F
authors.initials: PF
authors.lastname: Hu
authors.forename: Shiming
authors.initials: S
authors.lastname: Yang
authors.forename: Lynn G
authors.initials: LG
authors.lastname: Stansbury
authors.forename: Rhonda
authors.initials: R
authors.lastname: Cooke
authors.forename: Raymond
authors.initials: R
authors.lastname: Fang
authors.forename: Catriona
authors.initials: C
authors.lastname: Miller
authors.forename: Colin F
authors.initials: CF
authors.lastname: Mackenzie
mesh_terms: D000328:Adult; D001803:Blood Transfusion / Q000706:statistics & numerical data*; D003198:Computer Simulation; D004632:Emergency Medical Services / Q000379:methods*; D005260:Female; D006339:Heart Rate; D017052:Hospital Mortality; D006801:Humans; D007362:Intensive Care Units; D007902:Length of Stay / Q000706:statistics & numerical data; D008297:Male; D012189:Retrospective Studies; D014218:Triage; D055986:Vital Signs; D014947:Wounds and Injuries / Q000401:mortality / Q000628:therapy*
keywords: mortality; prehospital care; transfusion; vital signs