title: External validation of a prehospital risk score for critical illness.
abstract: Identification of critically ill patients during prehospital care could facilitate early treatment and aid in the regionalization of critical care. Tools to consistently identify those in the field with or at higher risk of developing critical illness do not exist. We sought to validate a prehospital critical illness risk score that uses objective clinical variables in a contemporary cohort of geographically and temporally distinct prehospital encounters.
  
  
  
  We linked prehospital encounters at 21 emergency medical services (EMS) agencies to inpatient electronic health records at nine hospitals in southwestern Pennsylvania from 2010 to 2012. The primary outcome was critical illness during hospitalization, defined as an intensive care unit stay with delivery of organ support (mechanical ventilation or vasopressor use). We calculated the prehospital risk score using demographics and first vital signs from eligible EMS encounters, and we tested the association between score variables and critical illness using multivariable logistic regression. Discrimination was assessed using the AUROC curve, and calibration was determined by plotting observed versus expected events across score values. Operating characteristics were calculated at score thresholds.
  
  
  
  Among 42,550 nontrauma, non-cardiac arrest adult EMS patients, 1926 (4.5 %) developed critical illness during hospitalization. We observed moderate discrimination of the prehospital critical illness risk score (AUROC 0.73, 95 % CI 0.72-0.74) and adequate calibration based on observed versus expected plots. At a score threshold of 2, sensitivity was 0.63 (95 % CI 0.61-0.75), specificity was 0.73 (95 % CI 0.72-0.73), negative predictive value was 0.98 (95 % CI 0.98-0.98), and positive predictive value was 0.10 (95 % CI 0.09-0.10). The risk score performance was greater with alternative definitions of critical illness, including in-hospital mortality (AUROC 0.77, 95 % CI 0.7 -0.78).
  
  
  
  In an external validation cohort, a prehospital risk score using objective clinical data had moderate discrimination for critical illness during hospitalization.
authors: Department of Critical Care Medicine, University of Pittsburgh School of Medicine, 3550 Terrace Street, Scaife Hall #607, Pittsburgh, PA, 15261, USA. kievlandr@upmc.edu.|Clinical Research, Investigation, and Systems Modeling of Acute Illness (CRISMA) Center, Pittsburgh, PA, USA. kievlandr@upmc.edu.
authors: Daniel R
authors: DR
authors: Kievlan
authors: Department of Emergency Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
authors: Christian
authors: C
authors: Martin-Gill
authors: Department of Critical Care Medicine, University of Pittsburgh School of Medicine, 3550 Terrace Street, Scaife Hall #607, Pittsburgh, PA, 15261, USA.|Clinical Research, Investigation, and Systems Modeling of Acute Illness (CRISMA) Center, Pittsburgh, PA, USA.
authors: Jeremy M
authors: JM
authors: Kahn
authors: Department of Emergency Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
authors: Clifton W
authors: CW
authors: Callaway
authors: Department of Emergency Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
authors: Donald M
authors: DM
authors: Yealy
authors: Department of Critical Care Medicine, University of Pittsburgh School of Medicine, 3550 Terrace Street, Scaife Hall #607, Pittsburgh, PA, 15261, USA.|Clinical Research, Investigation, and Systems Modeling of Acute Illness (CRISMA) Center, Pittsburgh, PA, USA.
authors: Derek C
authors: DC
authors: Angus
authors: Department of Critical Care Medicine, University of Pittsburgh School of Medicine, 3550 Terrace Street, Scaife Hall #607, Pittsburgh, PA, 15261, USA.|Clinical Research, Investigation, and Systems Modeling of Acute Illness (CRISMA) Center, Pittsburgh, PA, USA.|Department of Emergency Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
authors: Christopher W
authors: CW
authors: Seymour
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D016009:Chi-Square Distribution; D015331:Cohort Studies; D016638:Critical Illness / Q000145:classification* / Q000628:therapy; D003661:Decision Support Techniques; D057286:Electronic Health Records; D004632:Emergency Medical Services / Q000379:methods*; D005260:Female; D017052:Hospital Mortality; D006760:Hospitalization / Q000706:statistics & numerical data; D006801:Humans; D015601:Injury Severity Score; D007902:Length of Stay / Q000706:statistics & numerical data; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D010414:Pennsylvania; D015203:Reproducibility of Results; D014218:Triage / Q000145:classification / Q000379:methods
keywords: Clinical decision support systems; Critical illness; Emergency medical services; Forecasting; Prognosis; Triage