title: Would triage predictors perform better than first-come, first-served in pandemic ventilator allocation?
abstract: BACKGROUND
  In a pandemic, needs for ventilators might overwhelm the limited supply. Outcome predictors have been proposed to guide ventilator triage allocation decisions. However, pandemic triage predictors have not been validated. This quantitative simulation study evaluated outcomes resulting from allocation strategies varying in their performance for selecting short-stay survivors as favorable candidates for ventilators.
  
  
  METHODS
  A quantitative simulation modeled a pandemic surge. Postulated numbers of potential daily admissions presented randomly from a specified population, with a limited number of available ventilators. Patients were triaged to ventilator care vs palliation or turned away to palliation if no ventilator was available. Simulated triage was conducted according to a set of hypothetical triage tools varying in sensitivity and specificity to select favorable ventilator candidates vs first-come, first-served allocation. Death was assumed for palliation. Survival or death was counted for patients who were ventilated according to the specified characteristic of each randomly selected patient.
  
  
  RESULTS
  Triage predictors with intermediate-quality performance resulted in a median daily mortality of 80%, similar to first-come, first-served allocation. A poor-quality predictor resulted in a worse mortality of 90%. Only a high-quality predictor (sensitivity 90%, specificity 90%) resulted in a substantially lower 60% mortality.
  
  
  CONCLUSIONS
  Performance of unvalidated pandemic ventilator triage predictors is unknown and possibly inferior to first-come, first-served allocation. Poor performance of unvalidated predictors proposed for triage would represent an inadequate plan for stewarding scarce resources and would deprive some patients of fair access to a ventilator, thus falling short of sound ethical foundations.
authors: Department of Pediatric Critical Care Medicine, SUNY Upstate Medical University, Syracuse; and the National Center for Disaster Preparedness, Columbia University, New York, NY.. Electronic address: kanterr@upstate.edu.
authors: Robert K
authors: RK
authors: Kanter
mesh_terms: D003198:Computer Simulation; D003422:Critical Care / Q000706:statistics & numerical data*; D006801:Humans; D053118:Influenza A Virus, H1N1 Subtype; D007251:Influenza, Human / Q000453:epidemiology* / Q000821:virology; D058873:Pandemics; D011379:Prognosis; D015996:Survival Rate / Q000639:trends; D014218:Triage / Q000379:methods*; D014481:United States / Q000453:epidemiology; D012122:Ventilators, Mechanical / Q000009:adverse effects*