Does triage to critical care during a pandemic necessarily result in more survivors?
OBJECTIVE
The 2009 H1N1 pandemic reinforced the need for a planned response to increased demand for critical care. Triage protocols have been proposed incorporating the exclusion of specified subgroups of patients from critical care. There have been no studies that explore the theoretical underpinning of triage at referral, and it is not clear under what circumstances triage would confer the intended benefits. We sought to explore the mechanisms whereby triage could lead to fewer deaths across a critical care population in the context of a pandemic.


METHODS
We constructed a mathematical model based on queuing theory to compare the estimated short-term survival achieved by using a critical care service with and without triage at referral. Illustrative scenarios concerning a hypothetical critical care population were constructed to explore the roles of length of stay and critical care survival in determining the impact of triage and to identify "tipping points" of demand at which triage would result in more survivors.


METHODS
Not applicable as this was a data-free mathematical modeling exercise.


RESULTS
We identified circumstances in which triage would be expected to result in more survivors and circumstances in which it would not. In some scenarios, excluding patient groups solely on the basis of anticipated length of stay could be effective due to a more efficient use of critical care bed days.


CONCLUSIONS
The impact of triage is dependent on the level of demand and on the scale of achievable differences between included and excluded groups in terms of anticipated length of stay and critical care survival. It cannot be assumed that triage can or will result in fewer deaths. It should be remembered that there are considerations other than population-level short-term survival when determining the objectives of triage and its ethical implementation.
Clinical Operational Research Unit, University College London, London, United Kingdom. m.utley@ucl.ac.uk
Martin
M
Utley
Christina
C
Pagel
Mark J
MJ
Peters
Andy
A
Petros
Paula
P
Lister
D002423:Cause of Death; D003422:Critical Care / Q000379:methods*; D016638:Critical Illness / Q000401:mortality / Q000628:therapy; D005260:Female; D006285:Health Planning / Q000458:organization & administration; D017052:Hospital Mortality; D006801:Humans; D053118:Influenza A Virus, H1N1 Subtype / Q000302:isolation & purification; D007251:Influenza, Human / Q000175:diagnosis* / Q000401:mortality* / Q000628:therapy; D007362:Intensive Care Units; D007902:Length of Stay; D008297:Male; D008962:Models, Theoretical; D058873:Pandemics / Q000706:statistics & numerical data*; D012016:Reference Values; D012017:Referral and Consultation / Q000706:statistics & numerical data; D016019:Survival Analysis; D013997:Time Factors; D014218:Triage; D006113:United Kingdom