To develop and validate an algorithm to guide selection of patients for pediatric critical care admission during a severe pandemic when Crisis Standards of Care are implemented.Design:
Retrospective observational study using secondary data.Patients:
Children admitted to VPS-participating PICUs between 2009–2012.Interventions:
A total of 111,174 randomly selected nonelective cases from the Virtual PICU Systems database were used to estimate each patient’s probability of death and duration of ventilation employing previously derived predictive equations. Using real and projected statistics for the State of Ohio as an example, triage thresholds were established for casualty volumes ranging from 5,000 to 10,000 for a modeled pandemic with peak duration of 6 weeks and 280 pediatric intensive care beds. The goal was to simultaneously maximize casualty survival and bed occupancy. Discrete Event Simulation was used to determine triage thresholds for probability of death and duration of ventilation as a function of casualty volume and the total number of available beds. Simulation was employed to compare survival between the proposed triage algorithm and a first come first served distribution of scarce resources.Measurements and Main Results:
Population survival was greater using the triage thresholds compared with a first come first served strategy. In this model, for five, six, seven, eight, and 10 thousand casualties, the triage algorithm increased the number of lives saved by 284, 386, 547, 746, and 1,089, respectively, compared with first come first served (all p < 0.001).Conclusions:
Use of triage thresholds based on probability of death and duration of mechanical ventilation determined from actual critically ill children’s data demonstrated superior population survival during a simulated overwhelming pandemic.