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Pediatric Triage in a Severe Pandemic: Maximizing Survival by Establishing Triage Thresholds
Christine Gall1, Randall Wetzel, Alexander Kolker
11Virtual PICU Systems, LLC, Los Angeles, CA.2Department of Pediatrics and Anesthesiology, Children's Hospital Los Angeles, USC Keck School of Medicine, Los Angeles, CA.3API Healthcare, A GE Healthcare Company, Hartford, WI.4Department of Pediatrics, Virginia Tech Carilion School of Medicine, Roanoke, VA.5National Center for Disaster Preparedness, Columbia University, New York, NY.6Case Western University School of Medicine, Cleveland, OH.7Rainbow Babies and Children's Hospital, Cleveland, OH.
Insights
A new algorithm using probability of death and ventilation duration can improve survival during a pandemic. This pediatric critical care triage method saved more lives than a first-come, first-served approach in simulations.
Area of Science:
- Pediatric critical care medicine
- Public health preparedness
- Health services research
Background:
- Severe pandemics necessitate Crisis Standards of Care (CSC) for resource allocation.
- Pediatric intensive care units (PICUs) face difficult decisions during mass casualty events.
- Existing triage methods may not optimize survival in pediatric populations during pandemics.
Purpose of the Study:
- To develop and validate a novel algorithm for guiding pediatric critical care admission during a pandemic.
- To establish triage thresholds based on patient-specific data to maximize survival and resource utilization.
- To compare the effectiveness of the proposed algorithm against a first-come, first-served (FCFS) strategy.
Main Methods:
- Retrospective observational study using a large dataset (111,174 cases) from the Virtual PICU Systems database (2009-2012).
- Utilized previously derived predictive equations to estimate in-hospital mortality and ventilation duration for each patient.
- Employed Discrete Event Simulation (DES) to model pandemic scenarios and determine optimal triage thresholds for probability of death and ventilation duration, balancing survival and bed occupancy.
Main Results:
- The developed triage algorithm significantly increased population survival compared to a FCFS strategy across various casualty volumes (5,000-10,000).
- In simulated scenarios, the algorithm demonstrated substantial improvements in lives saved, ranging from 284 to 1,089 additional survivors.
- All comparisons showed statistically significant improvements (p < 0.001), highlighting the algorithm's effectiveness.
Conclusions:
- Triage thresholds derived from real-world data on critically ill children can enhance population survival during overwhelming pandemic events.
- The algorithm, based on probability of death and mechanical ventilation duration, offers a superior approach to resource allocation in pediatric critical care during crises.
- This validated algorithm provides a data-driven framework for critical care triage, improving outcomes when resources are scarce.
Objectives:
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.
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