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Bayesian modeling framework for optimizing pre-hospital stroke triage decisions
Uche Nwoke1, Mudassir Farooqui2, Jacob Oleson1
1Department of Biostatistics, College of Public Health, University of Iowa, Iowa City, IA, USA.
Journal of Applied Statistics
|January 15, 2025
Summary
Bayesian modeling optimizes stroke triage decisions, especially in rural areas. This framework accounts for diagnostic and therapeutic uncertainty, improving patient outcomes by refining transport choices for time-sensitive interventions like intravenous tissue plasminogen activator (IVT) and endovascular thrombectomy (EVT).
Area of Science:
- Neurology
- Biostatistics
- Health Services Research
Background:
- Ischemic stroke causes significant global morbidity and mortality.
- Optimizing stroke treatment necessitates rapid triage decisions by emergency medical personnel.
- Hospital capabilities for time-sensitive interventions (pharmaceutical and surgical) vary, impacting triage complexity, particularly in rural settings.
Purpose of the Study:
- To explore a Bayesian modeling framework for optimizing stroke patient triage decisions.
- To demonstrate how Bayesian techniques can incorporate diagnostic and therapeutic uncertainty into triage.
- To contextualize stroke triage decisions at a fine-grained spatial scale.
Main Methods:
- Utilized a Bayesian modeling framework to address complex triage decision-making.
- Accounted for diagnostic and therapeutic uncertainties inherent in stroke care.
- Applied the modeling approach using data from the Virtual International Stroke Trials Archive (VISTA) in Iowa.
Main Results:
- The Bayesian framework effectively models decision-making under uncertainty.
- Demonstrated the ability to contextualize triage decisions spatially.
- Showcased practical application in a real-world setting (Iowa) using VISTA data.
Conclusions:
- Bayesian modeling offers a robust approach to optimizing emergency medical service (EMS) triage for stroke patients.
- This framework can improve the accuracy of destination hospital selection, balancing delays in interventions like intravenous tissue plasminogen activator (IVT) and endovascular thrombectomy (EVT).
- Further development and implementation of this model can enhance stroke care delivery, especially in resource-limited rural areas.
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