Journal article
Bayesian modeling framework for optimizing pre-hospital stroke triage decisions
Journal of applied statistics, Vol.52(1), pp.135-157
01/02/2025
DOI: 10.1080/02664763.2024.2360590
PMCID: PMC11727061
PMID: 39811091
Abstract
Ischemic stroke is responsible for significant morbidity and mortality in the United States and worldwide. Stroke treatment optimization requires emergency medical personnel to make rapid triage decisions concerning destination hospitals that may differ in their ability to provide highly time-sensitive pharmaceutical and surgical interventions. These decisions are particularly crucial in rural areas, where transport decisions can have a large impact on treatment times - often involving a trade-off between delay in pharmaceutical therapy or a delay in endovascular thrombectomy. In this work, we explore a Bayesian modeling framework to address this decision-making process, showing how these techniques may be used to fully account for diagnostic and therapeutic uncertainty. We demonstrate how these techniques can contextualize triage decision at a fine-grained spatial scale. We further show the application of this modeling approach in the US State of Iowa, using data from the Virtual International Stroke Trials Archive (VISTA), and describe potential next steps for improved triage.ABBREVIATIONLVO: large vessel occlusion; non-LVO, non-large vessel occlusion; IVT: intravenous tissue plasminogen activator; EVT: endovascular thrombectomy; CSC: comprehensive stroke centers; PSC: primary stroke centers; DS: drip and ship; MS, mothership; EMS: Emergency Medical Service; BGLM: Bayesian Generalized Linear Model; BGAM: Bayesian Generalized Additive Model; BART: Bayesian Additive Regression Trees; VISTA: Virtual International Stroke Trials Archive; NIHSS: National Institute of Health Stroke Severity Scale; ASPECTS: Alberta Stroke Programme Early CT Score; mRS, modified Rankin score; ROCAUC: Area under the receiver operating characteristic curve; ELPD: Expected Log pointwise Predictive Density; SE: Standard Error; ICA: Internal Carotid Artery; M1: Middle Cerebral Artery segment 1; M2: Middle Cerebral Artery segment 2; TIA: Transient Ischemic Attack; Cr-I: Credible Intervals; LKW: Last Known Well
Details
- Title: Subtitle
- Bayesian modeling framework for optimizing pre-hospital stroke triage decisions
- Creators
- Uche Nwoke - University of IowaMudassir Farooqui - University of IowaJacob Oleson - University of IowaNicholas Mohr - University of IowaSantiago Ortega-Gutierrez - University of IowaGrant D. Brown - University of IowaVirtual International Stroke Trials Archive (VISTA) Collaborators
- Resource Type
- Journal article
- Publication Details
- Journal of applied statistics, Vol.52(1), pp.135-157
- DOI
- 10.1080/02664763.2024.2360590
- PMID
- 39811091
- PMCID
- PMC11727061
- NLM abbreviation
- J Appl Stat
- ISSN
- 0266-4763
- eISSN
- 1360-0532
- Publisher
- Taylor & Francis
- Number of pages
- 23
- Grant note
- R01NS127114 / US Department of Health & Human Services of the National Institutes of Health (NIH) of the United States of America; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; Office of the Administrator (NIH)
- Language
- English
- Electronic publication date
- 05/31/2024
- Date published
- 01/02/2025
- Academic Unit
- Neurology; Radiology; Epidemiology; Emergency Medicine; Iowa Neuroscience Institute; Biostatistics; Anesthesia; Injury Prevention Research Center; Neurosurgery; Otolaryngology
- Record Identifier
- 9984648572802771
Metrics
19 Record Views