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用于优化院前卒中分诊决策的贝叶斯建模框架。

Bayesian modeling framework for optimizing pre-hospital stroke triage decisions.

作者信息

Nwoke Uche, Farooqui Mudassir, Oleson Jacob, Mohr Nicholas, Ortega-Gutierrez Santiago, Brown Grant D

机构信息

Department of Biostatistics, College of Public Health, University of Iowa, Iowa City, IA, USA.

Department of Neurology, Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, IA, USA.

出版信息

J Appl Stat. 2024 May 30;52(1):135-157. doi: 10.1080/02664763.2024.2360590. eCollection 2025.

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. ABBREVIATION LVO: 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.

摘要

在美国乃至全球,缺血性中风都导致了极高的发病率和死亡率。优化中风治疗需要急救医疗人员迅速做出分诊决策,确定将患者送往哪家目的地医院,因为不同医院提供高度时间敏感型药物和手术干预的能力存在差异。这些决策在农村地区尤为关键,在这些地区,转运决策会对治疗时间产生重大影响——通常需要在药物治疗延迟和血管内血栓切除术延迟之间进行权衡。在这项研究中,我们探索了一种贝叶斯建模框架来处理这一决策过程,展示了如何使用这些技术来充分考虑诊断和治疗的不确定性。我们演示了这些技术如何在精细的空间尺度上为分诊决策提供背景信息。我们进一步展示了这种建模方法在美国爱荷华州的应用,使用了来自虚拟国际中风试验档案库(VISTA)的数据,并描述了改进分诊的潜在后续步骤。缩写:LVO:大血管闭塞;非LVO,非大血管闭塞;IVT:静脉注射组织纤溶酶原激活剂;EVT:血管内血栓切除术;CSC:综合中风中心;PSC:初级中风中心;DS:滴注与转运;MS,母舰;EMS:紧急医疗服务;BGLM:贝叶斯广义线性模型;BGAM:贝叶斯广义加性模型;BART:贝叶斯加法回归树;VISTA:虚拟国际中风试验档案库;NIHSS:美国国立卫生研究院中风严重程度量表;ASPECTS:阿尔伯塔中风项目早期CT评分;mRS,改良Rankin量表;ROCAUC:受试者操作特征曲线下面积;ELPD:预期对数逐点预测密度;SE:标准误差;ICA:颈内动脉;M1:大脑中动脉M1段;M2:大脑中动脉M2段;TIA:短暂性脑缺血发作;Cr-I:可信区间;LKW:最后已知正常时间

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