Geospatial modeling for stroke care

中风护理的地理空间建​​模

• 批准号: 10432727
• 负责人: Grant D Brown
• 金额: $ 41.42万
• 依托单位: UNIVERSITY OF IOWA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-01 至 2027-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10432727
• 关键词: Acute; Address; Algorithms; American; Bayesian Modeling; Bypass; Caring; Cessation of life; Characteristics; Clinical; Clinical Trials; Communities; Costs and Benefits; Data; Decision Analysis; Decision Making; Decision Trees; Destinations; Development; Diagnostic; Early treatment; Emergency Care; Emergency medical service; Evaluation; Fostering; Foundations; Future; Geography; Guidelines; Health system; Healthcare; Hospitals; Individual; Interdisciplinary Study; Intravenous; Ischemic Stroke; Lead; Link; Location; Modeling; Modification; Morbidity - disease rate; Neurological outcome; Outcome; Paramedical Personnel; Pathway interactions; Patient-Focused Outcomes; Patients; Population; Prevention; Probability; Qualifying; Randomized Controlled Trials; Recommendation; Registries; Reperfusion Therapy; Reproducibility; Research; Resources; Risk; Severities; Spain; Stroke; Stroke prevention; Structure; System; Telemedicine; Testing; Therapeutic; Time; Triage; Uncertainty; United States; base; design; diagnostic accuracy; diagnostic tool; disability; effective therapy; ethnic minority; evidence base; experience; geographic disparity; improved; individual patient; ineffective therapies; innovation; metropolitan; mobile application; mortality; neurological recovery; novel therapeutic intervention; operation; outcome prediction; point of care; prevent; racial disparity; randomized trial; rapid diagnosis; rural area; stroke model; stroke outcome; stroke patient; suburb; support tools; thrombolysis; tool

PROJECT SUMMARY Acute Ischemic stroke (AIS) remains the leading cause of disability in the US. Large vessel occlusion (LVO) represents up to 20% of all ischemic strokes, but causes 90% of stroke-related death and severe disability. Both intravenous thrombolysis (IVT) and endovascular therapy (EVT) are effective time-sensitive treatments to prevent stroke-related morbidity and mortality. EVT is highly effective for LVOs, does not provide any benefit in non-LVO strokes and is available in less than 20% of US stroke centers. IVT is readily available, has a modest effect for LVOs and is the only therapeutic alternative for non-LVO strokes. The challenge for paramedics is to expedite EVT for eligible patients without harming a large proportion of non-qualified patients in need of IVT, in the context of initial diagnostic uncertainty. The current system triage criteria have lagged behind emerging therapies available to the sickest subset, and the disparity in stroke outcomes is exacerbated in rural areas and for ethnic minorities. Herein, we propose a study to foster the development of an innovative geospatial triage algorithm of stroke care in the U.S. health system through a multidisciplinary collaboration to maximize neurological recovery to all stroke patients. The model will be constructed to provide optimal predicted outcomes for individual patients, using a Bayesian framework to inform each link of the treatment decision tree, building on prior studies while overcoming their limitations and closing the implementation gap. First, the patient outcome model will be built using individual and hospital level data randomized trials, which will enable a context sensitive triage decision algorithm without reliance on overbroad assumptions about the treatment pathway. We will uniquely incorporate uncertainty through modelling of individual level data in a Bayesian framework, rather than relying on point estimates at an aggregate level. Additionally, our model will be adaptable; we will be able to incorporate emerging LVO diagnostic tools with improved diagnostic accuracy, as well as new therapeutic strategies as the stroke field evolves. Furthermore, the conditional structure will allow the modification of facility capabilities, including the introduction of new EVT-capable stroke centers. The clinical and cost-benefit algorithm impact will be assessed by comparing with the current real-world triage by incorporating local stroke center and EVT-capable center data on stroke flow metrics from Get-With-The Guidelines-Stroke registry to better estimate the probability of good outcomes and improve triage capabilities. Finally, the triage algorithm will be integrated into a point-of-care decision tool support readily available for all EMS to recommend the optimal destination for all the entire stroke population after their initial assessment. After appropriate refinement and adequate implementation in subsequent studies, this tool will not only have the potential to optimize stroke outcomes, but also reduce the actual geographic and racial disparities in the U.S.

项目概要 急性缺血性中风（AIS）仍然是美国致残的主要原因。大血管闭塞（LVO） 占所有缺血性中风的比例高达 20%，但导致 90% 的中风相关死亡和严重残疾。 静脉溶栓（IVT）和血管内治疗（EVT）都是有效的、时间敏感的治疗方法 预防中风相关的发病率和死亡率。 EVT 对 LVO 非常有效，但不会提供任何益处 非 LVO 中风，在美国不到 20% 的中风中心提供。 IVT 很容易获得，但价格适中 对 LVO 有效，是非 LVO 中风的唯一治疗替代方案。护理人员面临的挑战是 加快符合条件的患者的 EVT，而不伤害大量需要 IVT 的不合格患者， 初始诊断不确定性的背景。当前的系统分类标准落后于新兴的系统分类标准 病情最严重的人群无法获得可用的治疗方法，并且农村地区和中风结果的差异加剧 对于少数民族。 在此，我们提出一项研究，以促进中风创新地理空间分类算法的开发 通过多学科合作在美国卫生系统中提供护理，以最大限度地提高所有人的神经功能恢复 中风患者。该模型的构建将为个体患者提供最佳的预测结果， 使用贝叶斯框架来告知治疗决策树的每个链接，以先前的研究为基础，同时 克服其局限性并缩小实施差距。首先，将建立患者结果模型 使用个人和医院级别的数据随机试验，这将有助于做出上下文敏感的分诊决策 算法不依赖于治疗途径的过于广泛的假设。我们将以独特的方式 通过在贝叶斯框架中对个体水平数据进行建模来纳入不确定性，而不是依赖 总体水平上的点估计。此外，我们的模型将具有适应性；我们将能够 结合新兴的 LVO 诊断工具，提高诊断准确性，以及新的治疗方法 随着中风领域的发展而制定的策略。此外，有条件的结构将允许修改设施 功能，包括引入新的具有 EVT 功能的中风中心。临床和成本效益 将通过结合局部中风与当前现实世界的分类进行比较来评估算法的影响 中心和支持 EVT 的中心关于中风流量指标的数据来自 Get-With-TheGuidelines-Stroke 登记处 更好地估计良好结果的可能性并提高分类能力。最后是分类算法 将被集成到护理点决策工具中，所有 EMS 都可以随时使用它来推荐 初步评估后所有中风人群的最佳目的地。经过适当的细化 以及在后续研究中的充分实施，该工具不仅具有优化中风的潜力 结果，但也减少了美国实际的地理和种族差异