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.

项目摘要 急性缺血性卒中（AIS）仍然是美国残疾的主要原因。大血管闭塞（LVO） 占所有缺血性中风的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性治疗， 预防中风相关的发病率和死亡率。EVT对LVO非常有效，但不提供任何好处 非LVO卒中，在不到20%的美国卒中中心可用。IVT是现成的，具有适度的 是治疗非LVO卒中的唯一替代疗法。护理人员面临的挑战是 加快合格患者的EVT，而不会伤害大部分需要IVT的非合格患者， 初始诊断不确定性的背景。目前的系统分流标准已经落后于新兴的 最严重的亚组可获得的治疗，在农村地区， 为少数民族。 在此，我们提出了一项研究，以促进中风的创新地理空间分类算法的发展 通过多学科合作在美国卫生系统中提供护理，以最大限度地恢复所有人的神经功能 中风患者该模型将被构建为提供个体患者的最佳预测结果， 使用贝叶斯框架来告知治疗决策树的每个链接，建立在先前的研究基础上， 克服其局限性，缩小执行差距。首先，将建立患者结局模型 使用个人和医院水平的数据随机试验，这将使上下文敏感的分流决策 算法，而不依赖于关于治疗途径的过于宽泛的假设。我们将独一无二地 通过在贝叶斯框架中对个人层面的数据进行建模，而不是依赖 在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 策略，因为中风领域的发展。此外，条件结构将允许修改设施 能力，包括引进新的EVT功能中风中心。临床和成本效益 将通过与当前现实世界的分诊进行比较，并结合局部卒中，评估算法影响 从指南-卒中登记研究到 更好地估计良好结果的概率，并提高分诊能力。最后，分类算法 将被集成到一个护理点决策工具支持，随时可供所有EMS推荐 最佳的目的地为所有的整个中风人口后，他们的初步评估。经过适当的提炼 并在随后的研究中充分实施，该工具不仅有可能优化中风 结果，但也减少了美国的实际地理和种族差异。