Dynamic Risk Prediction of Life-Threatening Mass Effect After Ischemic Stroke

项目摘要/摘要 王博士是一名神经学家，也是一名年轻的研究员，致力于以患者为导向的临床研究。为了这架K-23 提交后，她将开发一个新的框架，用于动态预测危及生命的群体效应的风险 入院后120小时内使用基线变量和纵向变量的缺血性中风患者。一个 K23奖项将为她提供获得关键职业发展技能的手段，使她能够 执行和加强她的项目，包括：1)动态风险预测和轨迹分析；2)多模式 神经预测方法；3)临床试验设计；4)专业发展。这些目标 将帮助王博士实现她的长期职业目标，即成为一名独立的数据临床调查员- 支持临床决策并优化急性脑损伤后结果的驱动工具。王博士已经 招募了一个多学科的指导团队来帮助她执行项目并实现科学 独立。她将由大卫·格里尔博士共同指导，大卫·格里尔博士是一位由R01资助的临床科学家，擅长 神经危重护理、神经预测和临床研究设计，以及由R01资助的Emelia Benjamin博士 具有丰富指导经验的领先脑血管流行病学家和风险预测专家。Dr。 波士顿大学公共卫生学院生物统计学主席何塞·杜普伊斯将担任方法论 指导王博士在动态建模策略方面的进展。王博士的主要假设是 用新获得的纵向数据更新预测的动态风险模型将得到改进 预测危及生命的群体效应，更好地实时支持临床决策。目标1将 使用3000名大面积中风患者的回溯性医疗记录数据集来确定变量轨迹 放射学LTME的预测，并使用该信息来开发更新的多变量动态风险 LTME模型包括入院前120小时的基线变量和纵向变量。在目标2中， 她将通过未来的招聘来研究每小时定量验光与LTME的关系 60例大面积卒中患者，建立了探索性的LTME动态多变量模型 王博士提议的研究是 意义重大，因为改进LTME预测可以促进更及时地进行挽救生命和功能的干预。 她的研究具有创新性，因为她将开发和应用一种新的动态风险建模框架来预测 缺血性卒中后继发性损伤，并研究新的有前途的纵向变量，定量 小学生计量学。 目标1中确定的预测变量以及每小时的斜视测量数据。 她的目标、培训计划和跨学科指导团队将为王博士成为一名 支持临床决策并优化术后结果的数据驱动工具的独立调查者 急性脑损伤。预期的结果将是测试R01提案效果的强有力的初步数据 临床实践中对介入时间和结果的动态LTME评估。

PROJECT SUMMARY/ABSTRACT Dr. Ong is a neurologist and young investigator pursuing patient-oriented clinical research. For this K-23 submission, she will develop a novel framework for dynamic risk prediction of life-threatening mass effect after ischemic stroke using both baseline and longitudinal variables through the first 120 hours after admission. A K23 award will provide her with the means to acquire critical career development skills that will enable her to execute and build upon her project including: 1) dynamic risk prediction and trajectory analysis; 2) multi-modal methods of neuroprognostication; 3) clinical trial design; and 4) professional development. These objectives will help Dr. Ong to achieve her long-term career goal of becoming an independent clinical investigator of data- driven tools that support clinical decision making and optimize outcomes after acute brain injury. Dr. Ong has recruited a multi-disciplinary mentorship team to assist her in executing her project and achieving scientific independence. She will be co-mentored by Dr. David Greer, an R01 funded clinician scientist with expertise in neurocritical care, neuroprognostication, and clinical study design, and Dr. Emelia Benjamin, an R01 funded leading cerebrovascular epidemiologist and risk prediction specialist with extensive mentorship experience. Dr. Josée Dupuis, Chair of Biostatistics at Boston University School of Public Health, will serve as a methodologic mentor overseeing Dr. Ong’s progress in dynamic modeling strategies. Dr. Ong’s overarching hypothesis is that dynamic risk models that update their predictions with newly available longitudinal data will improve prediction of Life-Threatening Mass Effect and better support clinical decision making in real-time. Aim 1 will use a retrospective medical record dataset of 3000 large stroke patients to identify variables trajectories predictive of radiographic LTME, and use this information to develop updating multivariable dynamic risk models of LTME comprised of baseline and longitudinal variables for the first 120 hours of admission. In Aim 2, she will study the relationship of hourly quantitative pupillometry and LTME through the prospective recruitment of 60 patients with large stroke, and develop an exploratory dynamic multivariable model of LTME using Dr. Ong’s proposed research is significant because improving LTME prediction can facilitate more timely life- and function-sparing interventions. Her research is innovative because she will develop and apply a novel dynamic risk modeling framework to predict secondary injury following ischemic stroke, and study the new promising longitudinal variable, quantitative pupillometry. predictive variables identified in Aim 1 as well as hourly pupillometry data. Her aims, training plan, and interdisciplinary mentorship team will prepare Dr. Ong to become an independent investigator of data-driven tools that support clinical decision making and optimize outcomes after acute brain injury. The anticipated results will be strong preliminary data for a R01 proposal testing the effect of dynamic LTME assessments on time to intervention and outcome in clinical practice.