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

缺血性中风后危及生命的质量效应的动态风险预测

• 批准号: 10599893
• 负责人: Charlene J Ong
• 金额: $ 21.88万
• 依托单位: BOSTON MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2026-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10599893
• 关键词: Acute Brain Injuries; Admission activity; Affect; Arousal; Biometry; Boston; Brain; Brain Edema; Brain Stem; Calibration; Caring; Clinical; Clinical Investigator; Clinical Pathways; Clinical Research; Clinical Trials Design; Critical Illness; Data; Data Set; Decision Making; Deterioration; Development; Discrimination; Epidemiologist; Funding; Future; Goals; Hour; Image; Injury; Intervention; Investigation; Ischemia; Ischemic Stroke; Laboratories; Life; Medical; Medical Records; Medical center; Mentored Patient-Oriented Research Career Development Award; Mentors; Mentorship; Methodology; Methods; Middle Cerebral Artery Infarction; Middle Cerebral Artery Occlusion; Mission; Modeling; Monitor; Morbidity - disease rate; National Institute of Neurological Disorders and Stroke; Neurologic; Neurologist; Operative Surgical Procedures; Outcome; Pathway interactions; Patients; Performance; Pharmaceutical Preparations; Public Health Schools; Pupil; Research; Research Design; Research Personnel; Risk; Risk Factors; Scientist; Severities; Specialist; Stroke; Structure; Surgical Decompression; Swelling; Testing; Time; Training; Universities; Update; career; career development; cerebrovascular; clinical decision support; clinical decision-making; clinical practice; cohort; disability; experience; functional outcomes; improved; improved outcome; innovation; mortality; multidisciplinary; multimodality; nervous system disorder; novel; novel strategies; outcome prediction; patient oriented; pressure; prospective; radiological imaging; recruit; risk prediction; risk prediction model; serial imaging; skill acquisition; stroke patient; support tools; tool

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.

项目概要/摘要 Ong 博士是一名神经科医生和年轻研究者，致力于以患者为导向的 K-23 临床研究。 提交后，她将开发一个新的框架，用于动态预测危及生命的质量效应 使用入院后 120 小时内的基线和纵向变量来评估缺血性卒中 A。 K23 奖项将为她提供获得关键职业发展技能的途径，使她能够 执行并建立她的项目，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。 将帮助 Ong 博士实现成为数据独立临床研究者的长期职业目标- Ong 博士拥有支持临床决策并优化急性脑损伤后结果的驱动工具。 招募了一个多学科的指导团队来协助她执行她的项目并实现科学目标 她将由 R01 资助的临床科学家 David Greer 博士共同指导。 神经重症监护、神经预测和临床研究设计，以及 R01 资助的 Emelia Benjamin 博士 领先的脑血管流行病学家和风险预测专家，拥有丰富的指导经验。 波士顿大学公共卫生学院生物统计学系主任 Josée Dupuis 将担任方法论专家 监督 Ong 博士在动态建模策略方面的进展。 Ong 博士的首要假设是。 使用新的纵向数据更新预测的动态风险模型将会得到改善 目标 1 将预测危及生命的质量效应并更好地支持临床决策。 使用 3000 名大中风患者的回顾性医疗记录数据集来识别变量轨迹 预测放射线 LTME，并使用此信息来开发更新的多变量动态风险 LTME 模型由入院前 120 小时的基线和纵向变量组成。在目标 2 中， 她将通过前瞻性招募研究每小时定量瞳孔测量与 LTME 的关系 研究人员对 60 名大中风患者进行了研究，并使用该模型开发了 LTME 的探索性动态多变量模型 Ong博士提出的研究是 意义重大，因为改进 LTME 预测可以促进更及时的生命和功能保护干预措施。 她的研究具有创新性，因为她将开发并应用一种新颖的动态风险建模框架来预测 缺血性中风后的继发性损伤，并研究新的纵向有希望的变量，定量 瞳孔测量。 目标 1 中确定的预测变量以及每小时瞳孔测量数据。 她的目标、培训计划和跨学科指导团队将使 Ong 博士成为一名 数据驱动工具的独立研究者，支持临床决策并优化治疗后的结果 预期的结果将是 R01 提案测试效果的强有力的初步数据。 对临床实践中的干预时间和结果进行动态 LTME 评估。