Identifying and predicting subgroups related to function in individuals after stroke.

识别和预测与中风后个体功能相关的亚组。

• 批准号: 10459813
• 负责人: Margaret French
• 金额: $ 5.9万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-09 至 2023-07-03
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10459813
• 关键词: Age; Area; Characteristics; Clinical Research; Cognition; Decision Trees; Development; Dose; Electronic Health Record; Environment; Fellowship; Funding; Goals; Growth; Health; Health Care Costs; Healthcare Systems; Impairment; Individual; Intervention; Measures; Mentors; Modeling; Outcome; Patient-Focused Outcomes; Patients; Pattern; Persons; Quality of life; Recovery; Recovery of Function; Rehabilitation therapy; Research; Research Personnel; Research Training; Scientist; Stroke; Subgroup; Testing; Time; Training; United States; Upper Extremity; Work; care costs; career; classification trees; clinical care; clinical decision-making; cognitive function; comorbidity; demographics; deprivation; design; disability; electronic data; experience; falls; functional status; hospital readmission; implementation intervention; improved; indexing; individualized medicine; intervention delivery; model development; post stroke; predictive modeling; prospective; regression trees; stroke recovery; stroke rehabilitation

PROJECT SUMMARY/ABSTRACT Two common, long-lasting deficits after stroke are impaired mobility and cognition. Impairments in mobility and cognition are associated with numerous negative health consequences, including falls, poor quality of life, and hospital readmissions. Recovery of mobility and cognition after stroke is highly variable and poorly understood. Therefore, rehabilitation professionals are unable to provide interventions that are tailored to each specific patients they treat, contributing to a “one size fits all” approach to post stroke rehabilitation that is characterized by incomplete recovery and high healthcare costs. A comprehensive characterization of recovery and models that predict recovery are essential to shifting towards interventions that are tailored to specific individuals after stroke. Thus, the overall objective of this proposal is to develop models that predict what recovery trajectory of mobility and cognition individuals after stroke will follow. Recovery trajectories completely describe patient recovery and are characterized both their extent (i.e., large, moderate, or limited) and temporal pattern (i.e., fast or slow). Both the extent and pattern of recovery can impact the type, timing, and dosing of rehabilitation interventions; however, the extent of recovery has been the focus of previous work. Models that predict the recovery trajectory, rather than just the extent of recovery, for mobility and cognition for individuals after stroke are essential to moving towards rehabilitation interventions that are targeted towards specific individuals after stroke. Past work related to upper extremity recovery after stroke suggest that there are subgroups related to recovery trajectories and that the characteristics of individuals can be used to develop predictive models. Therefore, the central hypothesis of this work is that we can 1) characterize recovery trajectories for mobility and cognition after stroke and 2) use characteristics of individuals after stroke to develop models to predict mobility and cognition recovery trajectories. The results of this proposal will provide a comprehensive understanding of the recovery of mobility and cognition after stroke and an accurate predictive model of the recovery trajectories of mobility and cognition. This will guide the development and delivery of interventions to the right person at the right time to optimize functional recovery and improve the efficiency of our healthcare system. Additionally, the proposed work serves as a first step towards the long-term goal of this fellowship applicant, which is to understand variability in patient recovery in order to move post-stroke rehabilitation towards the delivery of interventions that are tailored to each specific patient, thereby, improving patient outcomes within a more efficient healthcare system. By completing this fellowship proposal under the guidance of experienced mentors in a strong research environment, the applicant will receive the training needed to become a productive, independent scientist and to achieve her long term career goals.

项目摘要/摘要 中风后两种常见的、持续时间较长的缺陷是行动能力受损和认知障碍。行动不便，行动不便 认知与许多负面的健康后果有关，包括摔倒、生活质量差和 再次入院治疗。卒中后活动能力和认知能力的恢复具有很高的变异性，人们对此知之甚少。 因此，康复专业人员无法提供针对每个特定患者的干预措施 他们治疗的患者，为中风后康复提供了一种“一刀切”的方法，这种方法的特点是 不完全的恢复和高昂的医疗成本。经济复苏的综合表征和模型 预测复苏对于转向针对特定个人的干预措施是必不可少的 卒中。因此，这项提案的总体目标是开发预测经济复苏轨迹的模型 中风后的机动性和认知力也将随之而来。康复轨迹完全描述了患者 并且被表征为其程度(即，大、中或有限)和时间模式(即， 快或慢)。康复的程度和模式都会影响康复的类型、时间和剂量 干预措施；然而，恢复的程度一直是以往工作的重点。预测未来经济形势的模型 中风后患者的活动能力和认知能力的恢复轨迹，而不仅仅是恢复的程度 对于转向针对特定个人的康复干预是必不可少的 卒中。过去与中风后上肢康复相关的研究表明，有一些亚组与 他还指出，可以利用个人的特征来建立预测模型。 因此，这项工作的中心假设是，我们可以1)表征机动性的恢复轨迹 和中风后的认知，以及2)使用中风后个体的特征来开发模型来预测 活动能力和认知恢复轨迹。这项提议的结果将提供一个全面的 对卒中后活动能力和认知功能恢复的认识及准确的预测模型 活动能力和认知能力的恢复轨迹。这将指导制定和提供干预措施，以 在正确的时间选择合适的人来优化功能恢复并提高我们的医疗保健效率 系统。此外，拟议的工作是迈向该研究金长期目标的第一步。 申请者，这是为了了解患者康复中的变异性，以便推进中风后康复 为每个特定患者量身定做的干预措施的交付，从而改善患者 在更高效的医疗系统中取得的成果。通过在指导下完成本奖学金提案 在强大的研究环境中的经验丰富的导师，申请人将接受所需的培训 成为一名多产、独立的科学家，并实现她的长期职业目标。

项目成果

A Learning Health System Infrastructure for Precision Rehabilitation After Stroke.

用于中风后精准康复的学习健康系统基础设施。

• DOI: 10.1097/phm.0000000000002138
• 发表时间: 2023
• 期刊: American journal of physical medicine & rehabilitation
• 影响因子: 3
• 作者: French,MargaretA;Daley,Kelly;Lavezza,Annette;Roemmich,RyanT;Wegener,StephenT;Raghavan,Preeti;Celnik,Pablo
• 通讯作者: Celnik,Pablo