Pilot Study to Develop a Functional Status Score for Children with Acute Neurologic Illnesses and Injuries

为患有急性神经系统疾病和损伤的儿童制定功能状态评分的试点研究

• 批准号: 10226002
• 负责人: Jared D Huling
• 金额: $ 14.8万
• 依托单位: OHIO STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10226002
• 关键词: Acute; Address; Adult; Big Data; Child; Childhood; Clinic; Clinical; Code; Cognition; Cognitive; Communication; Comparative Effectiveness Research; Computerized Medical Record; Consensus; Data; Data Set; Data Sources; Development; Diagnosis; Effectiveness of Interventions; Etiology; Event; Face; Goals; Health; Healthcare; Heterogeneity; Injury; Inpatients; Intervention; Intervention Studies; Measures; Medical; Methodology; Methods; Modeling; Nervous System Trauma; Neurologic; Outcome; Outcome Measure; Patients; Pediatric Neurology; Pharmacologic Substance; Pilot Projects; Population; Procedures; Quality of life; Rehabilitation therapy; Research; Research Methodology; Research Personnel; Resources; Sampling; Self Care; Site; Standardization; Techniques; Time; Training; Trauma; United States; Work; acute care; base; clinical application; clinical care; clinically relevant; compare effectiveness; comparison intervention; cost effective; design; effectiveness evaluation; electronic data; experience; functional independence; functional outcomes; functional status; improved; innovation; predictive modeling; provider adoption; rehabilitation research; rehabilitation strategy; societal costs; tool

PROJECT SUMMARY Electronic Medical Record (EMR) data represents a promising, but under-utilized, resource for evaluating interventions that focus on devastating pediatric health events such as neurologic injuries and illnesses. However, a limitation to using existing data is the lack of universally used, standardized, meaningful outcome measures within the datasets. The overall objective of this project is to demonstrate feasibility and proof of concept for the development of a Pediatric Functional Status Score (PFSS) that uses clinically relevant claims data from the EMR (i.e., diagnoses, procedures, pharmaceuticals, and durable goods) to accurately represent a child’s functional mobility, self-care, and cognitive/communication status. The hypothesis is that the constellation of claims data associated with a particular point in a child’s clinical course is an accurate reflection of the child’s functional status at that time. The rationale for this research is that a functional status score based on readily available, high-quality big data sources will allow researchers to compare the effectiveness of pediatric rehabilitation interventions. Such a tool is necessary to give researchers the ability to quickly and accurately assess functional status at any time that includes child-specific claims data. Using consensus methodology (Delphi method and nominal group technique), this study will engage ten expert pediatric neurology, trauma, and rehabilitation clinicians to determine which billing codes are clinically relevant to a child’s functional mobility, self-care, and cognitive/communication status (Aim 1). Through the creation of predictive models, researchers will then determine the feasibility of using identified billing codes to develop a PFSS that accurately represents a child’s functional status in these domains at the time of discharge from inpatient rehabilitation (Aim 2). The PFSS will be calibrated against the Functional Independence Measure for Children (WeeFIM™), a validated, but not universally, used outcome measure. Aim 2 will incorporate retrospective data from the EMR for the 1,797 children (6 months to 18 years) with acute neurologic injuries or illnesses admitted to the Inpatient Rehabilitation Unit at large pediatric quaternary center between 2000 and 2019. The results of this work will provide the necessary proof of concept for a subsequent R01 submission wherein we will fully develop and validate the PFSS in a multi-site proposal. Ultimately, a PFSS that relies on highly conserved, child-specific claims data is significant because it provides a transformative tool that researchers can use to harness big data to determine the effectiveness of interventions for acute neurologic injury or illness at any time that has associated claims data and without additional burden to patients or clinicians. The innovation of our design mitigates current challenges to the use of big data in pediatric rehabilitation research by creating a clinically meaningful PFSS based on child-specific claims data that investigators can use immediately to study interventions for children with acute neurologic injuries and illnesses.

项目摘要 电子病历（EMR）数据是一种有前途的，但未充分利用的资源，用于评估 干预措施侧重于破坏性的儿科健康事件，如神经损伤和疾病。 然而，使用现有数据的一个局限性是缺乏普遍使用的、标准化的、有意义的结果 在数据集内进行测量。该项目的总体目标是证明可行性和证据， 使用临床相关声明开发儿科功能状态评分（PFSS）的概念 来自EMR的数据（即，诊断、程序、药品和耐用品），以准确地表示 儿童的功能性活动、自我照顾和认知/交流状态。假设是， 与儿童临床过程中的特定点相关联的索赔数据的星座是准确的 这反映了孩子当时的心理状态。这项研究的基本原理是， 基于现成的高质量大数据源的评分将使研究人员能够比较 儿科康复干预的有效性。这样的工具是必要的，使研究人员能够 随时快速准确地评估功能状态，包括儿童特定索赔数据。使用 本研究采用共识研究法（德尔菲法和名义团体法）， 儿科神经病学、创伤和康复临床医生，以确定哪些计费代码与临床相关 儿童的功能性活动、自我照顾和认知/沟通状态（目标1）。通过建立 预测模型，研究人员将确定使用已识别的计费代码来开发 PFSS能够准确反映儿童在出院时在这些领域的功能状态， 住院康复（目标2）。PFSS将根据功能独立性测量进行校准， 儿童（Weewealth ™），一种经过验证但不普遍使用的结局指标。目标2将纳入 来自EMR的1，797例急性神经损伤儿童（6个月至18岁）的回顾性数据， 2000年至2010年期间，大型儿科四级中心的住院康复科收治的疾病 2019.这项工作的结果将为随后的R 01提交提供必要的概念证明 其中，我们将在多站点提案中全面开发和验证PFSS。最终，PFSS依赖于 高度保守的、针对儿童的索赔数据非常重要，因为它提供了一种变革性工具， 研究人员可以利用大数据来确定急性神经系统疾病干预措施的有效性， 在任何时间发生的与索赔数据相关的伤害或疾病，且不会给患者带来额外负担， 临床医生我们设计的创新缓解了当前儿科大数据使用的挑战 通过基于儿童特定索赔数据创建具有临床意义的PFSS进行康复研究， 研究人员可以立即使用它来研究急性神经损伤儿童的干预措施， 疾病。

项目成果

Using Billing Codes to Create a Pediatric Functional Status e-Score for Children Receiving Inpatient Rehabilitation.

使用账单代码为接受住院康复治疗的儿童创建儿科功能状态电子评分。

• DOI: 10.1016/j.apmr.2023.03.025
• 发表时间: 2023
• 期刊: Archives of physical medicine and rehabilitation
• 影响因子: 4.3
• 作者: Lundine,JenniferP;Huling,JaredD;Adelson,PDavid;Burd,RandallS;Fuentes,Molly;Haarbauer-Krupa,Juliet;Hagen,Kaitlin;Iske,Cynthia;Koterba,Christine;Kurowski,BradG;Petrucci,Stephanie;Rose,SeanC;Sadowsky,CristinaL;Westendorf,Jen
• 通讯作者: Westendorf,Jen

Doubly structured sparsity for grouped multivariate responses with application to functional outcome score modeling.

• DOI: 10.1002/sim.9740
• 发表时间: 2023-07-10
• 期刊: STATISTICS IN MEDICINE
• 影响因子: 2
• 作者: Huling, Jared D.;Lundine, Jennifer P.;Leonard, Julie C.
• 通讯作者: Leonard, Julie C.