Developing a Childhood Asthma Risk Passive Digital Marker

开发儿童哮喘风险被动数字标记

• 批准号: 10571461
• 负责人: Arthur Hamie Owora
• 金额: $ 16.2万
• 依托单位: TRUSTEES OF INDIANA UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-02-01 至 2023-04-01
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10571461
• 关键词: Active Learning; Address; Adult; Age; Algorithms; Asthma; Award; Biometry; Birth; Blinded; Blood Tests; Child; Childhood Asthma; Clinical; Clinical Research; Data; Detection; Development; Diagnosis; Digital biomarker; Disease; Early Diagnosis; Early treatment; Electronic Health Record; Enrollment; Epidemiologist; Epidemiology; Evaluation; Evaluation Studies; Fellowship; Future; Goals; Grant; Health; Indiana; Individual; Intervention; Intuition; Knowledge; Logistic Regressions; Machine Learning; Medical; Medical History; Mentored Research Scientist Development Award; Mentors; Modeling; Morbidity - disease rate; Mothers; National Heart, Lung, and Blood Institute; Natural History; Nursery Schools; Online Systems; Pathway interactions; Patient Care; Patient-Focused Outcomes; Performance; Phenotype; Physicians; Postdoctoral Fellow; Predictive Analytics; Prognosis; Prognostic Factor; Proxy; Random Allocation; Randomized; Research; Risk; Sample Size; School-Age Population; Scientist; Spirometry; Symptoms; Testing; Time; Training; Training Activity; Translating; United States; United States National Institutes of Health; Validation; career; clinical decision support; clinical decision-making; clinical practice; cohort; design; digital; disorder risk; effectiveness testing; efficacious intervention; efficacy evaluation; experience; health information technology; implementation research; improved; insight; machine learning algorithm; machine learning method; multidisciplinary; novel; pediatrician; personalized care; personalized medicine; point of care; primary caregiver; prognostic; prognostic signature; randomized, clinical trials; skills; support tools; tool; translational scientist; treatment choice; usability

PROJECT SUMMARY/ABSTRACT Dr. Arthur Owora is a biostatistician and quantitative epidemiologist whose long-term career goal is to translate prognostic research into clinical practice by designing and testing the effectiveness of intuitive clinical decision support tools. This goal is predicated on the notion that applying novel biostatistical and machine learning (ML) methodologies to increasingly available electronic health record (EHR) prognostic data can generate predictive analytics and insights regarding disease risk. Clinicians can then use such insights for effective clinical decision- making at point-of-care, including more proactive and personalized care, for improved patient-centered outcomes. This is directly responsive to NIH National Heart, Lung, and Blood Institute’s strategic objective to “Optimize clinical and implementation research to improve health and reduce disease.” To achieve his long-term goal, Dr. Owora will leverage his graduate training in biostatistics and epidemiology, post-doctoral fellowship in the modeling of developmental origins of disease, as well as previous prognostic research experience to transition to research independence as a translational scientist. To this end, he requires additional training in how to apply novel biostatistical and ML methodologies to develop digital clinical decision- support tools, and 2) implement and evaluate the efficacy of such tools in clinical settings. This proposal describes a 4-year project to develop and determine the usability, acceptability, feasibility, and preliminary efficacy of a childhood asthma Passive Digital Marker for early disease detection. Here, a Passive Digital Marker (PDM) refers to a ML algorithm that can be used to retrieve and synthesize pre-existing ‘Passively’ collected mother/child dyad prognostic data (i.e., medical history) at ages 0-3 years in ‘Digital’ EHR to provide an objective and quantifiable ‘Marker’ of a child’s asthma risk and phenotype at ages 6-10 years. Proposed specific aims build on Dr. Owora’s ongoing prognostic research to: (1) develop and evaluate the predictive performance of a childhood asthma PDM, compared to a Pediatric Asthma Risk Score (as a proxy for standard practice), and (2) determine the usability, acceptability, feasibility, and preliminary efficacy of the childhood asthma PDM among pediatricians. To address these objectives, Dr. Owora proposes training activities that include didactic and experiential learning to build expertise in the development, implementation, and evaluation of the childhood asthma PDM in clinical settings. These training activities will be supported by a strong multidisciplinary team of mentors: Richard Holden (Translational Scientist in Health Information Technology), Eneida Mendonca (Pediatrician and Medical Informatician), Robert Tepper (Physician-Scientist and Pulmonologist), Malaz Boustani (Physician and Implementation Scientist), and Douglas Landsittel (Biostatistician and Bioinformatician). With the preliminary data generated, new skills, and expertise gained through this K01 award, Dr. Owora plans to submit a R01 grant to evaluate the efficacy of the PDM for improved early detection of childhood asthma.

项目概要/摘要 Arthur Owora 博士是一位生物统计学家和定量流行病学家，其长期职业目标是将 通过设计和测试直观临床决策的有效性对临床实践进行预后研究 支持工具。这一目标的基础是应用新颖的生物统计和机器学习 (ML) 越来越多可用的电子健康记录 (EHR) 预后数据的方法可以生成预测 有关疾病风险的分析和见解。然后，临床医生可以利用这些见解做出有效的临床决策—— 床旁护理，包括更主动和个性化的护理，以改善以患者为中心的 结果。这直接响应了 NIH 国家心肺血液研究所的战略目标： “优化临床和实施研究，以改善健康并减少疾病。” 为了实现他的长期目标，Owora 博士将利用他在生物统计学和流行病学方面的研究生培训， 疾病发育起源建模以及先前预后的博士后奖学金 作为转化科学家过渡到独立研究的研究经验。为此，他要求 关于如何应用新型生物统计和机器学习方法来开发数字临床决策的额外培训 支持工具，2) 在临床环境中实施和评估此类工具的功效。 该提案描述了一个为期 4 年的项目，旨在开发和确定可用性、可接受性、可行性和 儿童哮喘被动数字标记用于早期疾病检测的初步功效。在这里，被动 数字标记 (PDM) 是一种机器学习算法，可用于检索和合成预先存在的 在“数字”EHR 中“被动”收集 0-3 岁母子二元预后数据（即病史） 提供 6-10 岁儿童哮喘风险和表型的客观且可量化的“标记”。 拟议的具体目标建立在 Owora 博士正在进行的预后研究的基础上：(1) 开发和评估 儿童哮喘 PDM 的预测性能，与小儿哮喘风险评分（作为儿童哮喘风险评分的代表）相比 标准实践），以及（2）确定可用性、可接受性、可行性和初步功效 儿科医生中的儿童哮喘 PDM。 为了实现这些目标，Owora 博士提出了包括教学和体验式的培训活动 学习建立儿童哮喘 PDM 开发、实施和评估方面的专业知识 临床环境。这些培训活动将得到强大的多学科导师团队的支持： Richard Holden（健康信息技术转化科学家）、Eneida Mendonca（儿科医生和 医学信息学家）、Robert Tepper（医师科学家和肺病学家）、Malaz Boustani（医师和科学家） 实施科学家）和 Douglas Landsittel（生物统计学家和生物信息学家）。 凭借通过 K01 奖项生成的初步数据、新技能和专业知识，Owora 博士计划 提交 R01 拨款来评估 PDM 在改善儿童哮喘早期检测方面的功效。