Developing a Childhood Asthma Risk Passive Digital Marker

开发儿童哮喘风险被动数字标记

基本信息

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
项目类别：
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

项目摘要

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这一目标基于应用新颖的生物统计和机器学习 (ML) 的概念。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然后，临床医生可以利用这些见解来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这直接响应了 NIH 国家心脏、肺和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一名转化科学家，他需要有研究经验来过渡到独立研究。关于如何应用新颖的生物统计和机器学习方法来制定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对早期疾病检测的初步功效这里是被动数字标记。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）确定该方案的可用性、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（医师科学家和肺病学家）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。