Developing a Childhood Asthma Risk Passive Digital Marker

项目总结/摘要 博士亚瑟奥沃拉是一个生物统计学家和定量流行病学家，他的长期职业目标是翻译 通过设计和测试直观临床决策的有效性，将预后研究纳入临床实践 支持工具。这一目标的前提是，应用新的生物统计和机器学习（ML） 越来越多的电子健康记录（EHR）预测数据的方法可以产生预测 关于疾病风险的分析和见解。然后，临床医生可以使用这些见解进行有效的临床决策- 在护理点，包括更积极主动和个性化的护理，以改善以病人为中心的 结果。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，以改善健康和减少疾病。” 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， 博士后研究金在疾病的发展起源的建模，以及以前的预后 研究经验过渡到研究独立作为一个翻译科学家。为此，他要求 关于如何应用新型生物统计和ML方法来制定数字临床决策的额外培训- 支持工具，以及2）在临床环境中实施和评估此类工具的功效。 本提案描述了一个为期4年的项目，以开发和确定可用性、可接受性、可行性和 儿童哮喘被动数字标记物用于早期疾病检测的初步效果。在这里，被动 数字标记（PDM）是指可用于检索和合成预先存在的标记的ML算法。 “被动”收集的母亲/儿童二分体预后数据（即，病史）在“数字”EHR中的0-3岁 为6-10岁儿童的哮喘风险和表型提供客观和可量化的“标记”。 建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 与儿童哮喘风险评分相比，儿童哮喘PDM的预测性能（作为 标准实践），和（2）确定的可用性，可接受性，可行性，和初步疗效 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在内的培训活动。 学习建立儿童哮喘PDM开发、实施和评估的专业知识， 临床环境。这些培训活动将得到一个强大的多学科导师团队的支持： Richard霍尔顿（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（医生-科学家和肺病学家）、Malaz Boustani（医生和 执行科学家）和道格拉斯Landsittel（生物统计学家和生物信息学家）。 凭借通过K 01奖项获得的初步数据、新技能和专业知识，Owora博士计划 提交R 01补助金，以评估PDM改善儿童哮喘早期发现的有效性。

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.