Developing a Childhood Asthma Risk Passive Digital Marker

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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.