# Effect of iron status trajectories on lung function and asthma risk

> **NIH AHRQ R03** · INDIANA UNIVERSITY INDIANAPOLIS · 2024 · $11,475

## Abstract

A2. Abstract of the Funded Parent Grant (R03HS029088)
Undertreatment of childhood asthma is prevalent and often the right treatment for incident cases is unknown hence the
widespread use of therapeutic trials as a treatment strategy. Two-thirds of incident childhood asthma cases continue to
have persistent symptoms even after treatment initiation. Missed opportunities for early efficacious treatment contribute to
increased risk of childhood asthma-associated morbidity (i.e., uncontrolled asthma) that exerts a substantial burden on
patients, families, and the healthcare system. However, clinical decision-making tools needed to identify which child will
benefit from which treatment at an early stage are currently lacking. This proposal is predicated on the notion that
applying novel machine learning (ML) methodologies to increasingly available electronic health record (EHR)
risk/prognostic data can generate predictive analytics and insights regarding childhood asthma treatment response.
Clinicians can then use such insights toward effective treatment decision-making at point of care, including more
proactive and personalized treatment, for improved patient-centered outcomes. Although risk and prognostic factors
needed for treatment response prediction are often embedded in EHR, this information is sometimes overlooked by
clinicians. In busy pediatric clinics, active EHR review to identify such factors to inform treatment decisions can be
costly, time-consuming, error-prone, and infeasible. To address these challenges and technological gap, we propose to
develop, validate, and evaluate a childhood asthma Passive Digital Marker for treatment response prediction (PDM-TR),
that is, a ML algorithm that can retrieve and synthesize pre-existing 'passively' collected mother-child dyad
risk/prognostic data in 'digital' EHR to provide an objective and quantifiable 'marker' of treatment response. We
hypothesize that when applied to risk/prognostic EHR data derived from incident asthma cases exposed to first-line
treatments, our PDM-TR will predict asthma control at 2-3 months with high accuracy (≥80 sensitivity and ≥80
specificity). The PDM-TR will 'learn from existing EHR data' to predict whether a specific treatment may be successful
(i.e., achieve asthma control) for a given individual with a specific set of attributes (i.e., asthma risk and prognostic factors
[e.g., history of allergy sensitization, eczema, demographics, lung function, body mass index]). Applying our novel PDM-
TR in-real time to readily available EHR data could contribute towards the development of a timely, accurate and scalable
approach to inform personalized childhood asthma treatment at point of care.

## Key facts

- **NIH application ID:** 10842802
- **Project number:** 3R03HS029088-02S1
- **Recipient organization:** INDIANA UNIVERSITY INDIANAPOLIS
- **Principal Investigator:** Arthur Hamie Owora
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** AHRQ
- **Fiscal year:** 2024
- **Award amount:** $11,475
- **Award type:** 3
- **Project period:** 2022-08-01 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10842802

## Citation

> US National Institutes of Health, RePORTER application 10842802, Effect of iron status trajectories on lung function and asthma risk (3R03HS029088-02S1). Retrieved via AI Analytics 2026-06-11 from https://api.ai-analytics.org/grant/nih/10842802. Licensed CC0.

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