Developing a Childhood Asthma Risk Passive Digital Marker

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

TERMINATED

Clinical Phase

NA

Total Enrollment

34 participants

Study Classification

INTERVENTIONAL

Study Start Date

2024-06-01

Study Completion Date

2025-06-16

Brief Summary

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Underdiagnosis and undertreatment is a major problem in childhood asthma management, especially in preschool-aged children. Current prognostic approaches using risk-score based tools have poor-to-modest accuracy, are impractical, and have limited evidence of efficacy in clinical settings and hence are not widely used in practice.

The objective of the study is to determine the usability, acceptability, feasibility, and preliminary efficacy of the childhood asthma passive digital marker (PDM) among pediatricians. The study will include practicing pediatricians within the IU Health Network.

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Detailed Description

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Conditions

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Pediatric Asthma

Study Design

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Allocation Method

RANDOMIZED

Intervention Model

PARALLEL

Primary Study Purpose

SCREENING

Blinding Strategy

SINGLE

Caregivers

Study Groups

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Control Clinicians - post test only

N=25 control pediatric clinicians, who will receive the post test only. Each clinician will be presented with 10 randomly selected vignettes of 10 children \[5 with and 5 without asthma\] and asked to provide a prediction of a child's asthma risk at 6-10 years.

Group Type NO_INTERVENTION

No interventions assigned to this group

PDM Intervention Clinicians - post test only

N=25 intervention pediatric clinicians, who will receive the post test only. Using the PDM, each clinician will be presented with 10 randomly selected vignettes of 10 children \[5 with and 5 without asthma\] and asked to provide a prediction of a child's asthma risk at 6-10 years.

Group Type EXPERIMENTAL

Childhood Asthma Passive Digital Marker

Intervention Type OTHER

A childhood asthma Passive Digital Marker (PDM) is an ML algorithm that is able to retrieve and synthesize pre-existing "passively" collected mother/child dyad prognostic data in "digital" electronic health record (EHR) to provide an objective and quantifiable "marker" of a child's risk (probability) and associated pathophysiological phenotype to inform clinician decision-making at point-of-care.

Control Clinicians - pre and post test

N=25 control pediatric clinicians, who will receive the pre and post test. Each clinician will be presented with 10 randomly selected vignettes of 10 children \[5 with and 5 without asthma\] and asked to provide a prediction of a child's asthma risk at 6-10 years.

Group Type NO_INTERVENTION

No interventions assigned to this group

PDM Intervention Clinicians - pre and post test

N=25 intervention pediatric clinicians, who will receive the pre and post test. Using the PDM, each clinician will be presented with 10 randomly selected vignettes of 10 children \[5 with and 5 without asthma\] and asked to provide a prediction of a child's asthma risk at 6-10 years.

Group Type ACTIVE_COMPARATOR

Childhood Asthma Passive Digital Marker

Intervention Type OTHER

A childhood asthma Passive Digital Marker (PDM) is an ML algorithm that is able to retrieve and synthesize pre-existing "passively" collected mother/child dyad prognostic data in "digital" electronic health record (EHR) to provide an objective and quantifiable "marker" of a child's risk (probability) and associated pathophysiological phenotype to inform clinician decision-making at point-of-care.

Interventions

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Childhood Asthma Passive Digital Marker

A childhood asthma Passive Digital Marker (PDM) is an ML algorithm that is able to retrieve and synthesize pre-existing "passively" collected mother/child dyad prognostic data in "digital" electronic health record (EHR) to provide an objective and quantifiable "marker" of a child's risk (probability) and associated pathophysiological phenotype to inform clinician decision-making at point-of-care.

Intervention Type OTHER