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A Data-Driven Intervention Framework for Improving Adherence to Growth Hormone Therapy Based on Clustering Analysis
Matheus Araújo1, Paula van Dommelen2, Jaideep Srivastava1
1Computer Science Department, University of Minnesota, Minneapolis, MN, USA.
Studies in Health Technology and Informatics
|November 19, 2021
Summary
A new traffic light system effectively identifies children with poor adherence to recombinant human growth hormone (r-hGH) therapy. This data-driven approach enables early intervention to improve treatment outcomes for growth hormone deficiency (GHD).
Area of Science:
- Endocrinology and Metabolism
- Health Informatics
- Pediatric Endocrinology
Background:
- Recombinant human growth hormone (r-hGH) therapy is crucial for treating growth hormone deficiency (GHD).
- Suboptimal patient adherence and persistence significantly hinder achieving full height potential in children undergoing r-hGH treatment.
- Real-world data and data-sharing agreements are essential for developing effective adherence monitoring tools.
Purpose of the Study:
- To develop and validate a data-driven clinical decision support system using "traffic light" visualizations for managing adherence risks in pediatric r-hGH therapy.
- To categorize patient adherence patterns into distinct risk levels (high, medium, low) for proactive intervention.
- To identify the most effective metrics and thresholds for an alert system to monitor r-hGH adherence.
Main Methods:
- Utilized real-world adherence data from 11,015 children receiving r-hGH therapy for ≥180 days, sourced from easypod™ connect.
- Employed cluster analysis with a Gaussian mixture model to categorize adherence patterns based on daily adherence and hours to next injection.
- Developed a traffic light system (green, yellow, red) to visualize adherence risk, optimizing thresholds using Area Under the Receiver Operating Characteristic Curve (AUC-ROC).
Main Results:
- The most effective adherence monitoring utilized the standard deviation of hours to the next injection, achieving an AUC-ROC of 0.85.
- Established optimal thresholds for daily adherence: >0.82 (high), 0.53-0.82 (medium), <0.53 (low).
- Defined optimal thresholds for hours to next injection: <27.18 (high), 27.18-34.01 (medium), >34.01 (low).
Conclusions:
- A practical, data-driven alert system using traffic light coding can effectively monitor r-hGH adherence in pediatric patients.
- Early identification of sub-optimally adherent patients allows for timely intervention, potentially improving treatment outcomes.
- This system empowers healthcare practitioners to proactively manage adherence and enhance the efficacy of growth hormone therapy.
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