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Machine learning-based prediction of medication refill adherence among first-time insulin users with type 2 diabetes
Ya-Lin Chen1, Phung-Anh Nguyen2, Chia-Hui Chien3
1Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA; Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan.
Diabetes Research and Clinical Practice
|December 4, 2023
Summary
Predicting insulin adherence in Type 2 Diabetes Mellitus (T2DM) patients is crucial. This study developed models to identify individuals likely to be non-adherent, aiding personalized care for better diabetes management.
Area of Science:
- Endocrinology and Metabolic Diseases
- Pharmacoeconomics and Health Outcomes Research
- Artificial Intelligence in Healthcare
Background:
- Type 2 Diabetes Mellitus (T2DM) prevalence is rising, necessitating effective long-term management strategies.
- Adherence to injectable medications like insulin is suboptimal, falling below the 80% threshold, impacting treatment efficacy.
- Identifying patients at risk of non-adherence is critical for proactive intervention in T2DM care.
Purpose of the Study:
- To develop and validate classification models for predicting insulin adherence in adult T2DM patients new to insulin therapy.
- To identify key predictors of insulin non-adherence among this specific patient population.
- To create a potential clinical decision support tool for healthcare providers.
Main Methods:
- Utilized clinical data from the Taipei Medical University Clinical Research Database (TMUCRD) spanning 2004-2020.
- Defined adherence as a Medication Possession Ratio (MPR) of at least 80%.
- Developed two XGBoost models using predictors across demographics, medications, comorbidities, laboratory data, and healthcare utilization.
Main Results:
- The predictive models achieved an Area Under the ROC Curve (AUROC) of 0.782 for internal testing and 0.771 for external testing.
- SHAP analysis identified the number of prescribed medications, outpatient visits, and laboratory data as significant predictors of insulin adherence.
- The study cohort comprised 4134 T2DM patients from Taiwan.
Conclusions:
- This pioneering study successfully developed predictive models for insulin adherence in adult T2DM patients initiating insulin.
- The models demonstrate potential as clinical decision support tools to identify patients at risk of non-adherence.
- Findings can guide healthcare providers in designing tailored educational interventions to improve insulin adherence and diabetes outcomes.
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