Machine learning-based prediction of medication refill adherence among first-time insulin users with type 2 diabetes

Ya-Lin Chen; Phung-Anh Nguyen; Chia-Hui Chien; Min-Huei Hsu; Der-Ming Liou; Hsuan-Chia Yang

doi:10.1016/j.diabres.2023.111033

Machine learning-based prediction of medication refill adherence among first-time insulin users with type 2 diabetes

Diabetes Res Clin Pract. 2024 Jan:207:111033. doi: 10.1016/j.diabres.2023.111033. Epub 2023 Dec 2.

Authors

Ya-Lin Chen¹, Phung-Anh Nguyen², Chia-Hui Chien³, Min-Huei Hsu⁴, Der-Ming Liou⁵, Hsuan-Chia Yang⁶

Affiliations

¹ Department 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.
² Clinical Data Center, Office of Data Science, Taipei Medical University, Taipei, Taiwan; Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei Medical University, Taipei, Taiwan; Research Center of Health Care Industry Data Science, College of Management, Taipei Medical University, Taipei, Taiwan.
³ Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan; International Center for Health Information Technology (ICHIT), College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan; Office of Public Affairs, Taipei Medical University, Taiwan.
⁴ Office of Data Science, Taipei Medical University, Taipei, Taiwan; Graduate Institute of Data Science, College of Management, Taipei Medical University, Taipei, Taiwan.
⁵ Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan.
⁶ Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei Medical University, Taipei, Taiwan; Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan; International Center for Health Information Technology (ICHIT), College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan; Research Center of Big Data and Meta-Analysis, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan. Electronic address: lovejog@tmu.edu.tw.

PMID: 38049037
DOI: 10.1016/j.diabres.2023.111033

Abstract

Aims: The prevalence of Type 2 Diabetes Mellitus (T2DM) is projected to be 7 % in 2030. Despite its need for long-term diabetes care, the adherence rate of injectable medications such as insulin is around 60 %, lower than the acceptable threshold of 80 %. This study aims to create classification models to predict insulin adherence among adult T2DM naïve insulin users.

Methods: Clinical data were extracted from Taipei Medical University Clinical Research Database (TMUCRD) from January 1st, 2004 to December 30th, 2020. A patient was regarded as adherent if his/her medication possession ratio (MPR) was at least 80 %. Seven domains of predictors were created, including demographics, baseline medications, baseline comorbidities, baseline laboratory data, healthcare resource utilization, index insulins, and the concomitant non-insulin T2DM medications. We built two Xgboost models for internal and external testing respectively.

Results: Using a cohort of 4134 patients from Taiwan, our model achieved the Area Under the curve of the Receiver Operating Characteristic (AUROC) of the internal test was 0.782 and the AUROC of the external test was 0.771. the SHAP (SHapley Additive exPlanations) value showed that the number of prescribed medications, the number of outpatient visits, and laboratory data were predictive of future insulin adherence.

Conclusions: This is the first study to predict adherence among adult naïve insulin users. The developed model is a potential clinical decision support tool to identify possible non-adherent patients for healthcare providers to design individualized education plans.

Keywords: Insulin; Machine learning; Medication adherence; Observational study; Type 2 diabetes.

MeSH terms

Adult
Cohort Studies
Diabetes Mellitus, Type 2* / complications
Diabetes Mellitus, Type 2* / drug therapy
Female
Humans
Insulin / therapeutic use
Insulin, Regular, Human / therapeutic use
Machine Learning
Male
Medication Adherence
Retrospective Studies

Substances

Insulin
Insulin, Regular, Human