|
1
|
Chen YL, Nguyen PA, Chien CH, Hsu MH, Liou DM, Yang HC. Machine learning-based prediction of medication refill adherence among first-time insulin users with type 2 diabetes. Diabetes Res Clin Pract 2024; 207:111033. [PMID: 38049037 DOI: 10.1016/j.diabres.2023.111033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Revised: 09/05/2023] [Accepted: 11/30/2023] [Indexed: 12/06/2023]
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.
Collapse
Affiliation(s)
- Ya-Lin Chen
- 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
| | - Phung-Anh Nguyen
- 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
| | - Chia-Hui Chien
- 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
| | - Min-Huei Hsu
- Office of Data Science, Taipei Medical University, Taipei, Taiwan; Graduate Institute of Data Science, College of Management, Taipei Medical University, Taipei, Taiwan
| | - Der-Ming Liou
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
| | - Hsuan-Chia Yang
- 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.
| |
Collapse
|