Proof-of-Concept Study of Using Supervised Machine Learning Algorithms to Predict Self-Care and Glycemic Control in Type 1 Diabetes Patients on Insulin Pump Therapy.

Autor:	Kurdi S; Department of Pharmacy Practice, College of Clinical Pharmacy, Imam Abdulrahman bin Faisal University, Dammam, Saudi Arabia., Alamer A; Department of Clinical Pharmacy, Prince Sattam Bin Abdulaziz University, Alkharj, Saudi Arabia; Center for Health Outcomes & PharmacoEconomic Research, University of Arizona, Tucson, Arizona. Electronic address: aa.alamer@psau.edu.sa., Wali H; Department of Pharmacy Practice, College of Clinical Pharmacy, King Faisal University, Al-Ahsa, Saudi Arabia., Badr AF; Department of Pharmacy Practice, King Abdulaziz University Faculty of Pharmacy, Jeddah, Saudi Arabia., Pendergrass ML; Banner-University Medicine Endocrinology and Diabetes Clinic, Tucson, Arizona; Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, College of Medicine, Tucson, Arizona; Department of Pharmacy Practice & Science, College of Pharmacy, The University of Arizona, Tucson, Arizona., Ahmed N; Department of Clinical Pharmacy, Prince Sattam Bin Abdulaziz University, Alkharj, Saudi Arabia., Abraham I; Center for Health Outcomes & PharmacoEconomic Research, University of Arizona, Tucson, Arizona., Fazel MT; Banner-University Medicine Endocrinology and Diabetes Clinic, Tucson, Arizona; Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, College of Medicine, Tucson, Arizona; Department of Pharmacy Practice & Science, College of Pharmacy, The University of Arizona, Tucson, Arizona.
Jazyk:	angličtina
Zdroj:	Endocrine practice : official journal of the American College of Endocrinology and the American Association of Clinical Endocrinologists [Endocr Pract] 2023 Jun; Vol. 29 (6), pp. 448-455. Date of Electronic Publication: 2023 Mar 08.
DOI:	10.1016/j.eprac.2023.03.002
Abstrakt:	Objective: Using supervised machine learning algorithms (SMLAs), we built models to predict the probability of type 1 diabetes mellitus patients on insulin pump therapy for meeting insulin pump self-management behavioral (IPSMB) criteria and achieving good glycemic response within 6 months. Methods: This was a single-center retrospective chart review of 100 adult type 1 diabetes mellitus patients on insulin pump therapy (≥6 months). Three SMLAs were deployed: multivariable logistic regression (LR), random forest (RF), and K-nearest neighbor (k-NN); validated using repeated three-fold cross-validation. Performance metrics included area under the curve-Receiver of characteristics for discrimination and Brier scores for calibration. Results: Variables predictive of adherence with IPSMB criteria were baseline hemoglobin A1c, continuous glucose monitoring, and sex. The models had comparable discriminatory power (LR = 0.74; RF = 0.74; k-NN = 0.72), with the RF model showing better calibration (Brier = 0.151). Predictors of the good glycemic response included baseline hemoglobin A1c, entering carbohydrates, and following the recommended bolus dose, with models comparable in discriminatory power (LR = 0.81, RF = 0.80, k-NN = 0.78) but the RF model being better calibrated (Brier = 0.099). Conclusion: These proof-of-concept analyses demonstrate the feasibility of using SMLAs to develop clinically relevant predictive models of adherence with IPSMB criteria and glycemic control within 6 months. Subject to further study, nonlinear prediction models may perform better. Competing Interests: Disclosure The authors have no multiplicity of interest to disclose. (Copyright © 2023. Published by Elsevier Inc.)
Databáze:	MEDLINE
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