Academic Journal
Estimating prevalence and predictors of glucose-lowering overtreatment among older adults with type 2 diabetes in long-term care and community settings: a machine learning-based cohort study.
| Τίτλος: | Estimating prevalence and predictors of glucose-lowering overtreatment among older adults with type 2 diabetes in long-term care and community settings: a machine learning-based cohort study. |
|---|---|
| Συγγραφείς: | Carney G; The University of British Columbia, Vancouver, British Columbia, Canada Gregory.Carney@ubc.ca.; Department of Anesthesiology, Pharmacology and Therapeutics, The University of British Columbia, Vancouver, British Columbia, Canada., Burnett S; The University of British Columbia, Vancouver, British Columbia, Canada., Ambasta A; Department of Anesthesiology, Pharmacology and Therapeutics, The University of British Columbia, Vancouver, British Columbia, Canada., Thompson W; Department of Anesthesiology, Pharmacology and Therapeutics, The University of British Columbia, Vancouver, British Columbia, Canada.; Research Unit of General Practice, University of Southern Denmark, Odense, Denmark., Lapp L; Department of Anesthesiology, Pharmacology and Therapeutics, The University of British Columbia, Vancouver, British Columbia, Canada., Dormuth C; Department of Anesthesiology, Pharmacology and Therapeutics, The University of British Columbia, Vancouver, British Columbia, Canada. |
| Πηγή: | BMJ open [BMJ Open] 2026 Mar 13; Vol. 16 (3), pp. e106707. Date of Electronic Publication: 2026 Mar 13. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: BMJ Publishing Group Ltd Country of Publication: England NLM ID: 101552874 Publication Model: Electronic Cited Medium: Internet ISSN: 2044-6055 (Electronic) Linking ISSN: 20446055 NLM ISO Abbreviation: BMJ Open Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: [London] : BMJ Publishing Group Ltd, 2011- |
| Ιατρικοί όροι (MeSH): | Diabetes Mellitus, Type 2*/drug therapy , Diabetes Mellitus, Type 2*/epidemiology , Diabetes Mellitus, Type 2*/blood , Hypoglycemic Agents*/therapeutic use , Overtreatment*/statistics & numerical data , Machine Learning*, British Columbia/epidemiology ; Glycated Hemoglobin/analysis ; Long-Term Care/statistics & numerical data ; Sulfonylurea Compounds/therapeutic use ; Humans ; Female ; Aged ; Retrospective Studies ; Male ; Aged, 80 and over ; Predictive Learning Models ; Prevalence ; Prediction Algorithms |
| Περίληψη: | Objective: To estimate the prevalence of potential overtreatment of type 2 diabetes mellitus (T2DM) among older adults and to develop and compare predictive models to identify patient and physician characteristics associated with overtreatment. Design: Population-based retrospective cohort study with predictive modelling. Setting: A province-wide, publicly funded healthcare system in British Columbia, Canada, using linked administrative health claims data from 2016 to 2023. Participants: Residents of long-term care facilities over age 65, and community-dwelling individuals over age 75, with a diagnosis of T2DM and a glycated haemoglobin (A1C) laboratory value ≤7.0%. Participants were required to have ≥365 days of continuous provincial health insurance coverage prior to their index A1C test. Patients receiving palliative care and those with missing physician information were excluded. Primary and Secondary Outcome Measures: Potential overtreatment of T2DM, defined a priori as overlapping prescriptions for ≥2 glucose-lowering medications or ≥1 insulin or sulfonylurea dispensing within 90 days after the index A1C test.Model performance outcomes included discrimination (area under the curve (AUC), sensitivity, specificity, positive predictive value and negative predictive value). Performance metrics were calculated with 95% CIs using a 25% temporally distinct test dataset (2021-2023). No changes were made to outcome definitions after protocol development. Results: Among 133 773 patients with an A1C≤7.0%, 38 074 (28.5%) were classified as overtreated. These patients had a mean age of 79.6 years, were 47% female, and had a median A1C of 6.4%. The gradient boost model was the best performing model overall, using a combination of expert-selected variables and data-driven variables, achieving an AUC of 0.87, sensitivity of 0.81 and negative predictive value of 0.89. The top predictors of overtreatment included use of blood glucose test strips, A1C test volume, polypharmacy, specialist involvement and measures of diabetes severity. Conclusions: Overtreatment of T2DM was prevalent among older adults in our cohort. Machine learning algorithms that integrate clinical expertise with data-driven variable selection performed the best in predicting T2DM overtreatment. We identified several patient and physician characteristics as key contributors that may inform future clinical practice and quality improvement initiatives, although external validation is required before clinical implementation. (© Author(s) (or their employer(s)) 2026. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ Group.) |
| Competing Interests: | Competing interests: None declared. |
| References: | Lancet Oncol. 2019 May;20(5):e262-e273. (PMID: 31044724) J Am Med Dir Assoc. 2024 Mar;25(3):439-447.e18. (PMID: 38237904) Eur J Cardiothorac Surg. 2018 Aug 1;54(2):203-208. (PMID: 29741602) Stat Med. 2015 Sep 20;34(21):2941-57. (PMID: 25980520) Afr J Prim Health Care Fam Med. 2024 Dec 18;16(1):e1-e4. (PMID: 39846108) JAMA. 2016 Mar 8;315(10):1034-45. (PMID: 26954412) J Am Med Dir Assoc. 2021 Sep;22(9):1889-1897.e5. (PMID: 34004183) Ann Intern Med. 2009 Dec 15;151(12):854-60. (PMID: 20008761) Diabetes Obes Metab. 2016 Sep;18(9):892-8. (PMID: 27160505) Pharmaceut Med. 2022 Oct;36(5):295-306. (PMID: 35904529) Clin Rheumatol. 2021 Jan;40(1):11-23. (PMID: 32504192) Aging Dis. 2015 Mar 10;6(2):156-67. (PMID: 25821643) J Clin Med. 2022 Aug 31;11(17):. (PMID: 36079064) Biomedicines. 2022 Sep 05;10(9):. (PMID: 36140289) Biomedicines. 2023 Feb 16;11(2):. (PMID: 36831118) Diabetes Care. 2012 Dec;35(12):2650-64. (PMID: 23100048) BMJ. 2015 Dec 08;351:h6138. (PMID: 26646052) Diabetes Care. 2013 Nov;36(11):3535-42. (PMID: 23900589) JAMA Intern Med. 2015 Mar;175(3):356-62. (PMID: 25581565) Biomedicines. 2022 Sep 13;10(9):. (PMID: 36140369) JAMA. 2018 Apr 3;319(13):1317-1318. (PMID: 29532063) BMJ. 2024 Apr 16;385:e078378. (PMID: 38626948) BMJ Open Diabetes Res Care. 2022 Nov;10(6):. (PMID: 36356988) J Diabetes Sci Technol. 2015 Jan;9(1):86-90. (PMID: 25316712) JAMA Intern Med. 2014 Jul;174(7):1116-24. (PMID: 24838229) BMJ. 2019 Nov 5;367:l5887. (PMID: 31690574) Z Evid Fortbild Qual Gesundhwes. 2016 Oct;115-116:33-48. (PMID: 27837958) PLoS One. 2014 Jan 10;9(1):e84217. (PMID: 24427282) J Am Geriatr Soc. 2023 Aug;71(8):2657-2661. (PMID: 37070246) JAMA Intern Med. 2016 Jul 1;176(7):969-78. (PMID: 27273792) BMC Med. 2019 Dec 16;17(1):230. (PMID: 31842878) PLoS One. 2013 Nov 15;8(11):e78962. (PMID: 24260139) Front Pharmacol. 2021 Jan 14;11:568659. (PMID: 33519433) Can Fam Physician. 2017 Nov;63(11):832-843. (PMID: 29138153) Patient Prefer Adherence. 2019 Oct 18;13:1775-1783. (PMID: 31695339) Diabetes Spectr. 2022 Summer;35(3):344-350. (PMID: 36082014) Front Pharmacol. 2020 Jul 16;11:1028. (PMID: 32765261) Diabetes Care. 2021 Apr;44(4):915-924. (PMID: 33541857) |
| Contributed Indexing: | Keywords: Diabetes Mellitus, Type 2; Machine Learning; PUBLIC HEALTH; Polypharmacy; Primary Care; Quality Improvement |
| Substance Nomenclature: | 0 (Hypoglycemic Agents) 0 (Glycated Hemoglobin) 0 (Sulfonylurea Compounds) |
| Entry Date(s): | Date Created: 20260317 Date Completed: 20260711 Latest Revision: 20260813 |
| Update Code: | 20260813 |
| PubMed Central ID: | PMC12993340 |
| DOI: | 10.1136/bmjopen-2025-106707 |
| PMID: | 41840748 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 2044-6055 |
|---|---|
| DOI: | 10.1136/bmjopen-2025-106707 |