Rates of overtreatment and deprescribing of antihyperglycemics among long‐term care residents in British Columbia
Bibliographic record
Abstract
Over 25% of older adults live with type 2 diabetes (T2DM).1 Current guidelines discourage intensive T2DM treatment in older adults with functional limitations, dementia, and/or frailty, such as those living in long-term care (LTC). Intensive T2DM treatment has unclear benefit in this population, increases risk of harm, and may have a deleterious effect on quality of life.1, 2 Diabetes Canada guidelines recommend moderate control in this population (e.g., HbA1c targets 7.1%–8.5% or higher), and to avoid sulfonylureas (SUs) or insulin.3 Despite unclear benefit and increased risk of harm, older adults in LTC may be overtreated for T2DM.4-8 People that are overtreated may benefit from stopping or reducing the dose of antihyperglycemics ("deprescribing").9, 10 This examined rates of overtreatment and deprescribing of antihyperglycemics at urban LTC facilities in British Columbia, Canada. Residents with an HbA1c available at baseline were categorized as overtreated if they had a baseline HbA1c of <7% (on any antihyperglycemic[s]) or if they had a baseline HbA1c of <7.5% on a SU or insulin. Deprescribing was defined as having an antihyperglycemic stopped or the dose reduced without having another antihyperglycemic medication started or increased. We captured the proportion of antihyperglycemic medications that were changed as well as the proportion of individual residents that had an antihyperglycemic change during the follow up period. We used descriptive statistics to summarize the data. The study was approved by the University of British Columbia (UBC) Research Ethics Board (REB) and received Providence Health Care Institutional Approval (UBC REB # H22-01575). Of 630 residents in the five LTC homes, 120 residents met inclusion criteria (Table 1). Among residents with an HbA1c available (n = 85), 34/85 residents (40%) met the criteria for being overtreated. We found that 17/34 (50%) residents in the overtreated group had an antihyperglycemic stopped during follow-up and <5 had a dose reduced, while 9/51 (18%) of the not overtreated group had an antihyperglycemic stopped and 5/51 (10%) had a dose reduced. This corresponded to a deprescribing rate of 50% in the overtreated group and 27% in the not overtreated group, over 6 months (Figure 1). In the overtreated group, <5 residents had a dose increased or a new antihyperglycemic started over the study period. In the not overtreated group, the dose was increased for 6/69 (9%) of antihyperglycemics used at baseline and at least one new antihyperglycemic was started over the study period in 17/51 (33%) residents. Antihyperglycemics were started and subsequently stopped over the study period for 5/34 (15%) residents in the overtreated group compared to 10/51 (19%) residents in the not overtreated group. Medications were stopped and subsequently restarted in <5 residents in the overtreated group and <5 residents in the not overtreated group. Our results suggest that overtreatment of T2DM continues to occur in LTC. The rates of overtreatment we observed are consistent with other published studies among LTC residents in Canada and the United States.4-8 We found higher rates of deprescribing compared to a recent study in US LTC homes, which may be explained by our longer follow-up period.8 Limitations included missing HbA1c data for around 30% of residents with diabetes. We did not capture ethnicity, socioeconomic factors, and other factors that could affect T2DM management. Data collection was limited to urban Vancouver area LTC homes. Deprescribing of antihyperglycemics in LTC appears to be safe, with a 2022 US cohort study finding deintensification was not associated with an increased risk of adverse events (ED visits, hospitalizations, death) at 60 days.10 However, scaling back T2DM care can be challenging for both healthcare providers and people with T2DM.11 Most healthcare providers and people with T2DM are open to considering antihyperglycemic deprescribing but require support, tools, and guidance to do so.11, 12 Efforts that promote individualizing T2DM care in LTC continue to be warranted. This may include knowledge translation efforts for existing guidance and/or quality improvement or implementation strategies.1, 3 Concept and design: Wade Thompson, Aaron Tejani. Acquisition of data: Wade Thompson, Aaron Tejani, Isla Drummond, Jeffrey Pan, Alixandra Logan, AmirHossein Moradi, Maric Son, Heather Brodoway, Kanika Khosla, Kruti Shukla. Analysis and interpretation of data: All. Manuscript preparation: All. The authors would like to acknowledge Anthony Tung for his assistance with drug utilization data. None to declare. This article did not receive any financial support and thus no sponsor had a role in the study. No sources of funding.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".