1455-P: Evaluating Opportunities for Deprescribing in the Elderly and Extreme Elderly with Diabetes
Bibliographic record
Abstract
Individualised approaches to diabetes management are essential so that an optimal balance is realised between the risk and benefits of treatment. In the elderly (E, 75-84 years) and extreme elderly (EE, ≥ 85 years) treatment adjustment is the cornerstone of management, in order to provide medicines that deliver efficacy, and to de-prescribe agents to limit iatrogenic adverse effects. To assess the opportunity to de-prescribe and better target prescribing, we performed a retrospective audit of 1,806 patients (E, n=1,649; EE, n=157). Data are presented as E and EE respectively. The majority had T2DM (99.1%), with long duration (median, IQR 13.9, 7.4-21.7; 17.5, 10.3-25.1 years); and were overweight (mean ± SD BMI 28.7 ± 5.4; 27.0 ± 4.5 kg/m2). Over 50% used polypharmacy for glycaemic control; 37.9% and 40.8% used insulin +/- tablets. 39.4% of E and 33.3% of EE achieved an HbA1c of <7.0%, with HbA1c 7.5 ±1.4% and 7.8 ± 1.6% (mean± SD NGSP units). Over half (55.8%) of the EE group with an HbA1c <7.0% were treated with insulin and/or sulphonyureas, whereas only 38.2% of E were treated with these agents (p=0.01). While reported severe hypoglycaemia was uncommon (1.5% E and 2.8% EE), 35% of EE patients using insulin and/or sulphonyureas experienced hypoglycaemia. Metformin had appropriately been ceased in all but 7 of the 63 patients with an eGFR < 30 ml/min/1.73m2. While lipid profiles were favourable (HDL: 1.3 ± 0.4 mmol/L; LDL: 2.3 ± 0.9 mmol/L, Trig: 1.5, IQR: 1.1-2.1 mmol/L and HDL: 1.3 ± 0.3 mmol/L; LDL: 2.3 ± 1.0 mmol/L, Trig: 1.5, IQR: 1.0-2.1 mmol/L), lipid lowering therapy was used less frequently in EE (51.0%) versus E (62.6%) (p=0.004). Only 1 in 2 patients with known macrovascular disease were on anti-platelet treatment. This data demonstrates the potential over treatment of glycaemia in the EE specifically in hypoglycaemia inducing agents, highlighting the need to consider de-prescribing. Conversely, under-utilisation of evidence-based medication to address macrovascular risk is also evident. Disclosure S.A. Sharp: None. J. Overland: None. M. McGill: None. L.M. Molyneaux: None. T. Wu: Advisory Panel; Self; Boehringer Ingelheim International GmbH, Eli Lilly and Company, Novo Nordisk A/S, Sanofi. Speaker's Bureau; Self; AstraZeneca, Boehringer Ingelheim International GmbH, Eli Lilly and Company, Merck Sharp & Dohme Corp., Novo Nordisk A/S, Sanofi. S.M. Twigg: Advisory Panel; Self; Abbott, Boehringer Ingelheim Pharmaceuticals, Inc., Novo Nordisk Inc., Sanofi-Aventis. Board Member; Self; AstraZeneca. Research Support; Self; Abbott. Speaker's Bureau; Self; Abbott, AstraZeneca, Merck Sharp & Dohme Corp., Novo Nordisk Inc., Sanofi-Aventis. J. Wong: Advisory Panel; Self; Sanofi. Speaker's Bureau; Self; AstraZeneca, Lilly Diabetes.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".