Contemporary trends in the utilization of second‐line pharmacological therapies for type 2 diabetes in the United States and the United Kingdom
Bibliographic record
Abstract
AIM: To examine trends of second-line glucose-lowering therapies among patients with type 2 diabetes (T2D) initiating first-line metformin in the United States and the United Kingdom, overall and by subgroups of cardiovascular disease (CVD) and calendar time. METHODS: Using the US Optum Clinformatics and the UK Clinical Practice Research Datalink, we identified adults with T2D who initiated first-line metformin or sulphonylurea monotherapy, separately, from 2013 to 2019. Within both cohorts, we identified patterns of second-line medications through June 2021. We stratified patterns by CVD and calendar time to investigate the impact of rapidly evolving treatment guidelines. RESULTS: We identified 148 511 and 169 316 patients initiating treatment with metformin monotherapy in the United States and the United Kingdom, respectively. Throughout the study period, sulphonylureas and dipeptidyl peptidase-4 inhibitors were the most frequently initiated second-line medications in the United States (43.4% and 18.2%, respectively) and the United Kingdom (42.5% and 35.8%, respectively). After 2018, sodium-glucose co-transporter 2 inhibitors and glucagon-like peptide-1 receptor agonists were more commonly used as second-line agents in the United States and the United Kingdom, although these agents were not preferentially prescribed among patients with CVD. Initiation of first-line sulphonylureas was much less common, and most sulphonylurea initiators had metformin added as the second-line agent. CONCLUSIONS: This international cohort study shows that sulphonylureas remain the most common second-line medications prescribed following metformin in both the United States and the United Kingdom. Despite recommendations, the use of newer glucose-lowering therapies with cardiovascular benefits remains low.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".