Concomitant Use of Sulfonylureas and β-Blockers and the Risk of Severe Hypoglycemia Among Patients With Type 2 Diabetes: A Population-Based Cohort Study
Bibliographic record
Abstract
OBJECTIVE: The hypoglycemic potential of β-blockers among users of sulfonylureas, drugs that strongly increase the risk of this potentially fatal adverse effect, is not well understood. Our population-based cohort study assessed the potential association between concomitant use of sulfonylureas and β-blockers versus use of sulfonylureas alone and the risk of severe hypoglycemia. RESEARCH DESIGN AND METHODS: Using the U.K. Clinical Practice Research Datalink Aurum, we included patients initiating sulfonylureas between 1998 and 2020, excluding those with β-blocker use in the past 6 months. Time-dependent Cox models estimated hazard ratios (HRs) with 95% CIs of severe hypoglycemia (hospitalization with or death resulting from hypoglycemia; ICD-10 codes E16.0, E16.1, and E16.2) associated with current concomitant use of sulfonylureas and β-blockers compared with current sulfonylurea use alone, adjusted for baseline confounders. We also compared current concomitant use of sulfonylureas and non-cardioselective versus cardioselective β-blockers. RESULTS: Our cohort included 252,869 initiators of sulfonylureas (mean age 61.3 years; 43% female). Median follow-up was 7.9 years. The crude incidence rate of severe hypoglycemia was 7.8 per 1,000 per year. Concomitant use of sulfonylureas and β-blockers was associated with an increased risk of severe hypoglycemia compared with sulfonylurea use alone (HR 1.53; 95% CI 1.42-1.65). There was no difference in the risk between concomitant use of sulfonylureas and noncardioselective β-blockers and concomitant use of sulfonylureas and cardioselective β-blockers (HR 0.95; 95% CI 0.74-1.24). CONCLUSIONS: β-blockers could further increase the risk of severe hypoglycemia when used concurrently with sulfonylureas. β-blocker cardioselectivity did not seem to play a major role in this regard.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".