Dihydropyridine Calcium Channel Blockers and Risk of Pancreatic Cancer: A Population‐Based Cohort Study
Bibliographic record
Abstract
Background Recent studies have reported that dihydropyridine calcium channel blockers (dCCBs) may increase the risk of pancreatic cancer, but these studies had methodological limitations. We thus aimed to determine whether dCCBs are associated with an increased risk of pancreatic cancer compared with thiazide diuretics, a clinically relevant comparator. Methods and Results We conducted a new user, active comparator, population-based cohort study using the UK Clinical Practice Research Datalink. We identified new users of dCCBs and new users of thiazide diuretics between 1990 and 2018, with follow-up until 2019. Cox proportional hazards models were used to estimate hazard ratios (HRs) with 95% CIs for pancreatic cancer, comparing dCCBs with thiazide diuretics. Models were weighted using standardized morbidity ratio weights based on calendar time-specific propensity scores. We also conducted secondary analyses by cumulative duration of use, time since initiation, and individual drugs and assessed for the presence of effect modification by age, sex, smoking status, body mass index, history of chronic pancreatitis, and diabetes. The cohort included 344 480 initiators of dCCBs and 357 968 initiators of thiazide diuretics, generating 3 360 745 person-years of follow-up. After a median follow-up of 4.5 years, the weighted incidence rate per 100 000 person-years was 37.2 (95% CI, 34.1-40.4) for dCCBs and 39.4 (95% CI, 36.1-42.9) for thiazide diuretics. Overall, dCCBs were not associated with an increased risk of pancreatic cancer (weighted HR, 0.93; 95% CI, 0.80-1.09). Similar results were observed in secondary analyses. Conclusions In this large, population-based cohort study, dCCBs were not associated with an increased risk of pancreatic cancer compared with thiazide diuretics. These findings provide reassurance regarding the long-term pancreatic cancer safety of these drugs.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".