Association between calcium channel blockers prescription and outcome according to left ventricular ejection fraction: data from a real-world heart failure population
Bibliographic record
Abstract
Abstract Background Controversy exists on the safety of dihydropyridine calcium channel blockers (CCB) in heart failure (HF), especially in patients with reduced ejection fraction (HFrEF). We aimed to evaluate CCB prescription trends over time and related outcomes across the spectrum of EF. Methods We screened for inclusion outpatients with chronic HF prospectively enrolled in the nationwide observational IN-HF registry from 1998 to 2022. We used Cox regression methods to analyze time-to-event outcomes in patients on dihydropyridine CCB. The primary outcome was the composite of all-cause mortality and cardiovascular hospitalization at 1-year. Results We included 15785 outpatients. 10829 had an EF ≤40% (HFrEF, 69%) and 4956 had an EF>40% (HFmrEF/HFpEF, 31%). The median age was 69; 26.6% were females. Overall, a DHP CCB was prescribed to 1458 patients (9.1%). CCB administration was twice as prevalent in patients with HFmrEF/HFpEF (n=771) than in those with HFrEF (n=687) (respectively 13.9% vs 7.1%, p<0.001). CCB prescription rates increased over time (p<0.001). Patients who received CCB were older, more comorbid, and had a higher EF than those who were not prescribed CCB. After multivariable adjustment, CCB prescription was associated with a higher risk of the primary outcome among the overall cohort (HR 1.19, 95% CI 1.05-1.36, p=0.008), and HFrEF patients (HR 1.20, 95% CI 1.02-1.41, p=0.025), while no adverse effect was observed in HFmrEF/HFpEF patients (p=0.159). Conclusion In HFmrEF/HFpEF, CCB use was twice as likely than in HFrEF and appeared to be safe. In HFrEF, CCB use was not rare and was associated with worse outcomes.central illustration forest plot
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".