Use of Sacubitril/Valsartan Prior to Primary Prevention Implantable Cardioverter Defibrillator Implantation
Bibliographic record
Abstract
Background: Implantable cardioverter defibrillators (ICDs) are an adjunct to guideline-directed medical therapy for heart failure with reduced ejection fraction. The uptake of sacubitril/valsartan in this population is not well described. We report the uptake and factors associated with sacubitril/valsartan use in patients with left ventricular dysfunction undergoing ICD implantation. Methods: primary prevention ICD implantation between October 2015 and December 2021 (n = 422) at Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada. Pre-procedure sacubitril/valsartan use was determined. Logistic regression analysis was performed to examine factors associated with sacubitril/valsartan use. A Bayesian estimator of abrupt change was employed to determine a time period in which a change in the rate of sacubitril/valsartan use occurred. Results: Loop diuretic use (odds ratio [OR] = 2.20) and higher severity of New York Heart Association class symptoms (OR = 1.62) were associated with sacubitril/valsartan use. Sacubitril/valsartan use increased during the study period, to 59% in December 2021. This increase was larger among those aged ≥ 65 years (OR = 1.09). A change in the rate of sacubitril/valsartan use occurred 3 years after drug approval, 1 year after provincial drug coverage became available, and 6 months after being strongly recommended in clinical guidelines. Conclusions: In a contemporary cohort of ICD patients, sacubitril/valsartan use increased between 2015 and 2021, notably in those aged ≥ 65 years and after government drug coverage became available. Understanding barriers to sacubitril/valsartan use in ICD patients is recommended to improve clinical outcomes and survival in this population.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".