Use of Guideline-Directed Medical Therapy in Patients Aged ≥ 65 Years After the Diagnosis of Heart Failure: A Canadian Population-Based Study
Bibliographic record
Abstract
Background: Guideline-directed medical therapy (GDMT) improves clinical outcomes in patients with heart failure with reduced ejection fraction (HFrEF). Despite its proven efficacy, GDMT is underutilized in clinical practice. The current study examines GDMT utilization after incident hospitalization for HF to promote medication initiation, and titration to target dosing within a reasonable time period. Methods: This observational study identified 66,372 patients with HFrEF who were aged ≥ 65 years and had an incident HF hospitalization, using administrative health data (2013-2018). GDMT (angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, angiotensin receptor-neprilysin inhibitors, β-blockers (BB), and mineralocorticoid receptor antagonists ) received within the 6 months after hospitalization was evaluated by monitoring therapy combinations, optimal dosing (proportion receiving ≥ 50% of the target dose for these inhibitors and blockers, and any dose of MRA), and maximal and last dose assessed, and by use of a GDMT intensity score. Results: Among patients with HFrEF, 4768 (7.2%) were on no therapy, 17,184 (25.9%), were on monotherapy, 30,912 (46.6%) were on dual therapy, and 13,508 (20.4%) were on triple therapy. Only 8747 (13.2%) and 5484 (8.3%) achieved optimal GDMT based on the maximum dose and the last dispensed dose, respectively, within 6 months postdischarge. Finally, 38,869 (58.6%) achieved < 50% of the maximum intensity score, 23,006 (34.7%) achieved between 50% and 74% of the maximum intensity score, and 4497 (6.8%) achieved a score that was ≥ 75% of the maximum intensity score. Conclusions: Current pharmacologic management for patients with HFrEF does not align with the Canadian guidelines. Given this gap in care, innovative strategies to optimize care in patients with HFrEF are needed.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".