Therapy Gaps for Patients with Heart Failure and Reduced Kidney Function: A Prospective Cohort Study
Bibliographic record
Abstract
AIMS: Despite emerging evidence for new pharmacotherapies to improve outcomes in patients with heart failure (HF) and kidney dysfunction, data on contemporary HF therapy use in this population are lacking. This study evaluated contemporary longitudinal treatment patterns in patients with HF across the spectrum of kidney function and left ventricular ejection fraction (LVEF). METHODS: ) and HF with reduced (HFrEF), mildly reduced (HFmrEF) and preserved (HFpEF) ejection fraction. Clinical outcomes, incidence of hyperkalaemia (serum potassium > 5.5 mmol/L) and clinician-reported reasons for underutilizing HF therapies were examined. RESULTS: Median age was 68 (58 to 76) years; 29% were female; 54%, 37% and 9% had an eGFR of ≥60, 30 to <60 and <30, respectively. Among patients with eGFR ≥60, 95%, 94%, 75% and 15% were on a beta-blocker (BB), renin-angiotensin system inhibitor (RASi), mineralocorticoid receptor antagonist (MRA) and sodium glucose cotransporter-2 inhibitor, respectively. In patients with eGFR <30, corresponding baseline rates were 88%, 68%, 35% and 7%. Utilization rates were similar in patients with eGFR 30 to <60 compared with eGFR ≥60; however, fewer patients were on guideline-directed dose intensities with 44% versus 57% for RASi and 19% versus 26% for MRA. However, >90% of patients were on a BB, with similar utilization rates across HF and eGFR categories. Baseline ARNI use was 29%, 24% and 11% in eGFR ≥60, 30 to <60 and <30, respectively. Trends in HF therapy use persisted over 2 years. Among patients with eGFR <30, kidney dysfunction was the most frequently cited reason for underutilizing RASi. Patients with eGFR <60 experienced higher all-cause mortality, hospitalization and higher rates of hyperkalaemia. CONCLUSION: Gaps in HF therapy use persist in patients with comorbid kidney dysfunction. Targeted strategies to implement new therapies and improve adherence to HF treatments are necessary to improve outcomes in a highly comorbid and at-risk 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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".