Abstract 12549: What is the Efficacy of New Therapies in Black Patients With Heart Failure and a Reduced Ejection Fraction? A Systematic Review and Meta-Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
Introduction: Evaluating the efficacy of newer medical therapies in black patients with heart failure with reduced ejection fraction (HFrEF) remains an important and unanswered question. We conducted a systematic review and meta-analysis of randomized controlled trials (RCTs) in HFrEF to compare outcomes in black versus non-black patients with a specific focus on new therapies, namely ARNIs and SGLT2 inhibitors. Methods: Medline, Embase and Cochrane CENTRAL were searched from inception until May 2022. Pairs of reviewers independently identified RCTs that 1) compared either an SGLT2 inhibitor or an ARNI to placebo/standard of care in HFrEF patients and 2) reported outcomes stratified by race. Outcomes were pooled using the Generic Inverse Variance or Mantel-Haenszel models, and risk of bias was assessed using the Cochrane tool. Results: Four RCTs (n=17,797; 6.6% black) were identified, all of which were published in the past decade. In the placebo/control arm, black patients had a higher rate of heart failure hospitalization or cardiovascular death compared to non-black/white patients (OR: 1.52, 95% CI: 1.26, 1.84; absolute difference: 81, [95% CI: 43, 124] more events per 1,000 patients). In two RCTs, there was a trend towards a greater reduction in the composite of cardiovascular death or heart failure hospitalization with SGLT2 inhibitors in black patients (n=483; RR: 0.61, 95% CI: 0.45, 0.83) compared to white patients (n=6,445; RR: 0.84, 95% CI: 0.75, 0.95; p-interaction=0.06). In two RCTs, treatment with an ARNI was associated with reductions in the composite of cardiovascular death or heart failure hospitalization in both black patients (n=744; HR: 0.67, 95% CI: 0.40, 1.11) and non-black/white patients (n=6,109; HR: 0.80, 95% CI: 0.72, 0.89; p-interaction= p=0.49). Conclusions: Black patients are poorly represented in contemporary heart failure trials, and have worse outcomes compared with non-black patients. Newer therapies such as ARNIs and SGLT2 inhibitors are efficacious in black patients. SGLT2 inhibitors may afford greater risk reduction in black compared to non-black patients.
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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 | 0.027 | 0.062 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.027 | 0.028 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".