What strategies do cardiologists employ for heart failure management? Insights from Indian clinical practice
Bibliographic record
Abstract
Background: Heart failure (HF) is a leading cause of morbidity and mortality in India, with ischemic heart disease (IHD) being a primary contributor, particularly in patients with reduced ejection fraction (HFrEF). Despite comprehensive guidelines, a gap exists between guideline-directed medical therapy (GDMT) and real-world practices. Methods: A cross-sectional survey of 476 cardiologists across India was conducted from April to June 2023 to evaluate current therapeutic approaches for managing HFrEF. The study assessed alignment with established guidelines, including the American college of cardiology/American heart association (ACC/AHA) recommendations. Results: HFrEF accounted for 40-60% of HF cases, typically diagnosed at advanced stages New York heart association (NYHA class III), with EF often reduced to 20-30%. While 94.5% of cardiologists supported NT-proBNP testing for HF management and 73.5% endorsed ARNi as first-line therapy, ARNi usage remained suboptimal at 20-60%. Most cardiologists (67%) preferred initiating quadruple therapy within 12-24 weeks of diagnosis, citing medication tolerance as a key barrier to achieving optimal treatment goals. Conclusions: This study highlights substantial gaps in the adoption of guideline-recommended therapies for HFrEF in India. Improved strategies are needed to address barriers to GDMT implementation and ensure timely interventions to enhance patient outcomes.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".