Assessing hypotension incidence and dosing strategies of sacubitril/valsartan in real-world heart failure management: protocol for a retrospective, multicentre and cohort study
Bibliographic record
Abstract
Background: The burden of heart failure (HF) and hypertension in India underscores the need for effective management strategies. Sacubitril/valsartan, an angiotensin receptor neprilysin inhibitor (ARNi), has emerged as a pivotal therapy for HF with reduced ejection fraction (HFrEF). However, concerns about hypotension often hinder optimal dosing in clinical practice. The primary objective of this study is to observe the incidence of hypotension in HFrEF patients and to evaluate the best clinical practice to achieve an optimal tolerated dose of sacubitril/valsartan without treatment discontinuation. Secondary objectives include evaluating treatment outcomes, tolerability, and factors influencing dosing adjustments. Methods: This is the protocol of a retrospective, multicentre cohort study aimed at assessing real-world usage patterns of sacubitril/valsartan among Indian HFrEF patients. Patients aged 18-80 years diagnosed with HFrEF (left ventricular ejection fraction (LVEF) ≤40%) and initiated on sacubitril/valsartan between November 2023 and May 2024 will be included. Baseline and follow-up data, including demographics, medical history, and treatment outcomes, will be analysed using appropriate statistical tests. Data from approximately 150 healthcare facilities will be collected using a structured case report form (CRF). The study was initiated in February 2024. As of manuscript submission, 1039 individuals have been enrolled on the study. Data collection is expected to continue until the end of June 2024. Conclusions: This study aims to contribute valuable insights into optimizing sacubitril/valsartan therapy for HFrEF patients in India, addressing concerns about hypotension and dosage optimization. The study seeks to inform clinical practice and enhance patient care by elucidating real-world usage patterns and 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 | 0.041 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".