Pharmacologic Pitfalls in Heart Failure: A Guide to Drugs that May Cause or Exacerbate Heart Failure. A European Journal of Heart Failure Expert Consensus Document
Bibliographic record
Abstract
Abstract Heart failure (HF) exerts a global health burden, often complicated by polypharmacy due to the frequent coexistence of cardiovascular and non-cardiovascular comorbidities. While guideline-directed medical therapy and devices have significantly improved outcomes, a range of commonly prescribed medications may inadvertently worsen HF or precipitate decompensation. This expert consensus statement provides a comprehensive overview of drugs known to cause or exacerbate HF, offering practical guidance for clinicians to identify and avoid harmful pharmacologic exposures in this vulnerable population. The review examines the pathophysiological mechanisms, clinical evidence, and guideline-based recommendations for several drug classes, including antidiabetic agents (e.g. thiazolidinediones, dipeptidyl peptidase-4 inhibitors), antiarrhythmics (particularly Class I and III), calcium channel blockers, non-steroidal anti-inflammatory drugs, antifungals (e.g. itraconazole, amphotericin B), macrolide antibiotics, antihypertensives (e.g. α1-blockers, centrally acting sympatholytics), neurological and psychiatric medications (e.g. carbamazepine, pregabalin, lithium), and selected anaesthetic and anticancer agents such as anthracyclines and vascular endothelial growth factor inhibitors. Each section addresses clinical scenarios where these medications may be contraindicated or require close monitoring. Importantly, this document emphasizes the need for individualized therapy, close review of medication regimens, and collaborative care to minimize iatrogenic harm. The goal is to empower clinicians, pharmacists and nurses to optimize HF treatment while reducing the risk of drug-induced deterioration. Awareness of these pharmacologic pitfalls is critical to improving clinical outcomes and minimizing preventable adverse events and HF hospitalizations.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".