Home intravenous diuretic administration for heart failure management: A scoping review
Bibliographic record
Abstract
BACKGROUND: Heart failure (HF) significantly impacts healthcare systems due to high rates of hospital bed utilization and readmission rates. Chronic HF often leads to frequent hospitalizations due to recurrent exacerbations and a decline in patient health status. Intravenous (IV) diuretic administration is essential for treating worsening HF. Emerging strategies include home-based IV diuretic therapy administration; however, limited practical implementation guidance is available. This scoping review aims to summarize the literature on home IV diuretic administration for HF patients, focusing on the interventions' characteristics, and facilitators and barriers to its implementation. METHODS: This review followed the scoping review framework proposed by Arksey and O'Malley and PRIMSA-ScR. A comprehensive search was conducted across six databases (CINAHL, the Cochrane Library, EMBASE, MEDLINE, PsychINFO and Scopus) and grey literature to identify English studies from inception to April 13, 2024. Two independent reviewers screened articles and resources for inclusion and data was extracted using a form created by the authors in Covidence. RESULTS: The search yielded 2,049 results, with nine studies meeting the inclusion criteria. Studies varied in design, including feasibility, pilot, observational, and pre/post-intervention evaluations. The majority were conducted in European countries, with sample sizes ranging from 12 to 96 patients receiving home IV diuretics for HF. Key implementation challenges include appropriate patient selection, IV cannulation competency of healthcare providers, and multi-disciplinary and multi-agency collaboration. CONCLUSIONS: Evidence on home IV diuretic administration practices for HF management remains limited. However, this scoping review suggests that commonalities across studies could form the basis for developing standard protocols in outpatient settings. Despite the lack of formal evidence-based guidelines, the findings also suggest that tailored, community-specific approaches and safe infusion guidance documents could enhance the efficacy and scalability of home IV diuretic therapy. Future research should focus on refining these strategies and exploring diuretic escalation methods beyond traditional acute care administration to optimize patient outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.016 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".