Outpatient Intravenous Diuretic Clinic: An Effective Strategy for Management of Volume Overload and Reducing Immediate Hospital Admissions
Bibliographic record
Abstract
BACKGROUND: Heart failure (HF) readmissions pose a major burden to patients and the healthcare system. We evaluated whether outpatient intravenous (IV) diuretic clinic is a safe and effective strategy to reduce HF hospitalizations. METHODS: We reviewed 34 clinic encounters with 27 unique patients (median age 72) who had volume overload refractory to oral diuretics that were treated with IV furosemide in the outpatient clinic. One patient (2.9%) was admitted to the hospital directly, and the rest were discharged home. RESULTS: More than 80% of the patients had continued weight loss for 7 days (median weight loss 5.4 lb). During the median follow-up period of 15 months, 15 patients (56%) had subsequent HF hospitalizations. HF admission was delayed by a median of 22 days from the clinic visit and 138 days from the previous HF admission prior to clinic visit. Estimated cost saving per admission avoided was $10,395. One patient developed severe hypokalemia (< 3.0 mmol/L), and the remaining had no adverse events. CONCLUSION: Outpatient IV diuresis is effective and well tolerated. It leads to significant weight loss, persisting in the majority of patients for 7 days. In select patients, it should be considered as a strategy to rapidly improve symptoms, reduce hospitalizations and decrease costs.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".