Renoprotective Benefit of Tolvaptan in Acute Decompensated Heart Failure Patients With Loop Diuretic-Resistant Status
Bibliographic record
Abstract
BACKGROUND: While reduction of accumulated body fluid using loop diuretics is a commonly used therapeutic option for acute heart failure (AHF), some patients, especially those with chronic kidney disease (CKD), show significantly poor treatment response to loop diuretics. Tolvaptan (TLV) has shown effectiveness against AHF in several studies. We have been using TLV for AHF treatment, and it displayed favorable outcome even in patients with CKD. This study aimed to assess the therapeutic effectiveness of TLV in AHF patients. METHODS: Ninety-nine AHF patients who were hospitalized were assessed retrospectively. Patients were divided into two groups: TLV treatment (TLV group, n = 39) and conventional treatment (non-TLV group, n = 60). We retrospectively examined the efficacy of TLV combination therapy for renal insufficiency complications and loop diuretic-resistant AHF patients, and the detail analysis was performed for heart failure with preserved ejection fraction (HFpEF) or reduced ejection fraction (HFrEF) in patients. RESULTS: Changes in serum electrolyte levels before and after the treatment were similar in both groups. Although the patients in the TLV group at baseline displayed significantly lower estimated glomerular filtration rate (eGFR) indicating renal insufficiency probably due to higher dose of loop diuretics, the incidence of worsening renal function (WRF) was significantly lower than those in non-TLV group in HFpEF (TLV: 2.5% vs. non-TLV: 15.4%, P = 0.01). We performed logistic regression analysis and found that TLV was an independent contributing factor for reducing WRF (odds ratio: 0.14, 95% CI: 0.02 - 0.98, P = 0.04). CONCLUSIONS: Our results suggest that TLV application in acute stage may be renoprotective for AHF patients with CKD, especially in HFpEF.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".