Peritoneal Ultrafiltration for Heart Failure: Lessons from a Randomized Controlled Trial
Bibliographic record
Abstract
Peritoneal ultrafiltration (PuF) has been employed for severe heart failure (HF), but evidence for its benefit is lacking. The Peritoneal Dialysis for Heart Failure (PDHF) study was a multicenter prospective randomized controlled trial which aimed to investigate this issue. The trial stopped early due to inadequate recruitment. We describe methods, trial activity, and lessons learned.The trial aimed to recruit 130 participants with severe diuretic-resistant HF (New York Heart Association [NYHA] 3/4) and chronic kidney disease (CKD) stage 3/4 on optimal medical treatment for ≥ 4 weeks from 6 UK centers. Participants were randomized to either continuation of conventional HF treatment or to additionally receiving PuF (1 overnight exchange using Icodextrin dialysate). Primary outcome was change in 6-minute walk test (6MWT) between baseline and 28 weeks (end of trial). Secondary outcomes were changes in patient reported quality of life as assessed by the Kansas City Cardiomyopathy Questionnaire, short form 36 (SF 36) health survey results, hospitalization, and mortality.Over a 2-year period, 290 patients were screened from which only 20 met inclusion criteria and 10 were recruited. Reasons for ineligibility were fluctuating estimated glomerular filtration rate (eGFR), suboptimal HF treatment, frailty, and patients being too unwell for randomization. Barriers to recruitment included patient frailty, with some participants considered only when they were at end of life, unwillingness to engage in an invasive therapy, and suboptimal coordination between cardiology and renal services. This is a challenging patient group in which to perform research, and lessons learned from the peritoneal dialysis (PD)-HF trial will be helpful in the planning of future studies in this area.
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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 | 0.101 | 0.179 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.008 |
| 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".