#443 LEVIL evaluates the impact of HDx on HR-QoL and symptoms, including durability-of-effect and variability
Bibliographic record
Abstract
Abstract Background and Aims Current hemodialysis (HD), utilizing conventional high-flux dialyzers, is handicapped by clearance limitations (larger-sized uremic toxins), which contribute to poor health-related quality-of-life (HR-QoL) and symptom-burden. Recent international consensus and guideline-setting efforts have explicitly prioritized identification and management of symptoms and subjective experience, whilst also acknowledging the lack of tools available to fully appreciate and continuously monitor these challenges. This study aimed to utilize dynamic patient-reported-outcome-measurement tool (PROM), London Evaluation of Illness (LEVIL-developed in-house), to iteratively interrogate patient-experience and to confirm previously reported HR-QoL benefits of the use of expanded hemodialysis (HDx) using medium cut-off dialyzer (extending the spectrum of uremic toxins being addressed). Furthermore, this study aimed to extend appreciation of effects on patient subjective experience to include durability-of-effects, variability of symptom-measures and impact of HDx withdrawal. Methods We conducted a multi-centre interventional study in 47 patients established on conventional thrice weekly centre-based HD in Ontario, Canada. Study protocol was 15-months in length with five phases 1) one-month observation (high-flux-HD), 2) three-months HDx 3) two-month wash-out (high-flux-HD), 4) six-months HDx, 5) three-month wash-out (high-flux-HD). HR-QoL and symptom-burden were evaluated using LEVIL throughout study. Results HDx-therapy improved HR-QoL [p 0.0006 (19% from baseline)] and a variety of symptoms including general wellbeing [p 0.005 (23%)], energy [p 0.004 (33%)], sleep-quality [p 0.001 (33%)], pruritus [0.003 (30%)], pain [p 0.01 (19%)], restless leg syndrome [p 0.0006 (15%)], mood [p 0.02 (12%)], appetite [ p 0.03 (9%)], breathlessness [p 0.001 (9%)], and HD-recovery [p 0.004 (26%)]. Response was more pronounced in those with poorer HR-QoL and higher symptom-burden. Improvements were durable over time with continued use with less symptom-variability (range 10-35% improvement). Improvements diminished gradually with return to high-flux-HD. The drivers of poor HR-QoL were largely general-wellbeing, energy, sleep-quality, pruritus, and bodily pain. Conclusion Use of a dynamic PROM effectively allowed appreciation of HR-QoL burden in HD-patients. Use of HDx-therapy results in improved patient subjective outcomes that are durable and associated with significantly lower variation in important symptom-domains than patients experience with receiving conventional high-flux-HD.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".