P1062EXPANDED DIALYSIS (HDX): IS THERE AN IMPACT ON PATIENT REPORTED SYMPTOM?
Notice bibliographique
Résumé
Abstract Background and Aims High flux dialysis membranes sufficiently remove smaller sized uremic toxins however, the accumulation and retention of larger middle molecular weight toxins, which are associated with chronic inflammation, cardiovascular disease and suboptimal outcomes are poorly cleared. The recent advent of medium-cut-off dialysis membranes, labelled “expanded dialysis” (HDx) are permeable to molecules of larger size responsible for poor clinical outcomes. However, it remains unclear if HDx can directly impact the symptoms associated with hemodialysis (HD). Symptom burden plays a significant role in quality of life (QOL) and mortality rates in the HD population. The London Evaluation of Illness (LEVIL), an application-based platform has been developed to measure patient reported outcomes (PROM). In comparison to cross-sectional PROM’s, LEVIL more accurately represents the fluctuations in daily symptoms and the impact of intervention. LEVIL evaluates general well-being, energy, sleep, appetite, pain and breathing, all of which are outcomes of interest on symptom burden in chronic kidney disease. Our aim was to determine if HDx therapy had any effect on symtoms/QOL domains using LEVIL. Method 28 patients from two dialysis centers in London Ontario were consented to participate. Patients were required to be over 18 years of age and on conventional thrice weekly maintenance HD for at least three months. 23 participants completed study and analyzed (five lost for various reasons). Baseline (BL) symptom characteristics were obtained while using high flux membrane for two weeks. Symptoms continued to be measured throughout the 12 weeks of HDx therapy two-three times weekly using LEVIL. Laboratory biomarkers including beta-2 microglobulin and free-light chains were collected at baseline and after 12 weeks of HDx therapy. Results Patients were stratified into tertiles (high/middle/low) using mean values of BL symptoms scores in each domain (wellbeing, energy, sleep, appetite, pain, breathing). Those in the high BL group were labeled as “control”. Low and middle BL measures were further stratified into responders vs. non-responders (responders were considered to have a 50% increase in any symptom domain by ≥50%). Of those domains which responded to HDx, 76% also had low BL scores with 27% having middle BL scores. General wellbeing, energy and sleep were domains with the greatest response reaching statistical significance after eight weeks of therapy. HDx had limited effect on appetite, pain and breathing. Although stratification was per domain, overall, 74% of the population studied did respond in at least one domain, with some responding in as many as five. Conclusion HDx using Theranova (Baxter) shows the most benefit in domains with low BL measures. Additionally, not everyone who had low BL scores responded after 12 weeks of therapy, leaving us to question whether HDx may have a latent effect in some individuals/populations. Those who had no response to therapy in certain domains also had greater baseline quality of life respectively. This information may assist in decision making/rationale for the utilization and implementation of such therapy. Although more work is required to further stratify symptoms in relation to demographic/biochemical finding and clinical outcomes. It is evident that HDx improves patient reported symptoms and QOL.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».