MO686THE EFFECT OF A CONTINUOUS TRAINING PROGRAM ON PERITONEAL DIALYSIS PATIENTS' SURVIVAL
Notice bibliographique
Résumé
Abstract Background and Aims Peritoneal Dialysis (PD) is a well-established method for dialysis of end stage kidney disease patients. Peritoneal membrane alters with time from several causes such as bioincompatible PD solutions, uremia, and the cumulative effect of peritonitis episodes. Each center follows a specific training program to prevent the appearance of peritonitis episodes. The aim of this study was to retrospectively evaluate the influence of proper and continuous training on the mortality of peritoneal dialysis patients. Method This is a single center retrospective study of 133 PD patients conducted for the time period 2009 – 2019 (10 years). The training course was taught one-on-one, nurse-to-patient at the initiation of dialysis and then once every 6 months at their regular visit or sooner if there was a peritonitis episode. The program included a rated questioner based on the Canadian Association of Nephrology Nurses for Nursing Standards and Practice Recommendations published on August 2008. The patients were divided into two groups according to the mean value (34) of their questioner sum (QS). Group A included 69 patients with mean age of 66 ± 15 years (36 M, 33 F) with mean PD duration of 45 ± 30 months and they achieved a score less than 34. Group B included 64 patients with mean age of 61 ± 18 years (42 M, 22 F) with mean PD duration of 62± 32 months and they achieved a score greater than 34. The cumulative all-cause survival of the PD patients was calculated by Kaplan Meier, was compared using Long Rang analysis and was also adjusted for their age, gender, the modality of PD applied, the presence of Diabetes and their level of education. Using Cox Regression, we tried to find independent risk factors such as the score they achieved in the questioner. The two groups were compared also for their overhydration and their frequency appearance of peritonitis or exit site infection. Results The cumulative survival using Kaplan-Meier analysis revealed statistically significant deference between the two groups (Log Rank p<0.001) with Group B (QS>34) achieving better survival. When the survival was adjusted for age, sex, Diabetes, PD modality the result remains the same. Trying to find among the total of our patients the possible risk factors for mortality, using Cox Regression analysis, their QS score (representing their training level for PD) was statistically significant (HR 0,931 {0.892, 0.971}, p=0.001) independent risk factor, as well as age and PD modality, for our patient survival. Additionally, Group B (QS>34) had statistically significant a smaller number of peritonitis episodes (p<0.001) and presence of peripheral edema (p<0.001). Conclusion In our study we concluded that continuous and monitored training of peritoneal dialysis patients has a significant effect on their survival and the frequency of peritonitis appearance.
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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 | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 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) | 0,002 | 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 ».