MO716: A Different Pet Test: The Relationship between Pet Ownership and Peritonitis Risk in the Peritoneal Dialysis Outcomes and Practice Patterns Study
Notice bibliographique
Résumé
Abstract BACKGROUND AND AIMS In the USA, 64% of homes have dogs or cats (38% dogs and 25% cats in 2017-8). As a home-based treatment, peritoneal dialysis (PD) patients run the risk of developing zoonotic diseases from their pets. The 2016 International Society for Peritoneal Dialysis Guidelines recommend that domestic animals should not be in the area while the PD exchange is performed. The primary aim of this study was to see how common it was for PD patients to have pets and to examine the association between pet ownership and peritonitis risk in the Peritoneal Dialysis and Outcomes Practice Patterns Study (PDOPPS). METHOD PDOPPS is an international prospective cohort study of adult PD patients across eight countries—Australia, Canada, Japan, New Zealand, South Korea, Thailand, the UK, and the USA. Patient demographics and comorbid conditions were collected at study enrolment and patient questionnaires collected information on household pets. Peritonitis episodes were collected using uniform and standardized data collection tools, procedures and processes. Risk of peritonitis was modelled using a Cox proportional hazards regression adjusted for patient demographics, 14 comorbid conditions, serum albumin, and residual urine volume and stratifying on country and prior peritonitis episodes during follow-up. RESULTS In total, 3655 PD patients provided information about household pets (out of a total of 4473 participants who filled out patient questionnaires amongst a total of 12 447 eligible patients). Among patients who reported on pet ownership, the percentage of patients owning only dogs was 19%, only cats 7%, and 6% with dogs and cats. Most countries, including the USA, had less than 50% of PD patient households having pets, lower than the US general population. Notable differences could be seen between countries in the profile of pets at home, including low numbers of pet ownership in South Korea and greater numbers of dogs than cats in the USA, Thailand, Japan and the UK (Fig. 1). Over a median follow-up for this cohort of 14 months and a total exposure time of 55 475 patient-months, 1347 peritonitis episodes were detected with an overall peritonitis rate of 0.29 episodes/patient-year. Having pets was associated with a hazard ratio of 1.09 (95% confidence interval 0.96–1.25) compared to no pet ownership. Figure 2 compares various combinations of pet ownership to PD patients with no pets as the reference group. However, among patients who lived alone, the peritonitis risk associated with pet ownership versus no pets appeared elevated, especially for people who owned both dogs and cats, although the P-value for the interaction was 0.72 (Fig. 2). CONCLUSION Utilizing this large cohort study, our results suggest limited increased peritonitis risk with pet ownership was seen among patients who live with others. However, our findings do suggest that for patients who live alone, higher peritonitis rates may be seen in those who have a pet. While it would be prudent to ensure maintenance of a high level of hygiene, our results suggest that pet ownership should not be an obstacle to choosing PD.
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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 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| 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 ».