#3493 Two decades of dialysis in 41 countries: the new MONitoring Dialysis Outcomes (MONDO) initiative dataset
Notice bibliographique
Résumé
Abstract Background and Aims Data captured during dialysis care delivery can be used for monitoring outcomes yet is unable to distinguish the influences of local practice patterns versus pathophysiology. The MONitoring Dialysis Outcomes (MONDO) initiative is an academia-industry collaboration whereby providers can contribute data to an anonymized dataset used for joint research purposes. We present key demographics, cause of end-stage kidney disease (ESKD), and regional information to provide an understanding of patient characteristics in the new MONDO dataset. Method Data from Jan 2000–Dec 2019 on 366 fields was captured longitudinally on a per patient per observation level (e.g., each laboratory, treatment, value) with a universal data structure. Data anonymization was performed in alignment with recommendations from a re-identification risk determination (Privacy Analytics, Ontario, CA). The approach satisfies internal best practices, such as ISO/IEC 27559. Ethics approval for the MONDO dataset was obtained in the U.S., with data de-identified prior to analysis to ensure compliance with relevant guidelines. Final datasets were consolidated and hosted by the Renal Research Institute. Results The dataset includes information from 292,531 patients in 41 countries across 20 years. Data was contributed from nine distinct providers. Male represents 58.9% and the age distribution of dialysis patients worldwide showed most were aged 45–64 years (38.3%), followed by 65–74 years (23.6%), and ≥75 years (19.2%). Younger age groups, including 18–44 years (17.3%) and 0–17 years (1.5%), comprised a smaller proportion of the dataset. The distribution of race showed 42.5% were white race, 13.4% other race, 3.8% black Race, and 1.1% Asian race. A considerable proportion (39.2%) had unreported or missing race data. The primary cause of ESKD was most commonly unspecified/unknown (40.5%), followed by diabetes (15.8%), glomerular diseases (13.9%), and hypertensive diseases (14.0%). Less common causes include renal tubulo-interstitial diseases (5.6%), polycystic kidney disease (PKD) (3.7%), and cystic kidney disease other than PKD (0.37%). The geographic distribution of patients in the dataset has the highest representation from Latin America (33.4%), followed by Eastern Europe (23.3%), Southern Europe (14.3%), and Western Asia (8.5%). Other regions had lower yet meaningful representations, including Northern America (6.4%), Northern Europe (4.8%), Eastern Asia (0.5%), and Oceania (0.35%). Conclusion The MONDO initiative developed a new global research dataset to study dialysis outcomes, pathophysiology inference, and risk prediction. This dataset provides a comprehensive and internationally generalizable overview of ESKD patients, dialysis care, and outcomes across multiple countries and at multiple providers. The profiles of demographics and ESKD etiology act as benchmarks for the nephrology community and emphasize the importance of diabetes, hypertensive diseases, and glomerular disease management in kidney disease prevention. MONDO is open to collaboration and contributions without geographic restrictions. This dataset includes 20 years of longitudinal patient data and is among the most comprehensive global dialysis datasets available.
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 | 0,003 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,006 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,007 |
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 ».