#2305 Association between proteinuria and clinically meaningful endpoints in patients with C3G/IC-MPGN: a Delphi consensus of European experts
Notice bibliographique
Résumé
Abstract Background and Aims C3 glomerulopathy (C3G) and primary (idiopathic) immune complex-mediated membranoproliferative glomerulonephritis (IC-MPGN) are rare, progressive kidney diseases caused by dysregulation of the complement system, leading to excessive breakdown and glomerular deposition of the C3 protein. Accumulation of glomerular C3 is followed by inflammation and kidney damage. The most common symptoms of these diseases include hematuria, proteinuria and hypertension. Both diseases are associated with a poor prognosis. Since kidney failure occurs late in the natural history of the disease, and repeat biopsy is not commonly practiced due to its invasive nature, it is necessary to measure other surrogate endpoints to assess outcomes in clinical trials and clinical practice. While surrogate markers are limited, eGFR is the most commonly used; however, it can be affected by non-disease factors such as comorbidities and medications, making it a limited, less reliable endpoint. Although proteinuria is used as a primary endpoint in C3G and IC-MPGN clinical studies due to a growing body of real-world evidence demonstrating its association with long-term kidney outcomes, uncertainty remains on what constitutes a clinically meaningful change. This Delphi consensus aims to contribute to the evidence base and gather expert opinions on defining clinically meaningful changes. It focuses on validated statements regarding the use of proteinuria, alongside other markers, as appropriate endpoints for assessing treatment efficacy in patients with C3G/primary IC-MPGN. Method This European study used a modified Delphi technique involving two rounds of survey. A literature review was conducted to identify existing evidence and knowledge gaps, and facilitate setting an agenda for discussion. We recruited a steering group of 7 nephrologist experts in C3G and IC-MPGN, who attended a virtual meeting to discuss three main themes to inform the basis of consensus statement generation: The meeting was used to generate 26 statements related to the above themes, which were used to develop an online survey for testing with a wider panel of nephrologists and kidney pathologists, recruited anonymously via a third-party agency, M3 Global. Survey respondents (n = 51) shared their level of agreement using a 4-point Likert scale on each statement. Statements that did not reach the predefined consensus threshold of 75%, or required further clarification (e.g., testing different cut-off points), were selected by the group for an additional survey round. Results Overall, 51 survey responses were received from 26 nephrologists and 25 kidney pathologists in the first round of testing. Respondents were from different European countries including France (n = 9), Germany (n = 11), Spain (n = 11), Italy (n = 10) and the United Kingdom (n = 10). Respondent nephrology experience varied [≤5 years (n = 12), 6-10 years (n = 12), 11-20 years (n = 17), >20 years (n = 10)]. At the end of the first round, 24/26 (92%) statements achieved consensus. The steering group agreed to test a further 12 statements in Round 2, with 50 additional survey responses received from 25 nephrologists and 25 kidney pathologists. 11/12 (92%) of these statements achieved consensus. Over the two rounds of survey, 35 statements achieved consensus in total: 3 on the importance of endpoints, 11 on commonly used endpoints, and 21 on proteinuria as a clinically meaningful endpoint. Conclusion A modified Delphi consensus methodology was used to develop expert-informed statements regarding the use of proteinuria as a clinically meaningful endpoint in C3G and IC-MPGN. These statements aim to guide clinical trial design and treatment practices by providing a framework to determine disease progression and treatment response, and validating the use of proteinuria as a surrogate marker in C3G and IC-MPGN clinical trials. The final results and statements will be presented at the meeting.
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 | 0,155 | 0,163 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,004 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,002 | 0,012 |
| Intégrité de la recherche | 0,005 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
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 ».