201.4: The combination of urine CXCL10 and donor-derived cell free DNA in the non-invasive diagnosis of antibody mediated and T cell mediated rejection in kidney transplantation.
Notice bibliographique
Résumé
Introduction: In kidney transplantation today, an allograft biopsy is required to diagnose rejection. Biopsies are invasive and difficult to use as a tool to monitor alloimmune activity over time. While serum creatinine is used clinically, it is neither sensitive nor specific for rejection. While there is increasing evidence that donor derived cell free DNA (dd-cfDNA) performs well as a biomarker of clinical antibody-mediated rejection (AMR), its ability to identify T cell mediated rejection (TCMR, including borderline rejection) remains unclear. In contrast, urine chemokines, such as CXCL10, are well-characterised biomarkers of tubulitis. Due to these complementary properties, we hypothesized that use of these 2 biomarkers together would improve the diagnosis of rejection phenotypes marked predominantly by tubulitis. Method: A retrospective study was conducted whereby 126 kidney transplant biopsies were selected from the Centre Hospitalier de l’Université de Montréal transplant biobank. 120 of 126 biopsies had paired plasma and urine samples collected on the same day while the remaining biopsies had urine and plasma collected within 30 days. Banff 2019 criteria were followed to generate the following diagnostic categories: 20 cases of AMR (including suspicious AMR where 2 of 3 diagnostic criteria for AMR were met), 10 cases of low grade TCMR (Banff 1A or 1B), 7 cases of high grade TCMR (Banff 2B or greater) and 43 cases with normal histology (i,t,v,g and ptc scores=0). Banff borderline diagnoses were excluded. Urine CXCL10 was measured at the Chemokine laboratory, University of Manitoba using the Meso Scale V-Plex assay. Cell free DNA was extracted from EDTA plasma samples and percent of dd-cfDNA measured using the CareDx AlloSeq cfDNA assay (Brisbane, California). Cut-offs of 0.5% dd-cfDNA and 13 pg/ml urine CXCL10 (except for females less than 6 months post-transplant where we used a cut-off of 33 pg/ml) were selected for each assay, respectively. Results: The AUC for AMR (including suspicious AMR, compared to normal histology) was 0.952 (0.893-1000) using dd-cfDNA alone. In contrast, the AUC for urine CXCL10 alone for AMR was 0.595 (0.469-0.722) and increased to 0.969 (0.923-1.000) when combined with dd-cfDNA (p=1.71X10-8) (see Figure 1). When examining high grade TCMR, AUC for dd-cfDNA alone was 0.762 (0.562-0.963). In contrast, AUC for urine CXCL10 alone was 0.681 (0.474-0.888) and increased to 0.792 (0.585-1.000) when dd-cfDNA was added (p=0.16). For low grade TCMR, AUC was 0.577 (0.442-0.711) for dd-cfDNA alone. AUC for urine CXCL10 alone was 0.595 (0.424-0.767) and increased to 0.652 (0.473-0.832) (p=0.32) when combined with dd-cfDNA. Conclusion: Urine CXCL10 is a weaker diagnostic biomarker of AMR compared to dd-cfDNA. In contrast, when evaluating TCMR, there was no clear advantage of one biomarker over the other, though their combination may improve diagnosis. These findings require external validation and prospective studies.Fondation de CHUM.
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 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,003 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».