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Enregistrement W3094956814 · doi:10.1182/blood-2020-136746

Incidence, Outcomes and Predictors of Acute Kidney Injury Post Allogeneic Stem Cell Transplant

2020· article· en· W3094956814 sur OpenAlexaff
Kayla Madsen, Gabrielle Côté, Karyne Pelletier, Abhijat Kitchlu, Shiyi Chen, Jonas Mattsson, Ivan Pašić

Notice bibliographique

RevueBlood · 2020
Typearticle
Langueen
DomaineMedicine
ThématiqueMyeloproliferative Neoplasms: Diagnosis and Treatment
Établissements canadiensUniversity of TorontoPrincess Margaret Cancer CentreUniversity Health Network
Organismes subventionnairesnon disponible
Mots-clésMedicineInternal medicineHazard ratioIncidence (geometry)Proportional hazards modelHematopoietic stem cell transplantationUnivariate analysisAcute kidney injuryTransplantationGraft-versus-host diseaseMultivariate analysisCohortOncologyConfidence interval

Résumé

récupéré en direct d'OpenAlex

INTRODUCTION: Allogeneic hematopoietic stem cell transplantation (allo-HSCT) offers cure for some patients with hematological diseases but is associated with significant risk of morbidity and mortality. Acute kidney injury (AKI) represents an important cause of post-transplant complications, often multifactorial in the unique setting of allo-HSCT. However, to date, there is limited information on the overall impact of AKI in this patient population. To address this, we retrospectively reviewed the effect of AKI on transplant outcomes at Princess Margaret Cancer Centre (PMCC). METHODS: The study included 408 patients transplanted at PMCC between January 2015-January 2018 for any indication, using either reduced intensity (RIC) or myeloablative conditioning (MAC). Median follow-up time was 23 months. AKI was defined using the Kidney Disease: Improving Global Outcomes (KDIGO) criteria. Demographics, clinical characteristics and transplant related variables were extracted from patient records and the institutional allo-HSCT database. Univariate and multivariate Cox proportional hazard models were used to examine associations between AKI and outcomes including overall survival (OS), relapse free survival (RFS), graft versus host disease (GVHD) and relapse-free survival (GRFS). Univariate and multivariate Fine and Gray competing risk models were used to examine the association between AKI and incidence of relapse, treatment related mortality (TRM), grade 2-4 acute GVHD (aGVHD), grade 3-4 aGVHD and moderate-severe chronic GVHD (cGVHD). Multivariate models were used to examine the association of AKI and outcomes of interest, while adjusting for potential risk factors. RESULTS: The overall incidence of AKI at 100 days was 64% (stage 1: 62.6%, stage 2: 22.5% and stage 3: 14.8%). Dialysis was required in 2% of these patients. Mean baseline eGFR in the AKI group was 94.4 mL/min/1.73m2 (IQR 42, 141) vs 96.9 mL/min/1.73m2 (IQR 45.7, 142.9) in the non-AKI group. Patient-related risk factors for development of AKI were age over 60 (p=0.001), male gender (p=0.05), diabetes (p=0.004) and hypertension (p=0.003). Transplant related characteristics associated with AKI were MAC conditioning (p=0.02), veno-occlusive disease (p<0.0001), BK viremia (p=0.01), thrombotic microangiopathy (p=0.009), bacterial infections (p=0.001) and more than two cytomegalovirus (CMV) reactivations (p=0.02). There was no difference in mean cyclosporine levels in the first 100 days between patients who developed AKI and those who did not (p=0.80). AKI was less common in patients who received GVHD prophylaxis with dual T-cell depletion (TCD) with anti-thymocyte globulin (ATG) and post-transplant cyclophosphamide (PTCy) (p<0.001) than those who received alternative GVHD prophylaxis. In the univariate analyses, compared with patients who did not have AKI, those with AKI had inferior 2-y OS: 46% versus 63% (p=0.0004). AKI patients had lower 2-y GRFS (29% vs 45%, p=0.0002), higher 2-y TRM (31% versus 17%, p=0.0003), and higher incidence of day 100 grade 3-4 aGVHD (13% vs 6%, HR 2.18, 95% CI=1.18-4.01, p=0.01). In multivariate analysis, AKI was associated with decreased 2-y OS (HR= 1.36, 95% CI 1.00-1.65, p=0.048), 2-y GRFS (HR= 1.42, 95% CI 1.10-1.82, p=0.006), and increased risk of day 100 grade 3-4 aGVHD (HR= 1.93, 95% CI 1.04-3.58, p=0.03). There was an association between AKI and TRM, specifically in those patients with stage 2 (HR= 1.76, 95% CI 1.06-3.30, p=0.03) and stage 3 AKI (HR= 2.64, 95% CI 1.44-4.83, p=0.002) compared to no AKI. In multivariate models, there was no association between AKI and relapse (p=0.65), grade 2-4 aGVHD (p=0.7) or moderate-severe cGVHD (p=0.81). CONCLUSION: Patients who develop AKI within 100 days of transplant have lower OS, GRFS, and higher grade 3-4 aGVHD and TRM. The use of dual TCD for GVHD prophylaxis is associated with lower risk of AKI, suggesting this may be a favorable regimen for those at increased risk for AKI. Contrary to previously published literature, there was no difference in cyclosporine levels between the non-AKI and AKI groups, suggesting that it may not be a significant cause of AKI post-transplant. AKI was more common in patients who had multiple episodes of CMV reactivation, highlighting the importance of CMV prophylaxis. AKI patients require close follow up, preventative strategies and monitoring for new or progression of chronic kidney disease post transplant. Disclosures Madsen: Jazz Pharmaceuticals: Honoraria. Pelletier:Celgene: Honoraria; International Kidney and Monoclonal Gammopathy: Membership on an entity's Board of Directors or advisory committees. Mattsson:Jazz Pharmaceuticals: Honoraria; ITB: Honoraria; Takeda: Membership on an entity's Board of Directors or advisory committees; Mallinkrodt: Honoraria; Gilead: Honoraria.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,004
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,002
Score d'incertitude au seuil0,006

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,004
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,000
Science ouverte0,0000,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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.

Tête enseignante Opus0,011
Tête enseignante GPT0,236
Écart entre enseignants0,225 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations5
Publié2020
Routes d'admission1
Résumé présentoui

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