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Enregistrement W3096820242 · doi:10.1182/blood-2020-141662

Light Chain Deposition Disease: First Analysis of an International Study in 359 Patients

2020· article· en· W3096820242 sur OpenAlexaff
Paolo Milani, Nelson Leung, Efstathios Kastritis, Stefan Schönland, Ute Hegenbart, Frank Bridoux, Florent Joly, Sascha A. Tuchman, Víctor H. Jiménez‐Zepeda, Sriram Ravichandran, Holly Lee, Tamara Berno, Giampaolo Merlini, Giovanni Palladini, Ashutosh Wechalekar

Notice bibliographique

RevueBlood · 2020
Typearticle
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueAmyloidosis: Diagnosis, Treatment, Outcomes
Établissements canadiensUniversity of Calgary
Organismes subventionnairesnon disponible
Mots-clésMedicinePopulationInternal medicineRenal functionKidney diseasePediatricsSurgery

Résumé

récupéré en direct d'OpenAlex

Introduction: Light chain deposition disease (LCDD) is a rare complication of monoclonal gammopathies, defined by non-amyloid linear monoclonal light chain (most commonly kappa) deposits in the kidney and other organs. The rarity of LCDD has hampered clinical studies and staging systems and response criteria are lacking. The International Kidney Myeloma Working Group (IKMG) started a clinical data collection from all participating centers in order to define the natural history of LCDD, and to establish prognostic factors and response criteria in a large, international, unselected patient population. Methods: Eight referral centers have yet participated in the data collection at the data lock of July 31, 2020. Patient inclusion is ongoing and expected accrual is 500 patients. The diagnosis of LCDD had to be biopsy-proven. The patients were diagnosed between 1992 to 2020. Response was assessed 6 months after treatment initiation according to the criteria used in light chain (AL) amyloidosis. Renal survival (RS) was defined as time from diagnosis to dialysis or last follow-up. Patients who died without requiring dialysis were censored at the time of death. The analysis of factors predicting RS was performed in patients whose baseline estimated glomerular filtration rate (eGFR) was >15 mL/min. The cutoffs of baseline variables, as well as the cutoffs measured at the time of response, best predicting RS or OS at 12 months were identified by means of Receiver Operator Characteristics (ROC) analyses. All patients gave written informed consent for their clinical data to be used for research purposes. Results: Overall, 359 patients have been included in this first analysis. Sixteen (4%) subjects had concomitant cast nephropathy. The main clinical characteristics are reported in the Table. Median overall survival (OS) was 13 years and RS was 12 years (Figure1 A and 1B) and median survival of living patients is 4.5 years. At univariate analysis the only baseline variables predicting RS were proteinuria [best cutoff 2.5 g/24h, HR 2.25 (95%CI 1.13-4.60), P=0.02], and eGFR [best cutoff >30 mL/min, HR 0.50 (95%CI 0.26-0.96) P=0.037], but at multivariate analysis only proteinuria predicted RS [HR 2.17 (95% CI 1.08, 4.33), P=0.027]. At univariate analysis, a higher bone marrow plasma cell infiltrate (best cutoff ≥20%) at diagnosis was associated with a significantly lower OS [HR 1.96 (95% CI 1.23-3.13) P=0.004], as was having end stage renal disease (ESRD) defined as an eGFR <15 mL/min [HR 1.81 (95%CI 1.11-2.92) P=0.015]. We then tested the ability of the hematologic response criteria for AL amyloidosis to discriminate groups with different survival after treatment in a 6 months landmark analysis. Our choice of adopting the amyloidosis response criteria was corroborated by the results of the ROC analysis showing that the difference between involved and uninvolved free light chains (dFLC) cutoff (40 mg/L) used in AL amyloidosis to define very good partial response (VGPR) had 87% sensitivity and 65% specificity in identifying patients who needed dialysis within 12 months. Partial response (PR, 19% requiring dialysis at 3 years) was not associated with a RS benefit over no-response (29% requiring dialysis at 3 years, P=0.511). However, VGPR conferred a significant RS advantage (10% requiring dialysis at 3 years) over PR (P=0.002). No significant difference in RS was seen between complete response (CR, 0% requiring dialysis at 3 years) and VGPR (P=0.178). Thus, achieving VGPR or CR by amyloidosis response criteria [post-treatment dFLC<40 mg/L (VGPR by AL criteria), with or without negative serum and urine immunofixation and normal FLC-ratio (CR by AL criteria)] was adopted as a provisional criterion for hematologic response in LCDD (Figure 1D). LCDD response was also associated with prolonged OS (Figure 1C). Conclusions: Almost one-third of patients with LCDD are diagnosed when they already have ESRD resulting in shorter OS. The degree of proteinuria and of bone marrow plasma cell infiltration predict RS and OS, respectively. Achievement of post treatment dFLC <40 mg/L or negative serum and urine immunofixation at 6 months is proposed as a provisional criterion for hematologic response, being able to predict both improved RS and OS. Planned expanded recruitment might allow a validation analysis of the results, the analysis of organ response data and the evaluation of different time-points for response assessment. Disclosures Milani: Celgene: Other: Travel support; Janssen: Other: Speaker honoraria; Pfizer: Other: Speaker honoraria. Kastritis:Pfizer: Consultancy, Honoraria; Janssen: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria; Amgen: Consultancy, Honoraria, Research Funding; Genesis Pharma: Consultancy, Honoraria. Schönland:Janssen, Prothena, Takeda: Honoraria, Other: travel support to meetings, Research Funding. Bridoux:Baxter: Consultancy; Janssen: Honoraria; Celgene: Honoraria. Tuchman:Celgene: Honoraria, Research Funding, Speakers Bureau; Oncopeptides: Consultancy; Amgen: Research Funding; Caelum: Honoraria; Sanofi: Honoraria, Research Funding; Janssen: Research Funding; Roche: Research Funding; Karyopharm: Honoraria, Research Funding. Jimenez-Zepeda:Janssen, Celgene, Amgen, Takeda: Honoraria. Palladini:Jannsen Cilag: Honoraria, Other; Celgene: Other: Travel support. Wechalekar:Celgene: Honoraria; Caelum: Other: Advisory; Janssen: Honoraria, Other: Advisory; Takeda: Honoraria, Other: Travel.

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)0,003
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 enseignants0,005
Score d'incertitude au seuil0,015

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0030,004
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,002
Bibliométrie0,0030,004
Études des sciences et des technologies0,0010,001
Communication savante0,0020,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0040,001

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.

Tête enseignante Opus0,008
Tête enseignante GPT0,251
Écart entre enseignants0,244 · 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

Citations1
Publié2020
Routes d'admission1
Résumé présentoui

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