MétaCan
Menu
← Retour à la cohorte
Enregistrement W2985030816 · doi:10.1182/blood-2019-121743

The Impact of Identifying the Syndromic and Genetic Diagnoses on Hematopoietic Stem Cell Transplantation Outcome in Patients with Inherited Bone Marrow Failure Syndromes

2019· article· en· W2985030816 sur OpenAlexaffabout
Yeon Jung Lim, Omri Avraham Arbiv, Melanie Kalbfleisch, Robert J. Klaassen, Conrad V. Fernandez, Meera Rayar, MacGregor Steele, Jeffrey H. Lipton, Geoff D.E. Cuvelier, Yves Pastore, Mariana Silva, Josée Brossard, Bruno Michon, Sharon Abish, Roona Sinha, Mark Belletrutti, Vicky R. Breakey, Lawrence Jardine, Lisa Goodyear, Lillian Sung, Tal Schechter‐Finkelstein, Bozana Zlateska, Michaela Cada, Yigal Dror

Notice bibliographique

RevueBlood · 2019
Typearticle
Langueen
DomaineMedicine
ThématiqueAcute Myeloid Leukemia Research
Établissements canadiensInstitute for Clinical Evaluative SciencesJaneway Children's Health and Rehabilitation CentreLondon Health Sciences CentreMcMaster Children's HospitalUniversity of AlbertaCentre hospitalier de l'Université LavalCentre Hospitalier Universitaire de SherbrookeQueen's UniversityStollery Children's HospitalIzaak Walton Killam Health CentreAlberta Children's HospitalChildren's Hospital of Eastern OntarioUniversity Health NetworkBC Children's HospitalRoyal University HospitalMontreal Children's HospitalUniversity of TorontoSickKids FoundationUniversity of ManitobaKingston General HospitalPrincess Margaret Cancer CentreUniversity of SaskatchewanHospital for Sick Children
Organismes subventionnairesnon disponible
Mots-clésMedicineFanconi anemiaBone marrow failureBone marrowHematopoietic stem cell transplantationTransplantationPediatricsInternal medicineStem cellHaematopoiesisGenetics

Résumé

récupéré en direct d'OpenAlex

Background: Over the last decade major progress has been made in developing new diagnostic methods and in phenotypic and molecular classification of inherited bone marrow failure syndromes (IBMFSs). Nevertheless, data from the Canadian Inherited Marrow Failure Registry (CIMFR) indicates that 28% of patients with inherited bone marrow failure syndromes (IBMFS) cannot be assigned a specific syndromic diagnosis. These unclassified IBMFS (UIBMFS) cases may represent either novel syndromes or atypical presentations of previously described disorders. Hematopoietic stem cell transplantation (HSCT) is the only curative option for bone marrow failure and malignant myeloid transformation in IBMFSs. However, it is unknown whether the application of this treatment to UIBMFS patients without an ability to modify the procedure according to the underlying genetic and syndromic diagnosis affects outcome. To our knowledge, there are no published transplant data on cohorts of patients with UIBMFSs. The aims of this study were to evaluate the outcome and prognostic factors of HSCT in a cohort of patients with UIBMFSs and to determine whether the knowledge of the syndromic/genetic diagnosis before HSCT has an impact on transplant outcome. Methods: Patients were enrolled on the CIMFR if they were diagnosed with a specific IBMFSs (e.g. Fanconi anemia), and/or they had bone marrow failure and either a family history of bone marrow, or physical malformations or a diagnosis before the age of one year. Patients were considered as having an UIBMFS if they fulfilled the above criteria, but could not be assigned a specific syndromic diagnosis since they did not meet the diagnostic criteria for any known IBMFS. HSCT data were extracted from the CIMFR database and analyzed. Descriptive statistics were used to compare between groups. Cox proportional hazards model was used for univariate analysis to identify risk factors for worse overall survival post HSCT in patients with UIBMFSs. Results: Among the patients enrolled in the CIMFR, 22 with UIBMFSs and 68 with classified IBMFSs (CIBMFSs) underwent HSCT between January 2001 and December 31, 2017. Transplanted patients with UIBMFSs were hematologically characterized by multilineage cytopenia (n=13), single-lineage cytopenia (n=1), myelodysplastic syndrome (MDS) (n=5) or acute myeloid leukemia (AML) (n=3). Patients with CIBMFSs had Fanconi anemia (n=30), dyskeratosis congenita (n=7), Shwachman-Diamond syndrome (n=9), Kostmann syndrome (n=6), Diamond-Blackfan anemia (n=4) or others (n= 11). Median age at diagnosis of patients with UIBMFSs was 4.18 years (range; 0 to 32.0 years) and median age at HSCT for UIBMFSs was 5.74 years (range; 0.17-66.67 years). Median time between diagnosis of UIBMFS and HSCT was 0.48 years (range; 0.12 - 34.67), this was significantly shorter than that of CIBMFS (1.77 years, range; 0.17 - 15 years, P=0.014). Six patients (27.3%) of UIBMFS and 9 patients (19.7%) with CIBMFS underwent HSCT for MDS-RCEB or AML (P=0.15). The overall 5-year survival of UIBMFS patients was significantly inferior to that of CIBMFS patients: 56±11.4% vs. 76±5.5%, respectively (P=0.047). 5-year overall survival of patients with UIBMFSs was significantly worse among those whose stem cell source was cord blood (15±13.3%) vs. those who received other stem cell sources (91±8.7%, P=0.04), while stem cell source did not affect prognosis of patients with CIBMFSs. Engraftment failure among UIBMFS patients who received cord blood was significantly higher than engraftment failure among those who received bone marrow (55.6% vs. 9.1%, P=0.024). No other factors reached statistical significance when the impact of stem cell source on overall survival was analyzed, including transfusion load, transplant indications, intensity of conditioning regimens, related/non-related donor, degree of human leukocyte antigen (HLA) matching or identifying a diagnosis after HSCT. Conclusion: Identifying the syndromic diagnosis of IBMFSs is critically important when considering HSCT. The worse HSCT outcome of UIBMFSs in this study might be related to an inability to tailor the transplant approach to the patient specific phenotype and genotype. Our data suggest that cord blood should be avoided as a stem cell source in patients with UIBMFSs. Disclosures No relevant conflicts of interest to declare.

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)0,003
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 enseignants0,003
Score d'incertitude au seuil0,006

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,003
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0010,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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 Opus0,013
Tête enseignante GPT0,264
Écart entre enseignants0,250 · 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

Citations0
Publié2019
Routes d'admission2
Résumé présentoui

Explorer davantage

Même revueBlood→Même sujetAcute Myeloid Leukemia Research→Travaux en français237 207→