MétaCan
Menu
← Retour à la cohorte
Enregistrement W2738119439 · doi:10.1182/blood.v128.22.4662.4662

Improving Revised International Prognostic Scoring System (IPSS-R) Pre-Allogeneic Stem Cell Transplant Does Not Translate into Better Post-Transplant Outcomes for Patients with Myelodysplastic Syndromes

2016· article· en· W2738119439 sur OpenAlexaffabout
Musa Alzahrani, Maryse Power, Emilie Nevill, Yasser Abou Mourad, Michael J. Barnett, Raewyn Broady, Donna L. Forrest, Alina S. Gerrie, Donna E. Hogge, Stephen H. Nantel, David Sanford, Heather J. Sutherland, Cynthia L. Toze, Kevin Song, Thomas J. Nevill, Sujaatha Narayanan

Notice bibliographique

RevueBlood · 2016
Typearticle
Langueen
DomaineMedicine
ThématiqueAcute Myeloid Leukemia Research
Établissements canadiensDr. Georges-L.-Dumont University Hospital CentreVancouver General HospitalBC Cancer AgencyMcMaster UniversityLeukemia & Lymphoma Society of CanadaUniversity of British Columbia
Organismes subventionnairesnon disponible
Mots-clésMedicineInternational Prognostic Scoring SystemCytopeniaInternal medicineMyelodysplastic syndromesUnivariate analysisMultivariate analysisOncologyTransplantationCumulative incidenceBone marrow

Résumé

récupéré en direct d'OpenAlex

Abstract Background: The myelodysplastic syndromes (MDS) encompass a heterogeneous group of hematopoietic stem cell disorders characterized by dysplastic and ineffective blood cell production leading to cytopenia and a variable risk of transformation to acute myeloid leukemia. The natural history of patients (pts) with MDS is variable and several prognostic scoring systems have been developed to guide treatment decisions. The revised IPSS (IPSS-R) score is commonly used in practice to predict outcome in newly diagnosed pts as well as in predicting their transplant outcomes. It remains unclear however whether improving IPSS-R pre allogeneic transplant (allo-SCT), using different therapeutic strategies, is associated with better clinical outcomes post-transplant. Methods: The Leukemia/BMT Program of British Columbia database was queried to identify all pts with MDS who had undergone an allo-SCT between Feb 1997 and April 2013. Pertinent information on clinical features and outcomes were then retrospectively reviewed. IPSS-R was calculated at MDS diagnosis (dx) and then re calculated prior to transplant. Outcomes of pts who had improvement in IPSS-R were then compared to those with no improvement or worsened IPSS-R score. Overall survival (OS) and Event free survival (EFS) were estimated using the Kaplan-Meier method and a competing risk analysis was used to calculate relapse and non-relapse mortality (NRM). Univariate and multivariate analyses were conducted. Log-Rank test was used to determine the p value. Results: We identified 138 pts who have undergone allo-SCT with the following characteristics: median age at transplant was 49 years (yrs) (range 17-66); 76 (55%) were male; 121 pts (88%) underwent myeloablative (MA) conditioning, 68 (49%) related donor and 70 (51%) unrelated donors out of which 43 (61%) were matched and 27 (39%) were mismatched. The source of stem cells were: peripheral blood n=101 (73%), bone marrow n=35 (25%) and cord blood n=2 (1%). The median interval from dx to transplant was 128 days. The median follow up (FU) of live pts was 7.3 yrs (range 1.5-17.4). Acute graft vs. host disease (aGVHD) grade 2 or higher was present in 74 (54%), chronic graft vs. host disease (cGVHD) was present in 94 (68%). During the time of FU 83 (60%) of pts had died. Relapse occurred in 41 (30%). Causes of death were: relapse n=39; GVHD n=20; regimen related n=11; infection n=5 and other causes, n=9. Baseline characteristics of all pts are shown in table 1. In 12 (9%) pts, the IPSS-R could not be calculated either because cytogenetics failed or bone marrow biopsy pre transplant was not done. At the time of transplant 85 (62%) pts had blasts <5%, 38 (28%) had 5-20% and 10 (7%) had blasts >20% and blasts count was unknown in 5 pts. IPSS-R improved in 62 (45%), worsened in 23 (17%), no change 41 (30%) and unknown in 12 (9%). Type of treatment was chemotherapy in 55 (40%), best supportive care in 80 (58%) and immunosuppressive therapy (IST) in 3 (2%). The OS and EFS for all pts were 34% and 33%, respectively. There was no difference in outcome between pts who have undergone MA vs non-MA, OS 34% and 39% (p=0.63) and EFS 34% and 32% (p=0.86), respectively. OS was not statistically different between pts with improved IPSS-R vs worsened vs unchanged, 30% vs 22% vs 40%, p= 0.63 (see figure 1). EFS was 30% vs 21% vs 40%, p=0.53, respectively. Relapse was 36% vs 53% vs 31%, p=0.35 and non-relapse mortality was 54% vs 57% vs 42%, p=0.75, respectively. There was no difference in OS and EFS between pts treated with chemotherapy vs supportive care with OS of 40% vs 41%, p=0.63 and EFS of 33% vs 32%, p=0.46, respectively. OS and EFS for grade 2 or higher aGVHD were 34% vs 32%, p=0.13 and 32% vs 32%, p=0.22, respectively. Figure 2 shows OS for Pts with blast <5% vs 5-20% vs >20% at transplant. Relapse of pts with blasts <5 vs 5-20 vs >20 at the time of transplant was 23% vs 69% vs 66%, p=0.0004, respectively. On multivariate analysis, only three factors were associated with worse OS and EFS which were: cytogenetics at dx, blast count at transplant and absence of cGVHD. Conclusion: Improving IPSS-R before allogeneic transplant does not translate into better clinical outcome. The IPSS-R cytogenetic risk group at the time of diagnosis and blast count at transplant are highly predictive of post-transplant outcomes. Patient characteristics Patient characteristics Disclosures Gerrie: Roche Canada: Research Funding. Toze:Roche Canada: Research Funding. Song:Janssen: Honoraria; Otsuka: Honoraria; Celgene: Honoraria, Research Funding. Song:Janssen: Honoraria; Otsuka: Honoraria; Celgene: Honoraria, Research Funding. Nevill:Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees; Alexion: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding.

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)0,002
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 seuil0,008

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

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 Opus0,009
Tête enseignante GPT0,234
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

Citations0
Publié2016
Routes d'admission2
Résumé présentoui

Explorer davantage

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