Optimizing Patient Selection for Allogeneic Stem Cell Transplantation in Chronic Myeloid Leukemia.
Notice bibliographique
Résumé
Abstract Abstract 3392 Poster Board III-280 Although imatinib has replaced allogeneic stem cell transplantation (SCT) as the first treatment option for CML in first chronic phase (CP1), approximately 35% of patients do not obtain long-term benefit. As only 40-50% of these patients can be salvaged with second-generation tyrosine kinase inhibitors there is considerable debate about use of SCT as second line therapy. It is therefore important to identify groups of patients who would have a good outcome after SCT so that it may be offered after imatinib failure. In a previous analysis we identified the pre-conditioning level of C-reactive protein (CRP) as a prognostic factor for the outcome of SCT in CML. In this study, we investigated the prognostic value of comorbidities together with CRP at the time of myeloablative SCT, for patients with CML in CP1. Clinical data on 312 consecutive patients who underwent SCT between January 1991 and July 2008 was reviewed; 41 patients with incomplete data were omitted from the analysis. The median age of 271 analyzed patients was 34.3 years (range 9.7 – 59.6). 256 (94.5%) patients received bone marrow (BM) and 15 (5.5%) patients received peripheral blood stem cells (PBSC). Conditioning consisted of cyclophosphamide and TBI for 130 (48%) recipients of sibling stem cells. In addition in vivo T cell depletion with anti CD52 antibody (Campath 1H) was used for 141 (52%) unrelated donor transplants. Comorbidities were defined and assigned different weights (1-3) by the hematopoietic cell transplantation comorbidity index (HCT-CI; Sorror, ML et al, Blood 2005). HCT-CI scores were calculated for each patient stratifying them into low risk (LR, no comorbidities, HCT-CI =0), intermediate risk (IR, HCT-CI 1 or 2) and high risk (HR, HCT-CI ≥3) groups and were evaluated for their effects on transplant related mortality (TRM) and overall survival (OS). In multivariate analysis the HCT-CI score failed to predict OS or TRM at 1 year. The only significant difference was in TRM at 100 days between the patients with HCT-CI =0 (LR group) and patients with HCT-CI ≥1 (IR and HR groups; relative risk (RR) 4.0; 95% CI: 1.4 – 11.6). The absolute number of comorbidities (CMn) was a better prognostic indicator for day 100 TRM then the weighted HCT-CI (CMn=1, RR: 3.1, CI 1.3-7.3; CMn ≥1, RR: 4.9, CI: 1.7-14.1). Pre-conditioning CRP was predictive for both TRM and survival and was independent of CMn. We thus combined these two parameters into a new risk assessment tool (CRP/CMn), with 3 prognostic groups: LR (no comorbidities and CRP <2 mg/L), HR (1 comorbidity and CRP >10 mg/L or >1 comorbidity) and IR (remaining patients). These groups yielded probabilities of OS at 10 years of 81% (N=61, LR), 63% (N=159, IR) and 38% (N=25, HR; p=0.0001, Figure 1). When adjusted for patient age, duration of disease pre-SCT, donor type and patient/donor gender mismatch in a multivariate analysis, the relative risk of treatment failure (death) was 5.3 (95% CI: 2.5 – 11.5) for the HR group, and 2.3 (95% CI: 1.2 – 4.4) for the IR group when compared to the LR group. Fig 1 Probability of survival by CRP/CMn group Fig 1. Probability of survival by CRP/CMn group In our single center cohort of patients with CML in CP1 the HCT-CI was a poor indicator of prognosis post SCT. However, by removing the weighted scores and combining absolute number of co-morbidities with pre-conditioning CRP levels, we identified a new risk assessment tool that helps to select patients with CML in CP1 who could benefit from SCT as second line therapy. 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 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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».