Comparison of the Impact of Pre-Transplant Co-Morbidity Scores on Allogeneic Hematopoietic Stem Cell Transplant Outcomes
Notice bibliographique
Résumé
Abstract Introduction: Allogeneic hematopoietic cell transplantation (allo-HCT) is potentially curative for the treatment of various hematological diseases, in part due to the effect of conditioning chemotherapy, and in part due to graft-versus-malignancy effect. However, alloHCT is associated with significant morbidity and mortality. Multiple co-morbidity indices have been published in the literature for the purpose of pre-transplant risk assessment. The purpose of the presented study is to assess a number of these pre-transplant scores on a single-center transplant population and to determine the score with improved risk stratification ability using concordance statistics. Methods: We investigated the impact of the prospectively collected Hematopoietic Cell Transplantation-Comorbidity Index (HCT-CI) on post-transplant outcomes for 243 recipients of allo-HCT performed between August 2014 and October 2016 at the Princess Margaret Cancer Center (Toronto, Canada), and compared this score to other pre-transplant scores including the age-adjusted HCT-CI, PAM score (Pre-transplant Assessment of Mortality Score) and the Disease Risk Index (DRI). Partitioning of the HCT-CI, HCT-CI/age and PAM scores into three groups was performed based on maximum significant differences on univariate analysis for overall survival (OS). Concordance statistics were used to compare the stratification power of the scores. Statistical analyses were performed using SAS version 9.4 (SAS Institute, Inc, Cary, NC). Results: The median age at transplant is 56 years, patients were transplanted for AML (53%), ALL (7.5%), MDS (13.5%), MPN (14%), NHL/CLL (8.5%) and (3.5%) AA. Donors were matched related in 37%, unrelated in 59% and haploidentical in 3% of the patients. Reduced intensity conditioning chemotherapy was used in 132 patients (54%), 153 patients (63%) received in-vivo T-cell depletion by using Campath or ATG, both donor and recipient were CMV negative in 48 (20%) of the patients. DRI was high in 67 (29%), intermediate in 145 (62%) and low in 22 (9%) of patients. HCT-CI was 0 in 90 (37%), 1 in 49(21%) and ≥2 in 103 (43%) of patients. HCT-CI/age was 0 in 22 (10%), 1 in 72 (30%) and ≥2 in 148 (62%). PAM score was 1-17 in 157(68%), 18-24 in 70 (30%) and 25-27 in 7 (3%) of patients. Median follow up of survivors was 28 months (range 17-44 months). OS of the entire cohort was 51% and 43% at 2 and 5 years post-transplant respectively. Cumulative incidence of relapse (CIR) was 19% at 2 years. For OS, as grouped above, the DRI did not demonstrate a significant difference between groups (p=0.77). For HCT-CI, p=0.034 (Figure 1), for HCT-CI/age p=0.02 and for the PAM score p=0.38. For OS, for the DRI, the C-statistic was 0.51 (se=0.03, 95%CI 0.45-0.57). For the PAM score, C-statistic was 0.51 (se=0.02,95%CI 0.45-0.56). For the HCT-CI age, C-statistic was 0.56 (se=0.024, 95%CI 0.51-0.61). For the HCT-CI, C-statistic was 0.56 (se 0.02, 95% CI 0.50-0.61). For CIR, the PAM score demonstrated a superior C-statistic of 0.56 (se=0.06, 95%CI 0.44-0.67) compared to the other scores. For NRM, the HCT-CI score (Figure 2, p=0.039) is superior with C-statistic 0.56 (se=0.04, 95%CI=0.49-0.63). Conclusion: Based on the above described analysis, the original HCT-CI score as described by Sorror et aldemonstrates superior prognostic stratification ability for OS and NRM in our patient cohort compared to other scores. Further investigation for the development of an optimal risk scoring system for allogeneic HCT is required. Figure 1. Figure 1. Disclosures Kim: Paladin: Consultancy; Pfizer: Consultancy; Novartis: Consultancy, Honoraria, Research Funding; BMS: Consultancy, Honoraria, Research Funding. Lipton:Novartis: Consultancy, Honoraria, Research Funding; Pfizer: Consultancy, Honoraria, Research Funding; BMS: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria, 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 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 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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.
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 ».