Patient Age and Donor HLA Matching Can Stratify Allogeneic Hematopoietic Cell Transplantation (HCT) Patients into Prognostic Groups: A Collaborative Study
Notice bibliographique
Résumé
BACKGROUND: Allogeneic hematopoeitic cell transplant (HCT) is potentially curative for a variety of hematological diseases. It is however associated with significant morbidity and mortality. Numerous pre-transplant risk scores have been developed to predict outcomes, such as the Hematopoietic Cell Transplantation-Comorbidity Index (HCT-CI). This study assesses the value of the HCT-CI and related scores on a single center population, in comparison with other potential parameters influencing outcomes. A similar methodology was later applied to a different cohort of patients' data from Oslo University Hospital, Norway. METHODS: Two experienced physicians prospectively calculated the HCT-CI score for all patients transplanted at our center. The age-adjusted HCT-CI score and the augmented HCT-CI score were calculated retrospectively. Prospective calculation was performed during the patient's pre-transplant assessment before transplant admission using a pre-prepared form. The HCT-CI/age and the augmented HCT-CI (which includes ferritin, albumin and platelet count) were calculated using additional data retrospectively collected from the electronic patient records (EPR). All other patient and transplant characteristics were retrospectively collected from the EPR. Non-Relapse Mortality (NRM) and Overall survival (OS) were calculated to assess the prognostic power of the scores. We also looked at the impact of major transplant and patient related parameters in our patient population. A similar methodology was later applied to a different cohort of patients' data from Oslo University Hospital, Norway. RESULTS: From August 2014 to April 2017, 299 patients underwent allogeneic HCT at the Princess Margaret Cancer Centre (PMCC), Toronto. A similar analysis was performed in a cohort of 455 patients from Oslo University Hospital who underwent HCT between 2012 and 2018. Comparative patient characteristics are described in Table 1. On univariate analysis, 2-year OS of the PMCC cohort was 51% (95% CI 45-56%). For the HCT-CI scores 0-2 vs ≥3, 2-year OS was 53% vs 46% respectively (p=0.29). For the HCT-CI/age scores 0-2 vs ≥3, it was 56% vs 44% respectively (p=0.03). For the augmented HCT-CI scores 0-2 vs ≥3, it was 55% vs 46% respectively (p=0.05). Among other variables, age group (<50 vs 50-64 vs ≥65, p=0.02) and donor mismatch (p=0.01) were significant for OS. However, age (HR 1.48 and 1.75 for age 50-64 and ≥65 respectively, p=0.047) and donor mismatch (HR 1.60, p=0.02) alone were also prognostically significant in the multivariate analysis as well. We then developed a weighted score that would better reflect risk groups in our population. Age <50 and full HLA matching received 0 point each, age 50-64 and any mismatch (except DQ alone) received 1 point each, while age ≥65 received 2 points. The patients were grouped into 3 groups of 0, 1 and ≥ 2 points. This new simple 3 tier score predicted OS and NRM at 2 years with better accuracy. The 2 yr OS was 62%, 53% and 38% for the 3 groups (p value =0.0004) and 2 year NRM was 24%, 34% and 43% respectively (p value=0.015) (Table 2 and Figure 1). The same scoring system was later applied to an independent cohort of allogeneic HCT patients from the Oslo HCT registry and was found to be similarly significantly predictive of OS and NRM. In the Oslo cohort, the 2 yr OS was 69%, 65% and 35% for the 3 groups according to the new weighted score (p value <0.001) and 2 year NRM was 15%, 20% and 45% respectively (p value=0.015) CONCLUSION: A simple, weighted score involving donor HLA mismatch and age predicts survival and NRM better than the HCT-CI score for patients transplanted at our center with good replicability as shown from Oslo data. Efforts should continue to strive for the development of a widely applicable pre-transplant outcome predictive scoring system. Disclosures Mattsson: Gilead: Honoraria; Therakos: Honoraria; Celgene: Honoraria. Michelis:CSL Behring: Other: Financial Support.
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 | 0,005 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| 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 ».