Age ≥60 Is an Added Risk Factor for Mortality in High-Risk HCT-CI Patients Undergoing Allogeneic Stem Cell Transplant
Notice bibliographique
Résumé
Introduction: Allogeneic hematopoietic stem cell transplant (HCT) is the only curative option for many hematologic malignancies, yet non-relapse mortality (NRM) remains a challenge. Since its development in 2003, the HCT Comorbidity Index (HCT-CI) has been widely adopted to estimate NRM and overall survival (OS), helping assess HCT eligibility. A 2024 retrospective study of HCT recipients over 65 at our centre showed increasing age did not impact prognosis in this older population, but that an HCT-CI of ≥3 predicted for worse outcomes. This study aims to determine transplant-related outcomes in all adult HCT patients with high-risk HCT-CI, and to evaluate the impact of age and HCT characteristics on OS and relapse-free survival (RFS). Methods: Retrospective study of patients with HCT-CI score ≥3 who underwent HCT from January 2014 to January 2024 at the Royal Victoria Hospital. OS and RFS were estimated using Kaplan-Meier analysis and compared with logrank tests. NRM, relapse rate (RR), and acute graft-versus-host disease (aGVHD) were compared by chi-squared tests. Results: We identified 74 patients aged 20 to 72, of whom 49% were 20-59 years old and 51% were ≥60. Median HCT-CI was 3 (range 3-7), with 77% having a score of 3-4 and 23% of ≥5. Donor types were 44% matched unrelated, 26% matched related, 15% mismatched unrelated, 12% haploidentical, and 3% mismatched related. Conditioning was reduced-intensity (RIC) in 61% and myeloablative (MAC) in 39%. T-cell depletion (TCD) was used in 64% of cases, and included alemtuzumab (58%), antithymocyte globulin (19%), and post-transplant cyclophosphamide (23%). Most common HCT indications were acute myeloid leukemia (45%), myelodysplastic syndrome (16%), myeloproliferative neoplasm (14%), and acute lymphoblastic leukemia (12%). Median OS was 7.5 months and RFS 7 months, with 44% OS at 1 year and 33% at 2 years. NRM was 45.2% at 1 year and 53.6% at 2 years. Leading causes of NRM were sepsis (38%) and pneumonia (26%). There was no significant difference in OS, RFS or NRM when comparing donor type, transplant year, conditioning intensity, or HCT-CI of 3-4 vs ≥5. OS was significantly different between patients aged 20-59 and ≥60 years (47.1% vs 14.3%, p=0.027). This was driven by significantly increased 2-year NRM (p=0.011), with no difference in RR. The compared age groups had no difference in HCT-CI score, performance status, transplant year, or TCD. Grade II-IV aGVHD was 16.2% at 100 days and 26% at 1 year, without difference by age or HCT-CI group. MAC group had significantly higher 100-day aGVHD than RIC (31% vs 6.7%, p=0.009). Patients receiving TCD had higher 100-day NRM compared to the non-TCD group (p=0.011), without significant difference in 2-year OS or RR. Within the TCD group, 2-year OS was 45% in patients aged 20-59 (n=20) and 13% in those ≥60 (n=23). This was not statistically significant (0.249), perhaps due to lack of power. NRM cause in the TCD group was primarily sepsis (54%) and pneumonia (25%), whereas in the non-TCD group it was sepsis (38%) and GVHD (31%). Only GVHD was statistically significant as a cause of NRM between groups (p=0.023). Conclusion: At our institution, high-risk HCT-CI patients older than 60 had significantly worse 2-year OS than those 20-59, driven by significantly higher NRM. This may be due to older patients with comorbidities having less reserve to overcome the physiologic challenge of HCT and suggests that patient age and HCT-CI both need to be considered in assessing HCT eligibility. Patients ≥60 fared poorly with any high-risk HCT-CI score ≥ 3. In our high-risk cohort, TCD was associated with less GVHD-related mortality but higher 100-day NRM, though this finding is confounded by the fact unrelated donor transplants were more likely to receive TCD. As TCD becomes more common, strategies to mitigate the risk of infection-driven mortality are needed in this population.
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 | 0,000 | 0,001 |
| 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 | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».