Impact of Age on Outcome in Newly Diagnosed Multiple Myeloma Patients Undergoing Upfront Autologous Hematopoietic Cell Transplantation from the Worldwide Network for Blood and Marrow Transplantation Global Study
Bibliographic record
Abstract
Background : Induction therapy with proteasome inhibitors and immunomodulatory agents followed by autologous hematopoietic cell transplantation (HCT) is considered standard of care in front line multiple myeloma (MM) treatment. Utilization of autologous HCT is increasing worldwide in younger and older patients in light of improvements in supportive care and infrastructure. However, global perspectives on patterns of patient age and the impact of age on outcomes in this population are scarce. In the current analysis of global registry data, we focused on the age distribution and the association of age with outcomes after HCT worldwide. Methods: Data were provided by the Worldwide Network for Blood and Marrow Transplantation through the European Society for Blood and Marrow Transplantation (EBMT), the Center for International Blood and Marrow Transplantation (CIBMTR), the Australia and New Zealand Transplant and Cellular Therapy Registry (ANZTCTR), the Asian Pacific Blood and Marrow Transplant Group (APBMT), the Eastern Mediterranean Blood and Marrow Transplant Group (EMBMT), the Latin American Bone Marrow Transplant group (LABMT), and the Ottawa hospital myeloma registry. The study included newly diagnosed MM patients transplanted between 2013 and 2017. The primary endpoint was overall survival (OS) and secondary endpoints were progression-free survival (PFS), incidence of relapse, and non-relapse mortality (NRM). The probability of OS and PFS was estimated based on the Kaplan-Meier method and differences were analyzed using the log-rank test. The incidences of relapse and NRM were modeled using the crude cumulative incidence estimator and compared between groups with Gray's test. Multivariate analyses were performed using Cox (cause-specific) proportional hazards models including a random effect for country. Age at HCT was modeled as a categorical variable (18-39, 40-64, 65-69, 70-74, and ≥75 years). Models further included patient sex, year of HCT, stage of disease at HCT, Karnofsky score, myeloma subclassification, conditioning dosage, interval between diagnosis and HCT, HCT comorbidity index, ISS at diagnosis, and cytogenetic risk score. Results: In total, 61,725 patients were included in this study; 37,459 (60.1%), 16,217 (26.3%), 3,164 (5.1%), 3,122 (5.1%), 543 (0.9%), 524 (0.8%), 339 (0.5%), 188 (0.3%), and 169 (0.3%) from EBMT, CIBMTR, ANZTCTR, Japan (APBMT), EMBMT, Taiwan (APBMT), LABMT, Ottawa, and Malaysia (APBMT), respectively. The median age at HCT was 60.8 (interquartile range: 54.6-65.8) years. The percentage of patients in the age groups 18-39, 40-64, 65-69, 70-74, and ≥75 years, varied considerably; 2%, 68.9%, 21.8%, 6.5%, and 0.8%, respectively (Table 1). The proportion of <40 years was higher in Malaysia, LABMT, and EMBMT (4-6%) compared to EBMT, CIBMTR, Japan, and ANZTCTR (2%). In contrast, the proportion of patients ≥65 years was higher in EBMT, CIBMTR, ANZTCTR, and Japan (>20%) compared to Malaysia, LABMT, and EMBMT (7-13%). The following patterns were observed in the age groups 18-39, 40-64, 65-69, 70-74, and ≥75 years, respectively. Melphalan 200 mg/m 2 for conditioning was used more frequently in younger patients (78.4%, 75.4%, 63.2%, 40.6%, and 28.3%, respectively). In 60.7% of the group ≥75 years, a lower dose of melphalan 140 mg/m 2 was chosen. OS was lower with older age (p<0.001) and was 86%, 83%, 81%, 78%, and 75% at 3 years, respectively (Figure and Table 1). PFS was similarly associated with older age (56%, 51%, 50%, 47%, and 45% at 3 years, respectively (p<0.001)). The cumulative incidence of relapse was not significantly different (15%, 16%, 15%, 16%, and 16% at 1 year, respectively (p=0.74)), but the cumulative incidence of NRM was higher with older age (p<0.001) and was 0%, 1%, 2%, 2%, and 4% at 1 year, respectively. On multivariate analysis, older age was associated with lower OS (overall p<0.0001), lower PFS (overall p=0.003), and higher NRM (overall p<0.0001), but not with the risk of relapse (overall p=0.79). Conclusions: There is considerable global variability in the age distribution of patients receiving HCT. Globally, 2% of patients receiving front line HCT for myeloma are aged <45 and 0.8% are >75 years. Advancing age was a significant risk factor for OS and PFS due to differences in NRM, but even in patients >75 years NRM was very low.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".