Chemosensitive Disease Better Predicts Outcome Than Age Following Autologous Heamatopoietic Stem Cell Transplantation in Elderly Patients with Multiple Myeloma
Bibliographic record
Abstract
Abstract High-dose chemotherapy followed by autologous hematopoietic stem cell transplantation (AHSCT) is considered the standard of care as first-line therapy for eligible multiple myeloma patients(Barlogie et al. 1987; McElwain & Powles 1983). Median age at diagnosis of multiple myeloma is 69 years old (SEER 18 2010-2014). Data from the CIBMTR revealed that the number of transplants done per year and the age of recipients are still rising(D'Souza & Zhu 2016). With an expected myeloma prevalence rising by 57% from 2010 to 2030(Smith et al. 2009), the number of older patients considered for AHSCT will increase significantly. Best treatment options for those older patients must therefore be addressed. The assessment of hematopoietic cell transplant comorbidity index score (HCT-CI) (Sorror et al. 2005; Sorror et al. 2009) can be helpful in the selection of candidates for AHSCT. The goal of this study was to identify factors impacting the safety and efficacy of AHCT in older multiple myeloma patients in order to better select those who will benefit from such an intervention. This single center, retrospective study examines outcomes of AHSCT in elderly patients (≥60 years old) with multiple myeloma compared to younger patients ( From the 93 patients included in the study, 49(53%) were included in the younger patient group with a median age at transplant of 54 years-old (range 34 to 59). Forty-four (44) patients were included in the older patient group with a median age at transplant of 65 years-old (range 60 to70). Two thirds (66%) of patients were male. Based on the R-ISSS score, 46% were stage I, 54% were stage II and none were stage III. HCT-CI score was low, intermediate and high in 40%, 32% and 28% of patients respectively. Median karnofsky index was 90 (range 70 to 100). Acute renal failure was more frequent in the older group (p=0,028) at multiple myeloma diagnostic. Light chain disease was more frequent in the younger patient group with 19% compared to 3% (p=0,09). Mucositis was similar in occurrence in both groups with only 5% of grade ≥3 mucositis. No parenteral alimentation was required in the older patient group while 4 patients required it in the younger patient group(p=0,053). The incidence of febrile neutropenia was universal in all patients (100%) within the cohort. The incidence of septic shock and admission to intensive care unit was only 1%. Delirium was diagnosed in 2 older patients (p=0,131). There was no difference in toxicity profile when comparing both groups. Median engraftment for neutrophils was 14 days and 16 days for platelets. Median hospital stay duration was 21 days. Intravenous antibiotics were used a median time of 8 days. There was no difference in resources used during transplant in both groups. Median CD4 count was comparable in both groups at 3 months and at 1 year after transplant. With a median follow-up of 37 months (range 2 to 70), our cohort had a PFS of 24 months with no difference between younger and older patient groups(p=0,234). The median OS was not reached at the time of evaluation for both groups. Estimated 4 years PFS was 21% and OS was 81%. There was no difference between our younger and older patient cohorts (figure 1 and 2). Being in stable or progressive disease was associated with worse PFS (median of 9 months versus 26 months, p In conclusion, AHSCT is a good treatment option in well-selected patients. Comorbidities must be considered and HCT-CI can help clinicians choose carefully those patients. Our study demonstrates that age should not be the limiting factor when considering ASCT in older patients. Toxicity and survival were comparable in both groups. Best timing when to proceed to ASCT is still in investigation. Considering the significantly worse PFS with chemoresistant disease, other treatment options should be considered before AHSCT without age being a consideration. Download : Download high-res image (71KB) Download : Download full-size image Disclosures Delage: AbbVie: Research Funding; Roche: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Research Funding; Pfizer: Research Funding.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".