Outcome of Patients with IgD and IgM Multiple Myeloma Undergoing Autologous Hematopoietic Stem Cell Transplants: A Retrospective CIBMTR Study.
Bibliographic record
Abstract
Abstract Multiple Myeloma (MM) is the most common indication for autologous hematopoietic stem cell transplantation (auto HCT). However, little information is available regarding the outcome of patients (pts) with the rarer immunoglobulin subtypes IgD and IgM, which represent 2% and 0.5%, respectively, of all MM cases. In addition, IgD MM has been reported previously to have a poorer prognosis, at least after conventional therapy. The CIBMTR conducted a retrospective analysis of MM pts transplanted between 1995–2005 to describe the characteristics and results of auto HCT in IgD (n=36) and IgM (n=11) among 3578 MM patients with auto HCT during this period. Median follow-up of survivors was 41 (range 2–130) months for pts with IgD and 58 (range 5–101) months for those with IgM MM. Among pts with IgD subtype, median age was 52 years (yrs), 67% were male, 36% had a creatinine >2 mg/L, 61% had Durie-Salmon stage III disease at diagnosis and 33% had Kappa Serum Light chain; the corresponding values for IgM pts were 58 yrs, 36%, 73% and 55% respectively. Prior to auto HCT, 75% of IgD pts were chemosensitive and 25% had received >2 lines of chemotherapy, while all IgM pts were chemosensitive and none had received more than 2 lines of prior therapy. Median time from diagnosis to auto HCT was 9 months in both subtypes. The most common conditioning regimen was singleagent melphalan, and all but 1 pt with IgD disease were grafted with blood stem cells. The small sample size precluded multivariate analysis for potential prognostic factors for outcome. Below table summarizes the post-auto HCT results in these pts contrasted with a reference pool of IgG and IgA MM receiving auto HCT in the same time period. Outcomes, probability (95% CI) IgD IgM IgG/IgA (n=36) (n=11) (n=1475) 100-day mortality, % 0 9 ( 0 – 32) 7 (5 – 8) Non MM deaths, % @ 1 yr 0 9 (0 – 32) 5 (4 – 7) @ 3 yrs 3 (0 – 13) 21 (2 – 51) 16 (14 – 18) Relapse/ Progression, % @ 1 yr 21 (9 – 37) 20 (2 – 49) 18 (16 – 20) @ 3 yrs 59 (41 – 76) 32 (8 – 64) 36 (34 – 39) Progression-free survival, % @ 1 yr 79 (63 – 91) 71 (41 – 93) 77 (74 – 79) @ 3 yrs 38 (21 – 56) 47 (17 – 78) 47 (44 – 50) Overall survival, % @ 1 yr 87 (74 – 97) 91 (68 – 100) 79 (77 – 85) @ 3 yrs 69 (51 – 84) 68 (36 – 93) 53 (50 – 57) No striking differences are apparent in the post auto HCT outcomes of patients with IgD and IgM MM. These results are also consistent with published outcomes of pts with IgD/ IgM MM (Wechaleker et al Ann Hematol. 2005 Feb; 84(2):115–7; Maisner et al Bone Marrow Transplant. 2008 Jan; 41(1):51–4).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".