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Record W2979406515 · doi:10.1182/blood.v110.11.954.954

Autologous Stem Cell Transplantation (ASCT) in Multiple Myeloma (MM) Patients with Dialysis-Dependent Renal Failure Is Effective but Carries High Rates of Toxicity.

2007· article· en· W2979406515 on OpenAlexaff
Lisa Chodirker, Joseph Mıkhael, Keith Stewart, Andrew Winter, Donna Reece, Norman Franke, Suzanne Trudel, Vishal Kukreti, Wei Xu, Christine I. Chen

Bibliographic record

VenueBlood · 2007
Typearticle
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
Fundersnot available
KeywordsMedicineMelphalanMultiple myelomaNeutropeniaTransplantationInternal medicineDialysisHemodialysisSurgeryGastroenterologyToxicityAutologous stem-cell transplantationUrology

Abstract

fetched live from OpenAlex

Abstract Background: Studies have shown ASCT to be feasible in MM patients (pts) with renal impairment but there are limited data supporting this approach in pts with severe dialysis-dependent renal failure. Patients and Methods: This is a single institution retrospective review of all MM pts who were receiving regular dialysis support at the time of ASCT. Pts with light chain amyloidosis were excluded. Results: From 1998–2006, 22 hemodialysis patients underwent ASCT at our institution. Median age at transplant was 54.3 years (range, 39.4–64.7); 17(77%) pts were male; 17(76%) Salmon Durie stage II–IIIB . MM subtypes: light chains only 9(41%), IgG 8(36%), IgA 4(13%), IgD 2(9%). All pts received high-dose dexamethasone (DEX)-based induction therapy (VAD or DEX alone). High dose therapy consisted of Melphalan (MEL) 200 mg/m2 in 18 pts and MEL 140 mg/m2 in 3 pts. A median of 5.87 X 106 (range, 2.47–51.0) CD34+ cells/kg were collected using cyclophosphamide 2.5 g/m2 + GCSF 10ug/kg/day. Median days to discharge were 19 (range, 14–59). Hematologic toxicity: Of 20 pts with transfusion data available, 14(70%) required RBC and 18(90%) platelet transfusions. Median time to engraftment for neutrophils was 11 days (range, 9–14) and for platelets 13 days (range, 8–17). All patients developed febrile neutropenia. Non-hematologic toxicity (data available in 21 pts): cardiac 13(62%) (arrhythmias, myocardial infarction, CHF), hypotension 6(29%), neurologic 6(29%)(seizure, altered sensorium), infections 6(29%), diarrhea 6(29%), electrolyte imbalances 4(19%) and bleeding 3(14%). Most common grade 3–4 toxicities included mucositis 17(81%), cardiac 12(57%)(most due to atrial arrhythmias), bleeding 3(14%)(epistaxis, hematemesis, tissue hematomas) and infections 3(14%)(CMV, bacteremia, Candidemia). Transplant related mortality (TRM) was 13.6% (3/22) with causes of death including disseminated candidiasis (2) and CMV infection. Responses: Partial responses (PR) were achieved in 18/22 pts. Progression free survival (PFS) from transplant was 22.3 months (95% CI 15–45.6). Three pts (13%) became dialysis-independent (all within 30 days post-transplant). At a median follow-up of 29.6 months (range 0.8–79.6), 10/22 (45%) of patients are alive. Estimated median overall survival from date of transplant was 60 months (95%CI 20.2–79.6) with a 5-year survival probability of 53.2%. Discussion: ASCT in dialysis-dependent MM pts achieves response rates and survival data comparable to that of non-dialysis populations. However, it carries increased toxicity, prolonged median days to discharge (19 days vs. institutional mean of 14 days) and a higher TRM (13.6% vs. institutional mean of 1.6%). The higher rates of cardiac and neurological toxicities enforce the need for pre-transplant identification of pts with co-morbidities, for consideration of dose reduction and risk factor optimization.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.009
GPT teacher head0.241
Teacher spread0.232 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2007
Admission routes1
Has abstractyes

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