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Record W4405042667 · doi:10.1182/blood-2024-200877

Outcomes of Frailty Subgroups Treated with Teclistamab in the Real-World: An International Myeloma Foundation Study Database Analysis

2024· article· en· W4405042667 on OpenAlexaff
Hira Mian, Carlyn Tan, Sireesha Asoori, Rakesh Popat, Nadine Abdallah, Saurabh Chhabra, Ricardo Parrondo, Gregory R. Pond, Thomas G. Martin, B G M Durie, Yi Lin

Bibliographic record

VenueBlood · 2024
Typearticle
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineMultiple myelomaDiscontinuationInternal medicineClinical trialComorbidityDatabaseOncology

Abstract

fetched live from OpenAlex

Introduction: Frailty is known to be an important risk factor for adverse outcomes in multiple myeloma (MM) and is often under-represented in clinical trials. Frail patients are known to have increased toxicity, high rates of treatment discontinuation rates and decreased efficacy including worse overall survival in MM. Teclistamab (TEC) is a bispecific BCMA-CD3 directed T-cell antibody (BsAb) that was approved based upon the pivotal MajesTEC-1 study showing high overall response rates in patients with R/R MM. In MajesTEC-1, the median age was 63, and patients with ECOG ≥ 2 were excluded, suggesting that the vast majority of patients in the trial were fit. There is a paucity of data both in clinical trials as well as in the real-world regarding the outcomes of patients, particularly frail patients, with BsAb. This data is needed to both understand current outcomes of frail patients as well as devise strategies for future optimization with BsAb treatment. We performed a retrospective analysis utilizing the International Myeloma Foundation (IMF) immunotherapy database with the objective of understanding the outcomes of older adults (age ≥ 70) including frail patients treated in the real-world with teclistamab. Methods: Data was collected from 7 individual academic sites for patients aged ≥ 70 years treated with teclistamab in the real-world. Data collected include patient demographics, disease characteristics, as well as efficacy and toxicity associated with teclistamab. Frailty score was retrospectively calculated using the simplified/IFM frailty index which includes chronological age, ECOG PS and Charlson Comorbidity Index categorizing the patients into fit (score 0-1) or frail (≥ score 2). Fisher's exact tests were performed to evaluate differences in outcomes between fit and frail patients. Results: A total of 81 pts were included in our total cohort. The median age was 76 year (range 70-91). Among them, 44 (54%) were > age 75 and 23 (28%) > age 80. Twenty-eight (35%) patients had an ECOG performance status of ≥2. Using the simplified/IFM frailty index, 22 (27%) were classified as fit and 59 (73%) were frail. In the frail subgroup, a high proportion of patients (42%) would be classified as ultra frail (frailty score of ≥3). Selected baseline patient and disease characteristics are as follows: high risk cytogenetics including 1q gain/amp (fit 67% vs frail 43%), median prior lines (fit 7 vs frail 5), triple class refractory (fit 81% vs frail 88%), CrCl <30 (fit 5% vs frail 15%). For patients hospitalized for step-up dosing, median number of hospital days was 8 both in the fit and frail group. All grade CRS was noted in 12 (55%) fit and 28 (47%) frail patients. Among the fit patients, only 1 of the 12 patients (8%) had grade 2 CRS, whereas 8 of 28 (29%) frail patients had grade 2 (p-value=0.23). ICANS was reported in 2 (9%) fit patients and 8 (14%) frail patients (p=0.72). The majority of ICANS was grade 1 in both groups, but among the frail group, there was one grade 3 and two grade 4 ICANS cases reported. Infections were reported in 10 (45%) fit patients and 35 (59%) frail patients (p=0.32), with 2 (9%) and 14 (23%) being grade≥3 (p-value=0.21). IVIG was administered in most patients (fit 50% vs frail 58%, p=0.62). Grade 3/4 neutropenia was reported in 10 (45%) fit patients and 19 (32%) frail patients (p=0.30). The overall dosing interval was changed in 32% of fit patient and 34% of frail patients (p=1.00). The overall response rate was 50% for fit patients versus 66% for frail patient (p=0.61). The type of responses noted in the fit patient were as follows: sCR/CR 18%, VGPR 27%, PR/MR 5%, SD 18%, PD/NE 32% and for frail patients : sCR/CR 18%, VGPR 43%, PR/MR 9%, SD 5%, PD/NE 25%. The median follow-up time was 13.1 months. 12-month PFS for fit pts was 47.6% (95% CI 25.7% - 66.7%) and for frail pts 42.4% (29.0% - 55.3%). 12-months OS for fit pts was 64.9% (95% CI 39.9% - 81.6%) and for frail 61.5% (47.0% -73.1%). Conclusions: This is the first real-world study to evaluate the outcomes of teclistamab in frail older adults. With regards to toxicity, frail older adults showed a trend towards higher rates of ≥ grade 2 CRS, ≥ grade 2 ICANS as well as ≥3 grade infections. Efficacy was maintained in both fit and frail older adults similar to the MajesTEC-1 study. Our study highlights that BCMA BsAb can be safely utilized in older adults including frail older adults; however, additional proactive supportive care may be required to further optimize outcomes.

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)0.003
metaresearch head score (Gemma)0.007
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 score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.006
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.046
GPT teacher head0.378
Teacher spread0.332 · 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".

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Citations3
Published2024
Admission routes1
Has abstractyes

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