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Treatment Patterns and Associated Outcomes in Patients with Relapsed or Refractory Multiple Myeloma in the US and Non-US Countries: Findings from Preamble

2017· article· en· W2963547298 on OpenAlexaboutno aff
Ravi Vij, Clara Chen, Sergey Popov, Brian G.M. Durie, Gordon Cook, Teresa Zyczynski, Catherine Davis, Hartmut Goldschmidt

Bibliographic record

VenueBlood · 2017
Typearticle
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineDaratumumabInternal medicineObservational studyIxazomibMultiple myelomaFamily medicineBortezomibOncologyCarfilzomib

Abstract

fetched live from OpenAlex

Introduction: The management of multiple myeloma (MM) has undergone profound changes over the recent past due to newer therapeutic agents with novel mechanisms of action and refinement of treatment strategies. Proteasome inhibitors (PIs), immunomodulatory drugs (IMiDs), and 3-drug regimens involving a PI and an IMiD (PI+IMiD) have become standards of care in MM. The immuno-oncology (I-O) agents elotuzumab and daratumumab were approved to treat relapsed or refractory (RR) MM by the US Food and Drug Administration in 2015, and by the European Medicines Agency in 2016. Extension of survival for patients with MM is a realistic goal, although access to therapies may vary between the US and non-US countries. Comparison of treatment patterns and associated outcomes for patients with RRMM in the US with non-US countries will shed light on the impact of treatment access on patient outcomes. Methods:Patients from the US and 5 non-US countries (Canada, France, Germany, Italy, and UK), ≥18 y of age with RRMM, ≥1 prior systemic therapy, and initiating treatment with a PI, an IMiD, a PI+IMiD combination, or an I-O agent within 90 d before or 30 d after study consent, were identified from PREAMBLE, an ongoing, prospective, non-interventional observational study (NCT01838512). Patient data were collected at each healthcare provider visit over a 3-y period or until the end of patient follow-up. Statistical comparisons were made using t tests and Mann-Whitney U tests for continuous variables and chi-square tests for categorical variables; 2-sided p-values were obtained. Overall survival (OS) was analyzed using Cox regression and Kaplan-Meier techniques. Results: 1075 patients with RRMM (median age 69 y; 57.2% male) were enrolled; 341 (31.7%) from the US and 734 (68.3%) from non-US countries. At data cut-off (May 27, 2017), 522 (48.6%) patients had withdrawn from the study; 357 (68.4%) of patients who had withdrawn had died, mainly due to disease progression. Median follow-up was 18.7 mo (interquartile range: 8.8-31.5 mo). Patients from the US and non-US countries were similar with regard to sex, disease status, and International Staging System stage, although compared with patients from non-US countries, patients from the US were younger at baseline, had more comorbidities, and received more prior lines of treatment (Table). The proportion of patients receiving treatment in a subsequent line was similar between the US and non-US countries (54.3% vs 54.8%). Significantly more patients from the US received 2 or more subsequent therapies than patients from non-US countries (63.6% vs 53.4%; p=0.007). In addition, compared with patients from non-US countries, patients from the US were significantly more likely to receive I-O agents (8.2% vs 1.2%, p Median OS was longer for patients from the US than for patients from non-US countries (32.0 vs 28.6 mo), although the difference was not statistically significant (p=0.831). After adjusting for baseline differences between the US and non-US countries, mortality among patients from non-US countries was 30% higher than among patients from the US (hazard ratio 1.30; 95% CI 0.94-1.80; p=0.108). Conclusion: This pilot analysis of data from a clinical setting shows that patients with RRMM in the US had greater access to newer agents and received more subsequent therapies than patients in non-US countries. The longer OS in patients from the US may suggest clinical benefit associated with newer agents. Future analysis may be necessary to validate our findings. Study support:Bristol-Myers Squibb. Disclosures Vij: Celgene, Onyx, Takeda, Novartis, BMS, Sanofi, Janssen, Merck: Consultancy; Takeda, Onyx: Research Funding. Chen: Bristol-Myers Squibb: Employment. Popov: Parexel: Employment. Durie: Johnson & Johnson: Consultancy; Takeda: Consultancy; Amgen: Consultancy. Cook: Celgene: Consultancy, Honoraria, Research Funding, Speakers Bureau; Janssen: Consultancy, Honoraria, Research Funding, Speakers Bureau; Sanofi: Consultancy, Honoraria, Speakers Bureau; Bristol-Myers Squibb: Consultancy, Honoraria; Glycomimetcs: Consultancy, Honoraria; Takeda: Consultancy, Honoraria, Research Funding, Speakers Bureau; Amgen: Consultancy, Honoraria, Research Funding, Speakers Bureau. Zyczynski: Bristol-Myers Squibb: Employment. Davis: Bristol-Myers Squibb: Employment. Goldschmidt: Bristol-Myers Squibb: Consultancy, Honoraria, Membership on an entity9s Board of Directors or advisory committees, Research Funding, Speakers Bureau; Onyx: Membership on an entity9s Board of Directors or advisory committees, Speakers Bureau; Chugai: Consultancy, Honoraria, Research Funding, Speakers Bureau; Morphosys: Research Funding; Celgene: Consultancy, Honoraria, Membership on an entity9s Board of Directors or advisory committees, Research Funding, Speakers Bureau; Takeda: Consultancy, Honoraria, Membership on an entity9s Board of Directors or advisory committees, Research Funding, Speakers Bureau; Janssen: Consultancy, Honoraria, Membership on an entity9s Board of Directors or advisory committees, Research Funding, Speakers Bureau; Novartis: Consultancy, Honoraria, Membership on an entity9s Board of Directors or advisory committees, Research Funding, Speakers Bureau; Amgen: Consultancy, Honoraria, Membership on an entity9s Board of Directors or advisory committees, Research Funding, Speakers Bureau; Millenium: Research Funding, Speakers Bureau.

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)0.003
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 score0.025
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.017
GPT teacher head0.275
Teacher spread0.258 · 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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Citations2
Published2017
Admission routes1
Has abstractyes

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