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Record W3211973494 · doi:10.1182/blood-2021-149784

Sarcopenia Is a Clinically Relevant and Independent Predictor of Health Outcomes after Chimeric Antigen Receptor T-Cell Therapy for Lymphoma

2021· article· en· W3211973494 on OpenAlexaboutno aff
Alex Iukuridze, Jennifer Berano Teh, Justin Ramos, Teresa Vera, Kyuwan Lee, Rusha Bhandari, Andrew Artz, Elizabeth Budde, Alex F. Herrera, Leslie Popplewell, Geoffrey Shouse, Tanya Siddiqi, Stephen J. Forman, Lennie Wong, Saro H. Armenian

Bibliographic record

VenueBlood · 2021
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineDiffuse large B-cell lymphomaChimeric antigen receptorSarcopeniaInternal medicineCytokine release syndromeOncologyLymphomaPopulationAggressive lymphomaCancerRituximabImmunotherapy

Abstract

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Abstract Introduction: Chimeric antigen receptor T cell (CAR-T) therapy is an effective treatment for patients with relapsed/refractory diffuse large B cell lymphoma (DLBCL) or other B-cell lymphomas. However, the potent anti-lymphoma effect of CAR-T is balanced by the risk of acute toxicities, namely cytokine release syndrome (CRS) and Immune effector cell-Associated Neurotoxicity Syndrome (ICANS), as well as the variable length of progression-free survival (PFS) after CAR-T. Tools to better risk-stratify for adverse outcomes and to guide targeted interventions are lacking. Sarcopenia (loss of lean muscle mass) is an important cause of age-related functional decline in the general population and is an independent predictor of health outcomes in patients with solid and hematologic cancers, irrespective of age or comorbidity. Advances in software technology have facilitated the near real-time integration of body composition measurements into imaging studies obtained as part of standard clinical care. To date, there have been no studies to examine the association between sarcopenia and outcomes after CAR-T therapy. Methods: Using a retrospective cohort design, 280 consecutive patients with DLBCL or B-cell lymphoma, age ≥18y, and treated with CAR-T therapy between 2015 to 2020 at a single center were included in the study. This analysis was restricted to 226 (80.7%) patients with available computed tomography scans ≤60d from CAR-T. Skeletal muscle area was ascertained from abdominal scans using an automatic image analysis software (APACS; Voronoi Health Analytics; Vancouver, Canada); 3rd lumbar vertebra was used as a landmark because of its high correlation with whole-body muscle mass (J Clin Oncol 2016 34:1339); Figure. Trained researchers blinded to patient demographics and outcomes manually validated these measurements (SliceOmatic; Tomovision; Quebec, Canada). Skeletal muscle index (SMI) was calculated as the ratio of skeletal muscle area (cm 2) divided by height (m). Sarcopenia was defined according to sex-based cutoffs (lowest SMI tertile). Kaplan-Meier method was used to examine PFS at one-year. Multivariable regression was used to calculate the hazard ratio (HR) for PFS and odds ratio (OR) for toxicities with 95% confidence intervals (CI), adjusted for covariates (demographics [age, race/ethnicity], disease characteristics [largest lymph node diameter, blood lactate dehydrogenase], CAR-T product, ECOG performance status). Results: Median age at CAR-T was 63y (range: 18-84); 65.9% were male; 50.9% were non-Hispanic white; 8.8% had ECOG ≥2; 80.5% had a diagnosis of DLBCL; CAR-T products: axicabtagene ciloleucel (51.3%), lisocabtagene maraleucel (31.9%), other (16.8%); 46.9% were treated on a clinical trial; median residual lymph node diameter prior to CAR-T was 2.3cm (range: 0-17.2); 8.0% underwent HCT <1 year after CAR-T and follow-up was censored at HCT. Outcomes: 59.1% developed CRS (18.2% grade ≥2) and 30.1% developed ICANS (15.9% grade ≥2). In adjusted analyses, the odds of developing CRS or ICANS was 1.9-fold (CRS: 1.89 [95%CI: 1.02-3.5], ICANS: 1.93 [1.06-3.51]) higher among patients who were sarcopenic (reference: normal body composition). Average length of hospitalization was also longer (25.6d vs. 21.9d; p=0.037) among patients with sarcopenia. Survival: One-year PFS for the overall cohort was 50.1% (±4.2); PFS was significantly worse for patients who were sarcopenic compared to those with normal muscle mass (35.1% [±6.2] vs. 57.7% [±4.3], p=0.005; Figure). In adjusted analyses, sarcopenia was associated with inferior one-year PFS (HR=1.73 [CI: 1.12-2.68]) compared to those with normal muscle mass. Conclusion: Sarcopenia is an important and independent predictor of outcomes after CAR-T with potential downstream health-economic consequences, including increased burden of acute toxicities and prolonged length of hospitalization. Taken together, these data form the basis for real-time decision making prior to CAR-T (e.g. pre-habilitation, consideration of alternative treatments), or during/shortly after CAR-T (e.g. increased supportive care, rehabilitation), setting the stage for innovative strategies to improve outcomes after CAR-T therapy. Figure 1 Figure 1. Disclosures Artz: Radiology Partners: Other: Spouse has equity interest in Radiology Partners, a private radiology physician practice. Budde: Merck, Inc: Research Funding; Amgen: Research Funding; Astra Zeneca: Research Funding; Mustang Bio: Research Funding; Novartis: Consultancy; Gilead: Consultancy; Roche: Consultancy; Beigene: Consultancy. Herrera: Merck: Consultancy, Research Funding; Gilead Sciences: Research Funding; Bristol Myers Squibb: Consultancy, Research Funding; AstraZeneca: Consultancy, Research Funding; Karyopharm: Consultancy; Kite, a Gilead Company: Research Funding; Seagen: Consultancy, Research Funding; ADC Therapeutics: Consultancy, Research Funding; Takeda: Consultancy; Tubulis: Consultancy; Genentech: Consultancy, Research Funding. Popplewell: Novartis: Other: Travel; Pfizer: Other: Travel; Hoffman La Roche: Other: Food. Shouse: Kite Pharmaceuticals: Speakers Bureau; Beigene Pharmaceuticals: Honoraria. Siddiqi: Kite Pharma: Membership on an entity's Board of Directors or advisory committees; Juno therapeutics: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; BMS: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Abbvie: Membership on an entity's Board of Directors or advisory committees; AstraZeneca: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; BeiGene: Other: DSM Member, Speakers Bureau; PCYC: Speakers Bureau; Jannsen: Speakers Bureau; Dava Oncology: Honoraria; ResearchToPractice: Honoraria. Forman: Lixte Biotechnology: Consultancy, Current holder of individual stocks in a privately-held company; Allogene: Consultancy; Mustang Bio: Consultancy, Current holder of individual stocks in a privately-held company.

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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.033
GPT teacher head0.341
Teacher spread0.307 · 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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Citations12
Published2021
Admission routes1
Has abstractyes

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