Muscle Depletion Is an Important and Clinically Relevant Predictor of Outcomes after Autologous Hematopoietic Cell Transplantation
Bibliographic record
Abstract
Abstract Introduction: High intensity treatments such as autologous hematopoietic cell transplantation (HCT) can be curative for patients with relapsed/refractory lymphoma (Hodgkin [HL], non-Hodgkin [NHL]), but this needs to be balanced by the risk of non-relapse mortality (NRM) associated with HCT and potentially sub-optimal disease response with less intensive treatments. Measures of pre-treatment body composition such as quantity and quality of muscle are prognostic in patients with solid tumors, but their association with post-HCT outcomes is unknown. We examined the prognostic significance of muscle depletion prior to HCT, defined by having both low muscle quantity (lumbar skeletal muscle index [SMI]) and quality (muscle attenuation [MA]) on computed tomography (CT) imaging, in a population-based cohort of patients undergoing autologous HCT for lymphoma. Next, we examined the prognostic significance of muscle depletion after HCT in a subset of patients with normal muscle composition prior to HCT, allowing us to examine the impact of change in body composition over time. Methods: 440 consecutive patients with lymphoma, age ≥18y, who underwent a first HCT between 2009 and 2014 at a single institution were included in the study. Measures of muscle quantity (SMI) and quality (MA) were ascertained from pre- and post-HCT abdominal CT scans using image analysis software (SliceOmatic; Tomovision, Quebec, Canada). SMI was calculated as the ratio of skeletal muscle area (cm2) divided by height (m)2. Sex and body mass index (BMI)-specific cutoff values of low SMI and MA were used to identify patients with muscle depletion (J Clin Oncol 2013 31:1539). Measurements were made by trained researchers blinded to patient demographics and HCT outcome (Figure 1); 3rd lumbar vertebra was used as a landmark because of its high correlation with whole-body muscle mass (J Clin Oncol 2016 34:1339). This report is limited to 321 (73%) patients with CT scans performed ≤90 days from HCT. Cumulative incidence of NRM was calculated taking into consideration competing risk of disease-related mortality. Kaplan-Meier method was used to examine overall survival (OS). Multivariable Cox regression analysis was used to calculate the hazard ratio (HR) estimates and 95% confidence intervals (CI), adjusted for relevant covariates (demographics, diagnosis, pre-HCT Karnofsky performance score [KPS] and comorbidity index [HCT-CI]). Results: Sixty-two (19.3%) patients had muscle depletion pre-HCT. Median age at HCT was 53y (range: 18-78); 62.0% were male; 54.0% were non-Hispanic white; Diagnoses: HL (N=84 [26.2%]), NHL (N=237 [73.8%]); KPS ≤80 (N=87 [27.1%]); HCT-CI ≥3 (N=52 [16.2%]). Impact of pre-HCT muscle depletion: Patients with pre-HCT muscle depletion had significantly worse 5-y OS (56.4% vs. 77.8%, p<0.001; Figure 2) and higher NRM (11.4% vs. 5.1%, p=0.05) when compared to those with normal body composition. OS was especially poor for patients who were obese (BMI ≥30 kg/m2) and had muscle depletion (20.0% vs. 77.1%, p<0.001) pre-HCT. Median length of hospitalization was also significantly longer (27d vs. 23d; p=0.03) among patients with muscle depletion. Muscle depletion was associated with a 2.2-fold (HR=2.2 [CI: 1.0-4.5]) risk of NRM and 1.8-fold (HR=1.8 [CI: 1.1-3.1]) risk of all-cause mortality when compared to those with normal body composition. Impact of post-HCT muscle depletion: Among 223 patients with normal body composition prior to HCT, 24 (9.3%) developed muscle depletion after HCT, detected at a median 63d (range 27-165) from HCT. In these patients, there was a 3-fold (HR=3.1 [CI: 1.5-6.4]) risk of all-cause mortality compared to those who maintained normal muscle composition throughout HCT. Conclusion: Muscle depletion is an important and independent predictor of outcomes after HCT, with potential additional downstream impacts on health-economic outcomes such as length of hospitalization and the burden of chronic morbidity in long-term survivors. Taken together, these data form the basis for real-time decision making prior to HCT (e.g. pre-habilitation, less intensive treatment approaches), or during HCT (e.g. dietary optimization, increased supportive care services, resistance training), setting the stage for innovative strategies to improve outcomes after HCT. Disclosures Chen: Affimed: Research Funding; Merck & Co., Inc.: Consultancy, Research Funding, Speakers Bureau; Bristol-Myers Squibb: Consultancy, Research Funding; Seattle Genetics: Consultancy, Honoraria, Research Funding, Speakers Bureau; Genentech Inc.: Consultancy; Millennium Pharmaceuticals: Consultancy, Research Funding; Pharmacyclics: Consultancy, Research Funding. Forman:Mustang Therapeutics: Other: Licensing Agreement, Patents & Royalties, Research Funding.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".