Associations of Skeletal Muscle With Symptom Burden and Clinical Outcomes in Hospitalized Patients With Advanced Cancer

Seventer, Emily Van; Marquardt, J. Peter; Troschel, Amelie S.; Best, Till D.; Horick, Nora; Azoba, Chinenye C.; Newcomb, Richard; Roeland, Eric; Rosenthal, Michael H.; Bridge, Christopher P.; Greer, Joseph A.; El‐Jawahri, Areej; Temel, Jennifer S.; Fintelmann, Florian J.; Nipp, Ryan David

doi:10.6004/jnccn.2020.7618

Cited by 12 publications

(12 citation statements)

References 55 publications

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“…We used multivariable linear regression to model the relationship between muscle metrics and hospital length of stay as well as patient‐reported outcomes. We adjusted all models for potential confounders: age, sex, marital status, education level, insurance, cancer type, and body mass index (BMI) 29 . If the muscle metric was SMRA or SMG, we additionally adjusted for presence of intravenous contrast 30 …”

Section: Methodsmentioning

confidence: 99%

Percentile‐based averaging and skeletal muscle gauge improve body composition analysis: validation at multiple vertebral levels

Marquardt

Roeland

Seventer

et al. 2021

J cachexia sarcopenia muscle

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Background Skeletal muscle metrics on computed tomography (CT) correlate with clinical and patient-reported outcomes. We hypothesize that aggregating skeletal muscle measurements from multiple vertebral levels and skeletal muscle gauge (SMG) better predict outcomes than skeletal muscle radioattenuation (SMRA) or -index (SMI) at a single vertebral level. Methods We performed a secondary analysis of prospectively collected clinical (overall survival, hospital readmission, time to unplanned hospital readmission or death, and readmission or death within 90 days) and patient-reported outcomes (physical and psychological symptom burden captured as Edmonton Symptom Assessment Scale and Patient Health Questionnaire) of patients with advanced cancer who experienced an unplanned admission to Massachusetts General Hospital from 2014 to 2016. First, we assessed the correlation of skeletal muscle cross-sectional area, SMRA, SMI, and SMG at one or more of the following thoracic (T) or lumbar (L) vertebral levels: T5, T8, T10, and L3 on CT scans obtained ≤50 days before index assessment. Second, we aggregated measurements across all available vertebral levels using percentile-based averaging (PBA) to create the average percentile. Third, we constructed one regression model adjusted for age, sex, sociodemographic factors, cancer type, body mass index, and intravenous contrast for each combination of (i) vertebral level and average percentile, (ii) muscle metrics (SMRA, SMI, & SMG), and (iii) clinical and patient-reported outcomes. Fourth, we compared the performance of vertebral levels and muscle metrics by ranking otherwise identical models by concordance statistic, number of included patients, coefficient of determination, and significance of muscle metric. ResultsWe included 846 patients (mean age: 63.5 ± 12.9 years, 50.5% males) with advanced cancer [predominantly gastrointestinal (32.9%) or lung (18.9%)]. The correlation of muscle measurements between vertebral levels ranged from 0.71 to 0.84 for SMRA and 0.67 to 0.81 for SMI. The correlation of individual levels with the average percentile was 0.90-0.93 for SMRA and 0.86-0.92 for SMI. The intrapatient correlation of SMRA with SMI was 0.21-0.40. PBA allowed for inclusion of 8-47% more patients than any single-level analysis. PBA outperformed single-level analyses across all comparisons with average ranks 2.6, 2.9, and 1.6 for concordance statistic, coefficient of determination, and significance (range 1-5, μ = 3), respectively. On average, SMG outperformed SMRA and SMI across outcomes and vertebral levels: the average rank of SMG was 1.4, 1.4, and 1.4 for concordance statistic, coefficient of determination, and significance (range 1-3, μ = 2), respectively. Conclusions Multivertebral level skeletal muscle analyses using PBA and SMG independently and additively outperform analyses using individual levels and SMRA or SMI.

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Section: Methodsmentioning

confidence: 99%

Percentile‐based averaging and skeletal muscle gauge improve body composition analysis: validation at multiple vertebral levels

Marquardt

Roeland

Seventer

et al. 2021

J cachexia sarcopenia muscle

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“…Notably, we found that relatively few Black, Asian, and Hispanic/Latino patients enrolled onto cancer clinical trials in this study. Our overall cancer center population has historically included approximately 3% to 4% Black patients, 2% to 3% Asian patients, and 2% to 3% Hispanic/Latino patients 11,12 . These numbers largely reflect the overall phase 1, 2, and 3 cancer clinical trial population that we have reported on in this study.…”

Section: Discussionmentioning

confidence: 92%

Disparities in phase 1 cancer clinical trial enrollment

Perni

Moy

Nipp

2021

Cancer

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Background Phase 1 trials are increasingly important in the molecularly driven era of oncology, but few studies have examined phase 1 participation disparities. The authors of this study investigated factors associated with phase 1 versus phase 2/3 trial enrollment. Methods They authors conducted a cross‐sectional study using serial samples of patients age ≥18 years enrolling on cancer trials from October 2011 to November 2014 at an academic cancer center. They used univariable and multivariable logistic regression models to analyze sociodemographic and clinical associations with phase 1 versus phase 2/3 trial enrollment. Results Among 3103 patients enrolled in cancer trials, 2657 unique patients participated in phase 1/2/3 trials. For patients enrolled in phase 1 (n = 1401) versus phase 2/3 (n = 1256) trials, we found no significant differences by age, insurance status, marital status, and income. Overall, 1216 (93%) White, 72 (6%) Asian, and 21 (2%) Black patients enrolled on phase 1 trials, whereas 1068 (93%) White, 40 (3%) Asian, and 43 (4%) Black patients enrolled on phase 2/3 trials. Adjusting for age, sex, race, ethnicity, insurance status, marital status, income, cancer type, disease status, travel distance, and trial year, compared with White patients, Black patients had lower phase 1 enrollment (odds ratio [OR], 0.46; 95% confidence interval [CI], 0.25‐0.82), as did Hispanic/Latino (OR, 0.25; 95% CI, 0.08‐0.79) and male patients (OR, 0.77; 95% CI, 0.62‐0.94). Asian patients had higher phase 1 enrollment (OR, 1.38; 95% CI, 0.88‐2.16). Conclusions Disparities in phase 1 versus phase 2/3 cancer clinical trial enrollment underscore the urgent need for interventions addressing inequities in early‐phase trial participation. Lay Summary Phase 1 trials are of increasing importance in oncology. The authors of the study analyzed all patients enrolling on cancer clinical trials at a large academic cancer center from October 2011 to November 2014. Among the 2657 trial participants, when age, sex, race, ethnicity, insurance status, marital status, income, cancer type, disease status, travel distance, and trial year were taken into account, Black, Hispanic/Latino, and male patients were less likely to enroll on phase 1 trials versus phase 2/3 trials. These findings suggest a need for targeted interventions to improve access to and education about phase 1 trials for Black and Hispanic/Latino patients.

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“…Collectively, our findings expand on existing evidence of the association between unfavorable outcomes and cachexia, sarcopenia, and frailty in cancer patients. 24,31,32 Studies have demonstrated the correlation between body composition changes and survival outcomes, such as tolerance to chemotherapy, health care use, and survival, in various cancer populations. 25,33 Several reviews of cancerrelated body composition changes have highlighted that, across various cancer types (e.g., gastrointestinal, lung, renal) and stages (e.g., resectable, metastatic), patients with adverse body composition changes often experience poor survival outcomes.…”

Section: Discussionmentioning

confidence: 99%

Evaluating frailty, mortality, and complications associated with metastatic spine tumor surgery using machine learning–derived body composition analysis

Massaad

Bridge

Kiapour

et al. 2022

Journal of Neurosurgery: Spine

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OBJECTIVE Cancer patients with spinal metastases may undergo surgery without clear assessments of prognosis, thereby impacting the optimal palliative strategy. Because the morbidity of surgery may adversely impact recovery and initiation of adjuvant therapies, evaluation of risk factors associated with mortality risk and complications is critical. Evaluation of body composition of cancer patients as a surrogate for frailty is an emerging area of study for improving preoperative risk stratification. METHODS To examine the associations of muscle characteristics and adiposity with postoperative complications, length of stay, and mortality in patients with spinal metastases, the authors designed an observational study of 484 cancer patients who received surgical treatment for spinal metastases between 2010 and 2019. Sarcopenia, muscle radiodensity, visceral adiposity, and subcutaneous adiposity were assessed on routinely available 3-month preoperative CT images by using a validated deep learning methodology. The authors used k-means clustering analysis to identify patients with similar body composition characteristics. Regression models were used to examine the associations of sarcopenia, frailty, and clusters with the outcomes of interest. RESULTS Of 484 patients enrolled, 303 had evaluable CT data on muscle and adiposity (mean age 62.00 ± 11.91 years; 57.8% male). The authors identified 2 clusters with significantly different body composition characteristics and mortality risks after spine metastases surgery. Patients in cluster 2 (high-risk cluster) had lower muscle mass index (mean ± SD 41.16 ± 7.99 vs 50.13 ± 10.45 cm2/m2), lower subcutaneous fat area (147.62 ± 57.80 vs 289.83 ± 109.31 cm2), lower visceral fat area (82.28 ± 48.96 vs 239.26 ± 98.40 cm2), higher muscle radiodensity (35.67 ± 9.94 vs 31.13 ± 9.07 Hounsfield units [HU]), and significantly higher risk of 1-year mortality (adjusted HR 1.45, 95% CI 1.05–2.01, p = 0.02) than individuals in cluster 1 (low-risk cluster). Decreased muscle mass, muscle radiodensity, and adiposity were not associated with a higher rate of complications after surgery. Prolonged length of stay (> 7 days) was associated with low muscle radiodensity (mean 30.87 vs 35.23 HU, 95% CI 1.98–6.73, p < 0.001). CONCLUSIONS Body composition analysis shows promise for better risk stratification of patients with spinal metastases under consideration for surgery. Those with lower muscle mass and subcutaneous and visceral adiposity are at greater risk for inferior outcomes.

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Associations of Skeletal Muscle With Symptom Burden and Clinical Outcomes in Hospitalized Patients With Advanced Cancer

Cited by 12 publications

References 55 publications

Percentile‐based averaging and skeletal muscle gauge improve body composition analysis: validation at multiple vertebral levels

Percentile‐based averaging and skeletal muscle gauge improve body composition analysis: validation at multiple vertebral levels

Disparities in phase 1 cancer clinical trial enrollment

Evaluating frailty, mortality, and complications associated with metastatic spine tumor surgery using machine learning–derived body composition analysis

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