Development and validation of a grading system for assessing muscle mass phenotype using mid-upper arm muscle area and handgrip strength in patients with incurable cancer

Association of the muscle mass phenotype as assessed by a grading system with the quality of life of patients with incurable cancer in palliative care

BACKGROUND

Loss of muscle mass (MM) is common in advanced stages of cancer, with an impact on worsening quality of life (QoL). In the current study the relationship of a previously proposed simple grade system to assess MM phenotypes with QoL was investigated to strengthen its clinical significance.

AIM

To verify whether the MM phenotypes, which were evaluated by using a grading system, are associated with the quality of life (QoL) of patients with incurable cancer.

METHODS

Secondary data from a cohort of patients with incurable cancer in palliative care were analyzed. The grade system considers measurements of the muscle area arm and handgrip strength. Based on these measurements, patients are classified as probably non-sarcopenic (NSarc), probably sarcopenic (PSarc), and sarcopenic (Sarc). The outcome measure was QoL domains assessed by the EORTC QoL Questionnaire Core-15. Logistic regression models were used to verify the association of the domains of QoL with the MM phenotypes.

RESULTS

A total of 770 patients were included, median age of 62 years and 56.6% females. The PSarc group had significantly worse scores in the QoL domains when compared to the NSarc group (physical p = 0.001, emotional p = 0.018, fatigue p < 0.001, nausea p = 0.017, insomnia p = 0.001, appetite loss p = 0.002, and global health p = 0.043). Adjusted logistic regression analysis showed an increased risk of worse QoL in the PSarc and Sarc, when compared to NSarc, respectively (odds ratio [OR], 95% confidence interval [CI]): physical (OR: 2.54, CI: 1.78-3.62 and OR: 7.18, CI: 4.24-12.17), emotional (OR: 1.61, CI: 1.15-2.24 and OR: 1.49, CI: 1.01-2.20), fatigue (OR: 1.89, CI: 1.35-2.64 and OR: 1.83, CI: 1.23-2.71), insomnia (OR: 2.01, CI: 1.43-2.83 and OR: 3.11, CI: 2.04-4.75), while appetite loss domain was associated with the PSarc (OR: 1.40, CI: 1.02-1.96), together with global health in the Sarc group (OR: 1.56, CI: 1.06-2.29).

CONCLUSION

The severity of the MM phenotype was associated with a worse QoL domains. Our results highlight the importance of MM preserving to affecting QoL status. The grading system can be useful for predicting the QoL in those patients, and its usefulness can potentially impact clinical and therapeutic decision-making.

Factors associated with variability in skeletal muscle radiodensity in patients with metastatic cancer

OBJECTIVES

This study aimed to explore factors associated with skeletal muscle radiodensity (SMD) variability in patients with metastatic cancer.

METHODS

This study included 393 patients (median age 61 y, 70% women) who had computed tomography (CT) scans within 30 days of inclusion in the study. SMD was evaluated from CT by averaging the Hounsfield unit value of the total muscle area. Skeletal muscle index (SMI), visceral adipose tissue index (VATI), subcutaneous adipose tissue index (SATI), and total adipose tissue index (TATI) were also assessed by CT. Additionally, age, sex, race/skin color, disease characteristics, comorbidities, inflammatory markers, handgrip strength (HGS), and body mass index (BMI) were recorded and evaluated in the linear regression analysis to identify factors associated with SMD variability.

RESULTS

Multivariate explanatory models having SMD as an independent variable were performed and included BMI (model 1, r2 = 0.699), TATI (model 2, r2 = 0.712) or VATI and SATI (model 3, r2 = 0.706) in addition to age, race/skin color, tumor site, kidney disease, serum albumin, HGS, and SMI as dependent variables. For all models, lower SMD was associated with higher age, BMI, and adiposity measurements, kidney disease, White race/skin color, and lower serum albumin, HGS, and SMI. The primary tumor site also contributed to changes in SMD in all models, specifically those located in the gastrointestinal tract, gynecologic, and bone and connective tissue.

CONCLUSION

In this group of patients with metastatic cancer, lower SMD was associated with older age, White race/skin color, and an overall worse clinical condition.

Decision tree algorithm to predict mortality in incurable cancer: a new prognostic model

OBJECTIVES

To develop and validate a new prognostic model to predict 90-day mortality in patients with incurable cancer.

METHODS

In this prospective cohort study, patients with incurable cancer receiving palliative care (n = 1322) were randomly divided into two groups: development (n = 926, 70%) and validation (n = 396, 30%). A decision tree algorithm was used to develop a prognostic model with clinical variables. The accuracy and applicability of the proposed model were assessed by the C-statistic, calibration and receiver operating characteristic (ROC) curve.

RESULTS

Albumin (75.2%), C reactive protein (CRP) (47.7%) and Karnofsky Performance Status (KPS) ≥50% (26.5%) were the variables that most contributed to the classification power of the prognostic model, named Simple decision Tree algorithm for predicting mortality in patients with Incurable Cancer (acromion STIC). This was used to identify three groups of increasing risk of 90-day mortality: STIC-1 - low risk (probability of death: 0.30): albumin ≥3.6 g/dL, CRP <7.8 mg/dL and KPS ≥50%; STIC-2 - medium risk (probability of death: 0.66 to 0.69): albumin ≥3.6 g/dL, CRP <7.8 mg/dL and KPS <50%, or albumin ≥3.6 g/dL and CRP ≥7.8 mg/dL; STIC-3 - high risk (probability of death: 0.79): albumin <3.6 g/dL. In the validation dataset, good accuracy (C-statistic ≥0.71), Hosmer-Lemeshow p=0.12 and area under the ROC curve=0.707 were found.

CONCLUSIONS

STIC is a valid, practical tool for stratifying patients with incurable cancer into three risk groups for 90-day mortality.

Prediction equations to estimate muscle mass using anthropometric data: a systematic review

CONTEXT

Reduced muscle mass is linked to poor outcomes in both inpatients and outpatients, highlighting the importance of muscle mass assessment in clinical practice. However, laboratory methods to assess muscle mass are not yet feasible for routine use in clinical practice because of limited availability and high costs.

OBJECTIVE

This work aims to review the literature on muscle mass prediction by anthropometric equations in adults or older people.

DATA SOURCES

The following databases were searched for observational studies published until June 2022: MEDLINE, Embase, Scopus, SPORTDiscus, and Web of Science.

DATA EXTRACTION

Of 6437 articles initially identified, 63 met the inclusion criteria for this review. Four independent reviewers, working in pairs, selected and extracted data from those articles.

DATA ANALYSIS

Two studies reported new equations for prediction of skeletal muscle mass: 10 equations for free-fat mass and lean soft tissue, 22 for appendicular lean mass, 7 for upper-body muscle mass, and 7 for lower-body muscle mass. Twenty-one studies validated previously proposed equations. This systematic review shows there are numerous equations in the literature for muscle mass prediction, and most are validated for healthy adults. However, many equations were not always accurate and validated in all groups, especially people with obesity, undernourished people, and older people. Moreover, in some studies, it was unclear if fat-free mass or lean soft tissue had been assessed because of an imprecise description of muscle mass terminology.

CONCLUSION

This systematic review identified several feasible, practical, and low-cost equations for muscle mass prediction, some of which have excellent accuracy in healthy adults, older people, women, and athletes. Malnourished individuals and people with obesity were understudied in the literature, as were older people, for whom there are only equations for appendicular lean mass.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO registration number CRD42021257200.