AI Article Synopsis

  • This study investigates the prevalence of pre-therapeutic sarcopenia (PS) in adult cancer patients and its effects on cancer treatment outcomes.
  • A meta-analysis was conducted, reviewing data from 65,936 cancer patients, revealing a PS prevalence of 38% and significant associations with poorer overall survival, progression-free survival, and increased complications.
  • Using a consensus-based algorithm that included muscle mass and strength, the prevalence dropped to 22% and showed even stronger correlations with patient outcomes, indicating the importance of assessing PS in cancer care.

Article Abstract

This study will address the prevalence of pre-therapeutic sarcopenia (PS) and its clinical impact during cancer treatment among adult cancer patients ≥ 18 years of age. A meta-analysis (MA) with random-effect models was performed via a MEDLINE systematic review, according to the PRISMA statement, focusing on articles published before February 2022 that reported observational studies and clinical trials on the prevalence of PS and the following outcomes: overall survival (OS), progression-free survival (PFS), post-operative complications (POC), toxicities (TOX), and nosocomial infections (NI). A total of 65,936 patients (mean age: 45.7-85 y) with various cancer sites and extensions and various treatment modes were included. Mainly defined by CT scan-based loss of muscle mass only, the pooled prevalence of PS was 38.0%. The pooled relative risks were 1.97, 1.76, 2.70, 1.47, and 1.76 for OS, PFS, POC, TOX, and NI, respectively (moderate-to-high heterogeneity, I: 58-85%). Consensus-based algorithm definitions of sarcopenia, integrating low muscle mass and low levels of muscular strength and/or physical performance, lowered the prevalence (22%) and heterogeneity (I < 50%). They also increased the predictive values with RRs ranging from 2.31 (OS) to 3.52 (POC). PS among cancer patients is prevalent and strongly associated with poor outcomes during cancer treatment, especially when considering a consensus-based algorithm approach.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10005339PMC
http://dx.doi.org/10.3390/nu15051193DOI Listing

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