Objectives: Sarcopenia impairs physical function and increases healthcare burden among older adults with chronic musculoskeletal disorders. This study described the prevalence of sarcopenia and explored factors associated with sarcopenia among Vietnamese elderly outpatients. Methods: A hospital-based cross-sectional study was conducted among 88 consecutively recruited outpatients aged 60 years or older (mean age 70.5 ± 6.7 years; 69 women, 78.4%) with knee osteoarthritis and/or chronic spinal pain at a tertiary hospital in Northern Vietnam from May 2024 to October 2025. Sarcopenia and severe sarcopenia were defined according to the Asian Working Group for Sarcopenia 2019 criteria. Muscle mass, muscle strength, and physical performance were assessed using standardized bioelectrical impedance analysis, handgrip dynamometry, and usual gait-speed procedures, respectively. The primary analysis was descriptive and exploratory. Multivariable logistic regression was interpreted cautiously because only 36 sarcopenia events were available, and the exploratory CHAID tree was used only as a within-sample descriptive display rather than as a validated prediction model. Results: The prevalence of sarcopenia was 40.9%, including 23.9% with sarcopenia and 17.0% with severe sarcopenia. Age >70 years (adjusted odds ratio [AOR] 9.00, 95% confidence interval [CI] 2.40-33.60), history of falls (AOR 6.33, 95% CI 2.77-14.45), low educational attainment (AOR 2.86, 95% CI 1.46-5.61), and higher PSQI score (AOR 1.16, 95% CI 1.02-1.32) were independently associated with sarcopenia. The wide confidence intervals and low events-per-variable ratio indicate important statistical imprecision. Conclusions: Sarcopenia was common in this outpatient population, although the estimate should be interpreted in light of the modest single-center sample. Simple clinical indicators, particularly age >70 years, fall history, low educational attainment, and poorer sleep quality, may help prioritize further sarcopenia assessment in musculoskeletal outpatient settings. However, because of the modest sample size, residual confounding from unmeasured physical activity and nutrition-related factors, and lack of model validation, these findings should not be used as a formal risk-stratification or prediction algorithm.