Impact of Skeletal Muscle Mass on Physical Function and Locomotive Syndrome of Pre- and Postoperative Adult Spinal Deformity.
Tetsuro OhbaKotaro OdaNobuki TanakaGoto GoHirotaka HaroPublished in: Journal of clinical medicine (2024)
Background: The purpose of this study is to identify the relationship between locomotive syndrome (LS) status, physical performance and limb and trunk skeletal muscle mass before and after surgery in adult spinal surgery (ASD) patients. Methods: A retrospective observational investigation of 63 consecutive patients with ASD who underwent spinal surgery was conducted. The total skeletal muscle mass of the arms and legs was considered a measure of the total appendicular skeletal muscle mass measured with whole-body dual-energy X-ray absorptiometry. All data pertaining to the physical performance tests and LS were collected preoperatively with follow-up one year postoperatively. Results: Gait speed, the one-leg standing test and the stand-up test were significantly improved one year after surgery compared to preoperative measurements. The lower extremity skeletal muscle mass predominantly influences physical function improvement including gait stride, one-leg standing and the stand-up test after ASD surgery. Conclusions: This study is the first to show that assessing lower extremity muscles prior to ASD surgery is useful in predicting postoperative recovery.
Keyphrases
- minimally invasive
- dual energy
- autism spectrum disorder
- coronary artery bypass
- skeletal muscle
- attention deficit hyperactivity disorder
- spinal cord
- patients undergoing
- computed tomography
- surgical site infection
- physical activity
- end stage renal disease
- ejection fraction
- intellectual disability
- newly diagnosed
- chronic kidney disease
- high resolution
- spinal cord injury
- magnetic resonance imaging
- mass spectrometry
- body composition
- big data
- prognostic factors
- adipose tissue
- percutaneous coronary intervention
- bone mineral density
- case report
- acute coronary syndrome
- machine learning
- postmenopausal women
- peritoneal dialysis
- young adults
- patient reported outcomes
- deep learning
- coronary artery disease
- childhood cancer