Objective: This study aimed to explore the impact of sarcopenia on clinical outcomes after percutaneous kyphoplasty (PKP) for osteoporotic vertebral compression fracture (OVCF). Methods: We retrospectively analyzed the medical records of patients with single-segment OVCF who underwent percutaneous kyphoplasty (PKP) between September 2021 and August 2022. Patients were categorized into a sarcopenia group (43 patients) and a non-sarcopenia group (125 patients) based on their Advanced Skeletal Muscle Index (ASMI). Clinical and radiological data were collected and analyzed. Results: There were no significant differences between the sarcopenia and non-sarcopenia groups in age, sex, bone mineral density (BMD), body mass index (BMI), fractured segment, fracture type, surgical approach, bone cement volume, bone cement distribution, comorbidities, preoperative and immediate postoperative VAS and ODI scores ( P > .05). However, the time to ambulation, hospital stays, VAS and ODI scores at follow-up, excellent/good rate, and the incidence of residual pain and re-fractures in the non-sarcopenia group were significantly better than those in the sarcopenia group ( P < .05). Meanwhile, radiological outcomes, including regional kyphosis and vertebral height loss rate, were significantly better in the non-sarcopenia group than in the sarcopenia group at 6 and 12 month follow-ups ( P < .05). Conclusion: Clinical outcomes after PKP in patients with OVCF could be negatively affected by sarcopenia. Therefore, prevention and treatment of sarcopenia should be actively considered in the management of patients with OVCF.
Keyphrases
- skeletal muscle
- bone mineral density
- end stage renal disease
- body mass index
- community dwelling
- postmenopausal women
- chronic kidney disease
- ejection fraction
- body composition
- peritoneal dialysis
- healthcare
- type diabetes
- emergency department
- prognostic factors
- pain management
- spinal cord
- minimally invasive
- physical activity
- electronic health record
- drug induced
- weight gain
- deep learning
- patient reported
- radiofrequency ablation
- bone loss