Independent long-term result of robotic thymectomy for myasthenia gravis, a single center experience
Yin Dong-tao, Ling Huang, Bing Han, Xiu Chen, Shimin Yin, Wenxia Zhou, Jian Chu, Tao Liang, Tianyang Yun, Yang Liu
- Year
- 2018
- Citations
- 7
- Access
- Open access
Abstract
BACKGROUND: Robotic thymectomy has been suggested a feasible and safe approach for myasthenia gravis (MG). Few investigations have revealed the independent effect of robotic thymectomy without the confounding impact of immunosuppressive (IM) therapy. METHODS: Between May 2009 and December 2012, robotic extended thymectomy was carried out for patients with diagnosis of MG. The clinical data, subsequent neurological therapy and postintervention status were collected. RESULTS: Data of 37 cases was available for analysis. The mean follow-up was 70.0±13.3 months. The median age was 40 years. Twelve (32.4%) patients kept free of IM therapy, and 25 (67.6%) patients accepted postoperatively. The overall 5-year complete stable remission (CSR) rate was 40.6% and improvement rate was 81.6%. The young (age ≤40) displayed a significant better CSR rate (P=0.015) and a trend of better improvement rate (P=0.050) compared to the old (age >40). Patients without usage of IM therapy showed significant higher CSR rate (P=0.014) and improvement rate (P=0.024) compared to those with usage of IM therapy. Patients with Myasthenia Gravis Foundation of America (MGFA) classes I showed a trend of higher remission rate by multivariate analysis. No significant differences were found for the remission rate according to gender, pathology, and the duration of symptoms. CONCLUSIONS: The mono-therapy of robotic thymectomy may bring with a satisfactory long-term result for part of MG patients. Precision selection and individualized therapy are of the most importance.
Keywords
Related papers
Statistical Learning Theory
Yuhai Wu, Vladimir Vapnik
1999
Fractional Differential Equations
Igor Podlubný
2025
Applied Nonlinear Control
Jean-Jacques Slotine, Weiping Li
1991
Genetic Programming: On the Programming of Computers by Means of Natural Selection
John R. Koza
1992