“Maximal” minimally invasive thymectomy in patients with Nonthymomatous Myasthenia Gravis – short-term results over a 10year period – retrospective study

Introduction: Thymectomy has always been considered an important treatment modality for patients with Myasthenia Gravis (MG). Because of the absence of randomized trials, its exact role was not clear and was even questioned by some. Indications for surgery were made on empirical basis. It was not until the results of the first randomized double- blinded multicentric study were published, where the combined surgical treatment of patients with nonthymomatous MG was proved superior to conservative treatment alone. In this study the technique of extensive thymectomy via longitudinal sternotomy was used as a mainstay of surgical treatment. In the advent of minimally invasive techniques a variety of options were presented for minimally invasive surgical treatment of thymic pathology. Methods: The authors present a retrospective analysis of short term results using the slightly modified method of “Minimally Invasive Maximal Thymectomy” developed by Zielinski over a 10 year period (2008−2018). Besides the demographics, we assessed the peri- and postoperative results such as the length of surgery, blood loss, the need for conversion, learning curve impact, the lengths of drainage and hospital stay and the complication rate, both surgical and systemic. Results: 48 patients underwent surgery using the above mentioned method for non-thymomatous MG. 81.25% (n=39) of patients were diagnosed with a seropositive generalized form of MG, 6 (12.5%) had seropositive ocular form and 3 patients (6.25%) had seronegative form of the disease. The sex ratio profoundly favored females (89.5%; n=43). The median value of the length of surgery was 186,5 minutes, the mean LOS and length of drainage were 4 and 3 days, respectively. Conclusion: According to our results and experience with Minimally Invasive Maximal Thymectomy, we found it to be an effective and safe method for MG patients after conquering the learning curve.