gms | German Medical Science

71. Jahrestagung der Deutschen Gesellschaft für Neurochirurgie (DGNC)
9. Joint Meeting mit der Japanischen Gesellschaft für Neurochirurgie

Deutsche Gesellschaft für Neurochirurgie (DGNC) e. V.

21.06. - 24.06.2020

Surgeon’s learning curve in stereotactic robot-assisted surgery

Operative Lernkurve in der roboterassistierten, stereotaktischen Chirurgie

Meeting Abstract

  • presenting/speaker Kathrin Machetanz - Universitätsklinikum Tübingen, Klinik für Neurochirurgie, Tübingen, Deutschland
  • Florian Grimm - Universitätsklinikum Tübingen, Klinik für Neurochirurgie, Tübingen, Deutschland
  • Alireza Gharabaghi - Universitätsklinikum Tübingen, Klinik für Neurochirurgie, Tübingen, Deutschland
  • Marcos Tatagiba - Universitätsklinikum Tübingen, Klinik für Neurochirurgie, Tübingen, Deutschland
  • Georgios Naros - Universitätsklinikum Tübingen, Klinik für Neurochirurgie, Tübingen, Deutschland

Deutsche Gesellschaft für Neurochirurgie. 71. Jahrestagung der Deutschen Gesellschaft für Neurochirurgie (DGNC), 9. Joint Meeting mit der Japanischen Gesellschaft für Neurochirurgie. sine loco [digital], 21.-24.06.2020. Düsseldorf: German Medical Science GMS Publishing House; 2020. DocV139

doi: 10.3205/20dgnc140, urn:nbn:de:0183-20dgnc1407

Veröffentlicht: 26. Juni 2020

© 2020 Machetanz et al.
Dieser Artikel ist ein Open-Access-Artikel und steht unter den Lizenzbedingungen der Creative Commons Attribution 4.0 License (Namensnennung). Lizenz-Angaben siehe http://creativecommons.org/licenses/by/4.0/.


Gliederung

Text

Objective: Frame-based stereotactic procedures are still the gold standard in neurosurgery, however there is an increasing interest in robot-assisted technologies. Introducing these increasingly complex technologies in the clinical setting raises the question about the time efficacy of the system (i.e. operative time) and the essential learning curve of the surgeon.

Methods: This retrospective study enrolled 23 patients (52.4+/-24.1 years, 11 female) who underwent a robotic-assisted procedure performed by the same surgeon within the first four months after installation of the robot. We evaluated the intraoperative preparation time for setting-up the system and the operation time itself (i.e. skin-to-skin).

Results: In the first four months, we performed 23 robotic-assisted surgeries (19 biopsies, 3 SEEG impantations and 1 endoscopic procedure). The mean intraoperative preparation time was 36.7+/-16.0 min strongly depending on the applied registration technique - i.e. skin fiducials (59.5+/-4.7 min, n = 4), bone fiducials (29.1+/-8.3 min, n = 14) or surface registration (40.0+/-19.5 min, n = 5). However, there was a significant reduction of the preparation time during that period to 21.2+/-3.4 min (for the last five surgeries). Mean operation time was 54.6+/-34.3 min (biopsies: 39.6+/-11.1 min, SEEG: 110.3+/-28.7 min, Endoscopy: 72 min). In contrast to the preparation time, there was no significant improvement of the operation time over time.

Conclusion: Introducing stereotactic robotic-assisted surgery in an established clinical setting necessitates initially a prolonged intraoperative preparation time. However, there is a steep learning curve within the first 20 cases. Thus, a stereotactic robot can be integrated in the daily routines in a decent period of time.