Risto Kesävuori1, Peter Raivio2, Janne J Jokinen1, Antero Sahlman1, Kari Teittinen1, Antti Vento1. 1. Department of Cardiac Surgery, Heart and Lung Center, Helsinki University Central Hospital, Helsinki, Finland. 2. Department of Cardiac Surgery, Heart and Lung Center, Helsinki University Central Hospital, Helsinki, Finland. Electronic address: peter.raivio@hus.fi.
Abstract
OBJECTIVE: To report the learning curve and early results of robotic mitral valve repairs in comparison with propensity score-matched sternotomy controls after the adoption of a robotic mitral valve surgery program in a university teaching hospital. METHODS: A total of 142 patients underwent robotic mitral valve repair due to degenerative mitral regurgitation between May 2011 and December 2015. Control patients operated on via the conventional sternotomy approach were selected by the use of propensity score analysis resulting in 2 well-matched study groups. RESULTS: Valve repair rate was 98.6% and 97.9% in the robotic and sternotomy groups, respectively. Operation length, cardiopulmonary bypass, aortic crossclamp, and ventilation times were shorter in the sternotomy group. All of these times were statistically significantly reduced within the robotic group during the learning curve. Even though there was no statistically significant difference in the rate of perioperative complications between the groups, 3 patients in the robotic group required postoperative extracorporeal membrane oxygenation due to low cardiac output, and 1 patient in the robotic group died. In the robotic and sternotomy groups, 86.3% versus 84.7% of patients had grade ≤1+ mitral valve regurgitation at the latest follow-up visit, and there was no statistically significant difference in survival or reoperation rate between the 2 study groups during follow-up. CONCLUSIONS: The present series reports the entire early learning curve related to the introduction of robotic mitral valve repair in our institution. In all, repair rate and early durability were acceptable, but more patients in the robotic group had serious complications. Early major robotic complications that occurred may have been related to the simultaneous use of intra-aortic occlusion.
OBJECTIVE: To report the learning curve and early results of robotic mitral valve repairs in comparison with propensity score-matched sternotomy controls after the adoption of a robotic mitral valve surgery program in a university teaching hospital. METHODS: A total of 142 patients underwent robotic mitral valve repair due to degenerative mitral regurgitation between May 2011 and December 2015. Control patients operated on via the conventional sternotomy approach were selected by the use of propensity score analysis resulting in 2 well-matched study groups. RESULTS: Valve repair rate was 98.6% and 97.9% in the robotic and sternotomy groups, respectively. Operation length, cardiopulmonary bypass, aortic crossclamp, and ventilation times were shorter in the sternotomy group. All of these times were statistically significantly reduced within the robotic group during the learning curve. Even though there was no statistically significant difference in the rate of perioperative complications between the groups, 3 patients in the robotic group required postoperative extracorporeal membrane oxygenation due to low cardiac output, and 1 patient in the robotic group died. In the robotic and sternotomy groups, 86.3% versus 84.7% of patients had grade ≤1+ mitral valve regurgitation at the latest follow-up visit, and there was no statistically significant difference in survival or reoperation rate between the 2 study groups during follow-up. CONCLUSIONS: The present series reports the entire early learning curve related to the introduction of robotic mitral valve repair in our institution. In all, repair rate and early durability were acceptable, but more patients in the robotic group had serious complications. Early major robotic complications that occurred may have been related to the simultaneous use of intra-aortic occlusion.
Authors: Michael L Williams; Bridget Hwang; Linna Huang; Ashley Wilson-Smith; John Brookes; Aditya Eranki; Tristan D Yan; T Sloane Guy; Johannes Bonatti Journal: Ann Cardiothorac Surg Date: 2022-09