Bay Benjamin, Goßling Alina, Rilinger Jonathan, von Zur Mühlen Constantin, Hofmann Felix, Nef Holger, Möllmann Helge, Kellner Caroline, Seiffert Moritz, Brunner Fabian J
Department of Cardiology, University Heart and Vascular Center Hamburg, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246, Hamburg, Germany.
German Center for Cardiovascular Research (DZHK), Partner Site Hamburg/Kiel/Lübeck, Hamburg, Germany.
Clin Res Cardiol. 2025 Feb 5. doi: 10.1007/s00392-025-02596-6.
Robotic-assisted percutaneous coronary intervention (R-PCI) is an efficacious and safe treatment option for coronary artery disease. However, predictors of manual support during R-PCI are unknown, which we aimed to investigate in a multi-center study.
We utilized patient-level data from R-PCIs carried out from 2020 to 2022 at four sites in Germany. Manual support was defined as the combination of partial manual assistance, where the procedure is ultimately completed using robotic techniques, and manual conversion. A two-step selection process based on akaike information criteria was used to identify the ideal multivariable model predicting manual support.
In 210 patients (median age 69.0 years; 25.7% female), a total of 231 coronary lesions were treated by R-PCI. Manual support was needed in 46 lesions (19.9%). Procedures requiring manual support were associated with significantly longer procedural times, greater total contrast fluid volumes, longer fluoroscopy times, and higher dose-area products. Amongst the predictors of manual support were lesions in the left anterior descending artery [OR: 1.09 (95%-CI: 0.99-1.20)], aorto-ostial lesions [OR: 1.35 (95%-CI: 1.11-1.64)], chronic total occlusions [OR: 1.78 (95%-CI: 1.38-2.31)], true bifurcations [OR: 1.37 (95%-CI: 1.17-1.59)], and severe calcification [OR: 1.13 (95%-CI: 1.00-1.27)].
Our findings reveal that nearly one out five of patients undergoing R-PCI required manual support, which was linked to longer procedure durations. Predictors of manual support reflected characteristics of more complex coronary lesions. These results highlight the limitations of current R-PCI platforms and underscore the need for technical advancements to address different clinical scenarios.
机器人辅助经皮冠状动脉介入治疗(R-PCI)是治疗冠状动脉疾病的一种有效且安全的选择。然而,R-PCI过程中手动支持的预测因素尚不清楚,我们旨在通过一项多中心研究进行调查。
我们利用了2020年至2022年在德国四个地点进行的R-PCI患者层面的数据。手动支持定义为部分手动辅助(最终使用机器人技术完成手术)和手动转换的组合。基于赤池信息准则的两步选择过程用于确定预测手动支持的理想多变量模型。
在210例患者(中位年龄69.0岁;25.7%为女性)中,共通过R-PCI治疗了231处冠状动脉病变。46处病变(19.9%)需要手动支持。需要手动支持的手术与显著更长的手术时间、更大的总造影剂液体量、更长的透视时间和更高的剂量面积乘积相关。手动支持的预测因素包括左前降支病变[比值比(OR):1.09(95%置信区间:0.99-1.20)]、主动脉开口病变[OR:1.35(95%置信区间:1.11-1.64)]、慢性完全闭塞[OR:1.78(95%置信区间:1.38-2.31)]、真性分叉病变[OR:1.37(95%置信区间:1.17-1.59)]和严重钙化[OR:1.13(95%置信区间:1.00-1.27)]。
我们的研究结果显示,接受R-PCI的患者中近五分之一需要手动支持,这与更长的手术持续时间有关。手动支持的预测因素反映了更复杂冠状动脉病变的特征。这些结果凸显了当前R-PCI平台的局限性,并强调了技术进步以应对不同临床场景的必要性。