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Published on: May 29, 2015
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Manual support during robotic-assisted percutaneous coronary intervention.
Benjamin Bay1,2,3, Alina Goßling1,3, Jonathan Rilinger4
1Department of Cardiology, University Heart and Vascular Center Hamburg, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246, Hamburg, Germany.
Summary
Nearly 20% of patients undergoing robotic-assisted percutaneous coronary intervention (R-PCI) require manual support, associated with longer procedures. Complex coronary lesions predict the need for manual assistance during R-PCI.
Area of Science:
- Cardiovascular Interventions
- Medical Robotics
- Interventional Cardiology
Background:
- Robotic-assisted percutaneous coronary intervention (R-PCI) is a recognized treatment for coronary artery disease.
- Predictors for manual support during R-PCI procedures are not well-established.
Purpose of the Study:
- To investigate predictors of manual support during R-PCI procedures.
- To identify factors associated with increased need for manual assistance in R-PCI.
Main Methods:
- A multi-center study analyzed patient-level data from R-PCI procedures conducted between 2020 and 2022.
- Manual support was defined as partial manual assistance or manual conversion during R-PCI.
- A multivariable model was developed using Akaike information criteria to predict manual support.
Main Results:
- Manual support was required in 19.9% of 231 R-PCI procedures across 210 patients.
- Procedures needing manual support had longer procedural times, increased contrast use, and longer fluoroscopy times.
- Predictors included left anterior descending artery lesions, aorto-ostial lesions, chronic total occlusions, true bifurcations, and severe calcification.
Conclusions:
- Manual support is needed in approximately one in five R-PCI procedures, correlating with extended procedural durations.
- Complex coronary lesion characteristics are significant predictors for requiring manual assistance.
- Findings indicate current R-PCI platform limitations and the necessity for technological advancements to manage complex cases.
