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Innovations (Philadelphia, Pa.) · 2024

Development and Validation of a Low-Cost, High-Fidelity Simulation Model for Robotic Internal Mammary Artery Harvest Using the da Vinci Xi Robot.

СтатьяВалидационное исследованиеНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
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Ashraf Syed Faaz, Seese Laura, Hasan Irsa S, Babu Ashok N, Balkhy Husam H, Kiaii Bob B, Guy T Sloane, Kaczorowski David J, Bonatti Johannes

Аннотация

OBJECTIVE: We created and validated a low-cost simulation model for robotic internal mammary artery (IMA) takedown.

METHODS: The simulation model utilized a calf fetus thorax cavity stented open internally and secured to a table. The simulation model was validated at a 2-day robotic cardiac surgery workshop. Each participant harvested one IMA using the da Vinci Xi robot (Intuitive Surgical, Sunnyvale, CA, USA). We compared participant self-reported confidence at robotic IMA harvest before and after using the simulator.

RESULTS: Our novel thorax-securing strategy resulted in a stable structure and allowed access to both IMAs from the same 3 ports. The cost to set up the first simulation model was $176 and $133 for every subsequent model. Fifty participants used the simulation model: 42 cardiothoracic surgery attendings and 8 fellows or residents. The feedback form response rate was 78% (n = 39). On the Likert scale, participants rated realism of the calf model to simulate robotic IMA harvesting (0 = not realistic, 10 = highly realistic) with a median of 8 out of 10 (interquartile range [IQR] 7 to 9). Participant confidence (0 = not at all confident, 10 = very confident) in robotic IMA harvesting before and after using the simulator increased (P = 0.001) from a median of 5 (IQR 1 to 7) to 9 (IQR 7 to 10).

CONCLUSIONS: This robotic IMA harvest simulation model is affordable, realistic, and improved participant confidence in robotic IMA harvest. It may provide a valuable training tool for surgeons learning robotic coronary bypass surgery and allows for training frequency necessary to pass basic learning curves.

Предметные рубрики MeSH

ИП Урсолов Владлен Сергеевич

ИНН 500118482352 · ОГРНИП 314500117700049

Реквизиты и контакты