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Multicenter comparative study of three "non-destructive" methods of detecting the cleanliness of the da Vinci
Aiqin Chen1, Xiumei Zou2, Yanhua Tan3
1Central Sterile Supply Department, Sun Yat-sen University Cancer Center, Guangzhou, China.
Background:
The robotic instrument of the da Vinci surgical system determines the accuracy of robotic-assisted surgery, However, the most effective cleaning method of robotic equipment is a challenge for healthcare professionals. This study compared three da Vinci robot-assisted surgery manipulators to detect the effect of "non-destructive" testing of the cleaning effect by two different methods.
Methods:
The post-surgical cleaning of the da Vinci robotic instruments in the Sun Yat-sen University Cancer Center, The First Affiliated Hospital of Guangzhou Medical University and the Shenzhen Second People's Hospital was performed using two different processes from January 2019 to January 2020: manual joint automatic ultrasonic cleaning and automatic mechanical cleaning. The efficacy of visual estimation, the residual protein assay (quantitative) and adenosine triphosphate (ATP) biological biofluorescence detection of the cleanliness of the mechanical instrument's work (distal working end) surface and the shaft's inner chamber was compared. If the cleaning effect of any position on the surface or inner cavity of the manipulator did not qualify, the entire robotic instrument was judged as disqualified.
Results:
A total of 198 cases of da Vinci robotic instrument postoperative cleanliness data were collected. The qualifying rates of automatic ultrasonic cleaning were 96.97% by visual estimation, 93.94% by residual protein assay and 60.61% by ATP biological fluorescence detection. The respective rates for automatic mechanical cleaning were 100% by visual estimation, 90.91% by residual protein assay and 66.67% by ATP biological fluorescence detection.
Conclusions:
The cleaning of the da Vinci robotic instrument detected by "non-destructive" residual protein assay or ATP biological fluorescence detection is more accurate than visual estimation.
