Robotic-assisted Navigation Bronchoscopy: A Meta-Analysis of Diagnostic Yield and Complications.

BACKGROUND: Robotic-assisted navigation bronchoscopy (RANB) is a novel method to biopsy lung nodules, with initial reports demonstrating excellent accuracy. We aimed to evaluate pooled estimates of diagnostic yields and complication rates with RANB by performing a meta-analysis of the available literature. METHODS: We searched 3 databases, including PubMed, EmBase, and Web of Science. The resulting abstracts were reviewed by 2 investigators. Analyses were performed using random effects models, and diagnostic yield and complication rates were estimated after the Freeman-Tukey transformation. RESULTS: A total of 23 articles, comprising 1409 patients and 1541 nodules, were included in the final analysis. Mean ages ranged from 63.2 to 69.3 years. The average size of the nodules ranged between 5.9 and 25.0 mm. Most patients (54.0% to 92.0%) had a current or prior smoking history in studies that reported them (n=8). The pooled diagnostic yield was 81.9% (12 studies, 838 nodules, 95% CI: 83.4%-91.0%), and the pooled sensitivity for malignancy was 87.6% (8 studies, 699 nodules, 95% CI: 81.3%-89.5%). The pooled incidence of pneumothorax rates was 0.60% (95% CI: 0.11%-1.35%). The pooled incidence of major bleeding was <0.01%. CONCLUSION: Diagnostic yield for patients with pulmonary nodules undergoing RANB is high, though may be impacted by the prevalence of malignancy, participant selection, and publication bias. Complication rates, including pneumothoraces and bleeding rates, appear low across all studies. If RANB is available, clinicians should consider utilizing this platform to biopsy pulmonary nodules.

Safety, efficiency and learning curves in robotic surgery: a human factors analysis.

BACKGROUND: Expense, efficiency of use, learning curves, workflow integration and an increased prevalence of serious incidents can all be barriers to adoption. We explored an observational approach and initial diagnostics to enhance total system performance in robotic surgery. METHODS: Eighty-nine robotic surgical cases were observed in multiple operating rooms using two different surgical robots (the S and Si), across several specialties (Urology, Gynecology, and Cardiac Surgery). The main measures were operative duration and rate of flow disruptions-described as 'deviations from the natural progression of an operation thereby potentially compromising safety or efficiency.' Contextual parameters collected were surgeon experience level and training, type of surgery, the model of robot and patient factors. Observations were conducted across four operative phases (operating room pre-incision; robot docking; main surgical intervention; post-console). RESULTS: A mean of 9.62 flow disruptions per hour (95 % CI 8.78-10.46) were predominantly caused by coordination, communication, equipment and training problems. Operative duration and flow disruption rate varied with surgeon experience (p = 0.039; p < 0.001, respectively), training cases (p = 0.012; p = 0.007) and surgical type (both p < 0.001). Flow disruption rates in some phases were also sensitive to the robot model and patient characteristics. CONCLUSIONS: Flow disruption rate is sensitive to system context and generates improvement diagnostics. Complex surgical robotic equipment increases opportunities for technological failures, increases communication requirements for the whole team, and can reduce the ability to maintain vision in the operative field. These data suggest specific opportunities to reduce the training costs and the learning curve.