Automated performance metrics may predict outcomes during robotic partial nephrectomy

A recent study has shown that expert surgeons are more efficient and focused in their movement during robot-assisted partial nephrectomy. In addition, automated performance metrics during key steps are associated with objective measures of tumour complexity and may predict clinical outcomes.

In this study, a system data recorder was used to record automated performance metrics (instrument motion tracking/system events) and synchronized surgical videos from da Vinci Si systems during robot-assisted partial nephrectomy. Each case was divided into seven steps: colon mobilization, ureteral identification/dissection, hilar dissection, exposure of tumour within Gerota’s fascia, intraoperative ultrasound/tumour scoring, tumour excision, and renorrhaphy.

The authors then compared the automated performance metrics from each step between expert (≥150 cases) and trainee (<150 cases) surgeons via Mann-Whitney U test (continuous variables) and Pearson’s chi-squared test (categorical variables).

Finally, clinical outcomes were collected prospectively and correlated to automated performance metrics and R.E.N.A.L. (radius, exophytic/endophytic, nearness of tumour to collecting system, anterior/posterior, location relative to polar line) nephrometry score by Spearman’s correlation coefficients (r).

Fifty robot-assisted partial nephrectomy cases, performed by seven expert and 10 trainee surgeons, were eligible for analysis. Automated performance metric profiles were significantly different between experts and novices in the initial five steps (p<0.05). In particular, experts demonstrated faster dominant instrument movement and greater dominant instrument usage (bimanual dexterity) than trainees in select steps (p≤0.045).

Automated performance metrics during tumour excision and renorrhaphy, including those related to instrument efficiency, task duration, and dominant instrument use, significantly correlated with R.E.N.A.L. score (r≥0.364; p≤0.041).

“These data help establish a standardized metric for surgeon assessment and training during robotic assisted partial nephrectomy,” the authors said.