Robotic performance metrics model fellow proficiency in living donor nephrectomy

Area of Science:

• Surgical Education
• Robotic Surgery
• Transplant Surgery

Background:

• Robot-assisted donor nephrectomy (RDN) is a complex procedure requiring specialized training.
• Objective performance metrics (OPM) are increasingly used to assess surgical skill acquisition.
• Standardizing training benchmarks for RDN is crucial for patient safety and surgical competence.

Purpose of the Study:

• To evaluate the utility of robotic objective performance metrics (OPM) in predicting the number of cases required for abdominal transplant fellows to achieve proficiency and independence in robot-assisted donor nephrectomy (RDN).
• To establish benchmarks for surgical training in RDN based on objective performance data.

Main Methods:

• Analysis of 101 RDN cases performed by 5 transplant fellows between September 2020 and October 2023.
• Utilized OPM including fellow percent active control time (%ACT) and handoff counts (HC).
• Defined proficiency as %ACT ≥ 80% and HC ≤ 2; independence as %ACT ≥ 99% and HC ≤ 1.

Main Results:

• Case number significantly correlated with increased fellow %ACT (proficiency at 14 cases, independence at 32 cases; R²=0.56, p<0.001).
• Case number significantly correlated with decreased HC (proficiency at 18 cases, independence at 33 cases; R²=0.29, p<0.001).
• Total active console time was not associated with case number (p=0.91), indicating sustained operative efficiency.

Conclusions:

• Abdominal transplant fellows achieve proficiency in RDN at approximately 14-18 cases and independence at 32-33 cases.
• OPM like %ACT and HC effectively predict learning curves in RDN.
• These findings provide valuable benchmarks for independent and safe RDN training for fellows.