Strengths and Limitations
To our best knowledge, this is the first study presenting a learning
curve analysis during introduction of a novice robotic surgeon in the
gynaecological oncology field. We used RA-CUSUM analysis, which is
considered the reference standard for studying surgical learning
curves29 and has an additional value in its individual
risk adjustment compared to standard CUSUM analysis wherein parameters
are set based on literature. Another strength is that we use oncological
outcomes, i.e. survival, which is considered the foremost relevant
measure of performance when treating cancer. We were able to correlate
these oncological outcomes to the introduction of a novice to an
experienced surgical team.
Several limitations to this study exist. Since we report a
single-institution analysis of a small surgical team, our results might
not be transferrable to other centres. Although the reported learning
curves should be considered institutional learning curves, given the
team effort of robotic procedures, individual aspects within the small
surgical team could contribute to different outcomes in other centres.
Not all surgeons new to robot-assisted surgery will learn at the same
pace. With the current analysis we are not able to present an individual
learning curve for the novice surgeon, as the learning curve fades into
the institutional learning curve. To quantify the performance of a
structured learning curriculum, validation in larger, preferably
multicentre, datasets is warranted.
We expanded our cohort with 61 procedures as we previously reported this
to be the learning phase. Inherently, the follow-up times of the latter
group are shorter and additional recurrences could present in the
upcoming years as not all patients in group 3 have completed the
recommended five years of follow-up yet. However, the majority of
patients in group 3 completed three years of follow-up, the period in
which most recurrences occur. More importantly, Kaplan-Meier curves were
used to correct for differences in follow-up time through censoring.