Assessing the learning curve of single-port robot-assisted prostatectomy.

Abstract

In an era of rapid technology development in surgery, urologists remain at the forefront of surgical innovation. Incorporating new technology into routine clinical practice requires analysis of both safety and efficacy. Although healthcare research often relies heavily on randomised trials, limitations of randomised trials in surgical research are well known [1]. In the early stages of surgical innovation, outcomes are influenced by the surgeon’s learning curve (LC). Critical LC evaluation provides a nuanced understanding of early outcomes, as well as providing valuable information of what is required to achieve proficiency. In this issue of the BJUI, Lenfant et al. [2] describe the LC of single-port (SP) robot-assisted extraperitoneal prostatectomy (SP-EPP) using the cumulative sum (CUSUM) method. Over a 19-month study period 150 consecutive cases were performed by a single surgeon (Kaouk). Anticipated outcomes were defined from a reference multiport (MP) cohort. Acceptable levels of complications were reached after ˜30 cases. Expected operative time based on the MP cohort was 168 min, whereas the mean observed SP-EPP operative time was 190 min with improvement occurring in four phases of 30–40 cases each. Beyond ˜120 cases, the surgeon reached a mastery level, where the study-defined trifecta outcome (surgical margin status, postoperative complications, and biochemical recurrence) threshold was reached. Learning curves can be considered to have three components [3]. First is the ‘starting point’ and the second is the ‘slope’, which reflects how fast a person learns a new task. In the Lenfant et al. [2] paper, the study surgeon Kaouk, began his SP learning curve with a baseline experience of >2000 MP robot-assisted radical prostatectomies (RARPs). In addition, he undertook cadaveric and dry laboratory training. Together, these factors would undoubtably have affected his starting point, as well as the slope of the LC, contributing to a relatively short journey in transitioning to safe and proficient SP surgery. Consequently, the authors sensibly acknowledge the potential limitations of generalising their findings to novice surgeons. The third component to the LC is the ‘plateau’. This is where incremental change in the measured outcome is no longer significant. However, a key point here is that reaching a plateau does not necessarily equate to competence or safe performance [4]. Analysis of the surgical LC is complex. The surgical learning environment is prone to bias. Risk-adjustment models that could remove the noise from confounding factors such as patient-to-patient variation and operating room team composition would be immensely useful [4, 5]. It may be possible to remove the effect of variation by using some type of regression analysis, but this is uncommonly done. Ideal LC assessment would include multivariate analysis accounting for patient and surgical team factors [3]. Outcomes or endpoints also need to be selected carefully as few variables will assess true competency [4]. Generally, it is recommended that parallel analyses of several variables should be undertaken [5]. Operative time is often used as a surrogate for performance; however, faster procedures do not always imply better outcomes and may reflect the proficiency of the surgical team rather than individual ability. Lovegrove et al. [6] in their paper on a structured and modular training pathway for RARP, took a unique approach to LC assessment, assessing technical skill as an independent variable. Using healthcare failure mode effect analysis (HFMEA), to breakdown the surgical procedure into a series of critical steps, they were able to analyse the LC for each sub-step separately. As surgeons, we know that for each procedure there will be aspects that are more challenging than others. In keeping with this, the LCs for each of the sub-steps in the Lovegrove et al. [6] study varied. Manual assessment of performance of so many discrete steps is time consuming. However, the large volume of data generated in the sub-steps of robotic surgery lends itself to machine learning and we are beginning to see its application in robotic surgical education. Surgeons are regularly presented with new learning opportunities. We must acquaint ourselves with methods for LC analysis to inform quality assurance projects and training. The LC literature is much more extensive in the industrial space and there is little overlap with surgical LC [5]. The Woodall et al. [5] recent critique of the use of CUSUM methods with surgical LC data suggests that industrial LC models have not been applied to full advantage in surgical applications. Ongoing work with LC analysis and integration of machine learning will be an important part of future innovation in robotic surgery. The authors have no disclosures of interest.