The Learning Curve for Retroperitoneoscopic Donor Nephrectomy

The prospectively collected data of 172 consecutive kidney donors undergoing pure RDN for transplantation at Ondokuz Mayıs University from January 2010 to July 2019 were evaluated retrospectively. Tc-99m mercaptoacetyltriglycine (MAG3) renal scintigraphy was performed to evaluate for split renal function. Three-dimensional computed tomography was performed to evaluate kidney anatomy and vasculature. The final decision for the laterality of surgery was based on the conviction that the better kidney remains with the donor. If both kidneys have equal functional characteristics, the left kidney or the kidney with simpler vascular anatomy was procured based on the transplantation medical review board's decision. The demographic data including age, gender, body mass index (BMI), preoperative renal function, number of renal arteries and veins were recorded. Perioperative variables such as operation time (OT), warm ischemia time (WIT), estimated blood loss (EBL), perioperative and postoperative complications were also recorded. Furthermore, the outcomes of renal recipients including graft function, complications and follow-up were recorded as well. In our previously published article provides a thorough description of the surgical procedure and details for the right and left sides of pure RDN. All RNDs were performed by a single surgeon with experience in retroperitoneoscopic laparoscopic surgery and kidney transplantation. Learning curve analysis The CUSUM analysis and BinSeg (Binary Segmentation) method Although the CUSUM analysis was first built to evaluate industrial sector performance, it has been adopted in medicine to interpret many surgical techniques learning curve. In a timeline, it calculates the sum of the existing differences between individual operation times and the mean of all operation times. Its main use of in medicine is that calculates the sum of the existing differences between individual operation times and the mean of all operation times in a timeline. The next step is to identify significant change points to determine the development. The "changepoint" package was used in the R program to determine the change points. The changepoints were determined according to the mean variation. BinSeg methods were used to identify two exchange points. Penalty methods were not used to determine surgical time change points. The "Normal" method was used as the test statistic for the surgical time. According to the learning curve analysis methods that are mentioned above, the whole cohort was divided into three groups. Inspired by the Dreyfus model of skill acquisition in the naming of the groups. Thus, group 1 represents competence, group 2 is proficiency level, and group 3 is the expert level.