Cumulative sum analysis for experiences of a single-session retrograde intrarenal stone surgery and analysis of predictors for stone-free status.
BACKGROUND
This study investigated the learning curve of a single-session retrograde intrarenal surgery (RIRS) in patients with mid-sized stones. Competence and trainee proficiency for RIRS was assessed using cumulative sum analysis (CUSUM).


METHODS
The study design and the use of patients' information stored in the hospital database were approved by the Institutional Review Board of our institution. A retrospective review was performed for 100 patients who underwent a single-session RIRS. Patients were included if the main stone had a maximal diameter between 10 and 30 mm. The presence of a residual stone was checked on postoperative day 1 and at one-month follow-up visit. Fragmentation efficacy was calculated "removed stone volume (mm(3)) divided by operative time (min)". CUSUM analysis was used for monitoring change in fragmentation efficacy, and we tested whether or not acceptable surgical outcomes were achieved.


RESULTS
The mean age was 54.7±14.8 years. Serum creatinine level did not change significantly. Estimated GFR and hemoglobin were within normal limits postoperatively. The CUSUM curve tended to be flat until the 25th case and showed a rising pattern but declined again until the 56th case. After that point, the fragmentation efficacy reached a plateau. The acceptable level of fragmentation efficacy was 25 ml/min. Multivariate logistic regression analyses showed that stone-free rate was significantly lower for cases with multiple stones than those with a single stone (OR = 0.147, CI 0.032 - 0.674, P value  = 0.005) and for cases with higher number of sites (OR = 0.676, CI 0.517 - 0.882, P value  = 0.004).


CONCLUSIONS
The statistical analysis of RIRS learning experience revealed that 56 cases were required for reaching a plateau in the learning curve. The number of stones and the number of sites were significant predictors for stone-free status.
Department of Urology, Seoul Metropolitan Government- Seoul National University Boramae Medical Center, Seoul, Korea.
Sung Yong
SY
Cho
Department of Urology, Seoul National University Hospital, Seoul, Korea.
Min Soo
MS
Choo
Department of Urology, Seoul National University Hospital, Seoul, Korea.
Jae Hyun
JH
Jung
Department of Urology, Seoul National University Hospital, Seoul, Korea.
Chang Wook
CW
Jeong
Department of Biostatistics, Seoul Metropolitan Government- Seoul National University Boramae Medical Center, Seoul, Korea.
Sohee
S
Oh
Department of Urology, Seoul Metropolitan Government- Seoul National University Boramae Medical Center, Seoul, Korea.
Seung Bae
SB
Lee
Department of Urology, Seoul Metropolitan Government- Seoul National University Boramae Medical Center, Seoul, Korea.
Hwancheol
H
Son
Department of Urology, Seoul Metropolitan Government- Seoul National University Boramae Medical Center, Seoul, Korea.
Hyeon
H
Jeong
D001185:Artificial Intelligence; D005260:Female; D006801:Humans; D007669:Kidney Calculi / Q000601:surgery*; D008297:Male; D008875:Middle Aged; D012189:Retrospective Studies; D013223:Statistics as Topic / Q000379:methods*; D013997:Time Factors; D016896:Treatment Outcome