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Learning curve of robot-assisted laparoscopic sacrocolpo(recto)pexy: a cumulative sum analysis

      Background

      Determination of the learning curve of new techniques is essential to improve safety and efficiency. Limited information is available regarding learning curves in robot-assisted laparoscopic pelvic floor surgery.

      Objective

      The purpose of this study was to assess the learning curve in robot-assisted laparoscopic pelvic floor surgery.

      Study Design

      We conducted a prospective cohort study. Consecutive patients who underwent robot-assisted laparoscopic sacrocolpopexy or sacrocolporectopexy were included (n=372). Patients were treated in a teaching hospital with a tertiary referral function for gynecologic/multicompartment prolapse. Procedures were performed by 2 experienced conventional laparoscopic surgeons (surgeons A and B). Baseline demographics were scored per groups of 25 consecutive patients. The primary outcome was the determination of proficiency, which was based on intraoperative complications. Cumulative sum control chart analysis allowed us to detect small shifts in a surgeon’s performance. Proficiency was obtained when the first acceptable boundary line of cumulative sum control chart analysis was crossed. Secondary outcomes that were examined were shortening and/or stabilization of surgery time (measured with the use of cumulative sum control chart analysis and the moving average method).

      Results

      Surgeon A performed 242 surgeries; surgeon B performed 137 surgeries (n=7 surgeries were performed by both surgeons). Intraoperative complications occurred in 1.9% of the procedures. The learning curve never fell below the unacceptable failure limits and stabilized after 23 of 41 cases. Proficiency was obtained after 78 cases for both surgeons. Surgery time decreased after 24–29 cases in robot-assisted sacrocolpopexy (no distinct pattern for robot-assisted sacrocolporectopexy). Limitations were the inclusion of 2 interventions and concomitant procedures, which limited homogeneity. Furthermore, analyses treated all complications in cumulative sum as equal weight, although there are differences in the clinical relevance of complications.

      Conclusion

      After 78 cases, proficiency was obtained. After 24–29 cases, surgery time stabilized for robot-assisted sacrocolpopexy. In this age of rapidly changing surgical techniques, it can be difficult to determine the learning curve of each procedure. Cumulative sum control chart analysis can assist with this determination and prove to be a valuable tool. Training programs could be individualized to improve both surgical performance and patient benefits.

      Key words

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      References

        • Carroll A.W.
        • Lamb E.
        • Hill A.J.
        • Gill E.J.
        • Matthews C.A.
        Surgical management of apical pelvic support defects: the impact of robotic technology.
        Int Urogynecol J. 2012; 23: 1183-1186
        • Hoekstra A.V.
        • Morgan J.M.
        • Lurain J.R.
        • et al.
        Robotic surgery in gynecologic oncology: impact on fellowship training.
        Gynecol Oncol. 2009; 114: 168-172
        • Steiner S.H.
        • Cook R.J.
        Monitoring surgical performance using risk-adjusted cumulative sum charts.
        Biostatistics. 2000; 1: 441-452
        • Bolsin S.
        • Colson M.
        The use of the CUSUM technique in the assessment of trainee competence in new procedures.
        Int J Qual Health Care. 2000; 12: 433-438
        • Van Zanten F.
        • Brem C.
        • Lenters E.
        • Broeders I.A.M.J.
        • Schraffordt Koops S.E.
        Sexual function after robot-assisted prolapse surgery: a prospective study.
        Int Urogynecol J. 2018; 29: 905-912
        • Van Iersel J.J.
        • de Witte C.J.
        • Verheijen P.M.
        • et al.
        Robot-assisted sacrocolporectopexy for multicompartment prolapse of the pelvic floor: a prospective cohort study evaluating functional and sexual outcome.
        Dis Colon Rectum. 2016; 59: 968-974
        • Morena Sierra J.
        • Ortiz Oshiro E.
        • Fernandeze Pérez C.
        • et al.
        Long-term outcomes after robotic sacrocolpopexy for vaginal prolapse: prospective analysis.
        Urol Int. 2011; 86: 414-418
        • Paraiso M.F.R.
        • Jelovsek J.E.
        • Frick A.
        • Chen C.C.G.
        • Barber M.D.
        Laparoscopic compared with robotic sacrocolpopexy for vaginal prolapse: a randomized controlled trial.
        Obstet Gynecol. 2011; 118: 1005-1013
        • Matthews C.A.
        • Carroll A.
        • Hill A.
        • Ramakrishnan V.
        • Gill E.J.
        Prospective evaluation of surgical outcomes of robot-assisted sacrocolpopexy and sacrocervicopexy for the management of apical pelvic support defects.
        South Med J. 2012; 105: 274-278
        • Seror J.
        • Yates D.R.
        • Seringe E.
        • et al.
        Prospective comparison of short-term functional outcomes obtained after pure laparoscopic and robot-assisted laparoscopic sacrocolpopexy.
        World J Urol. 2012; 30: 393-398
        • Culligan P.J.
        • Gurshumov E.
        • Lewis C.
        • et al.
        Subjective and objective results 1 year after robotic sacrocolpopexy using a lightweight Y-mesh.
        Int Urogynecol J. 2014; 25: 731-735
        • Woelk J.L.
        • Casiano E.R.
        • Weaver A.L.
        • Gostout B.S.
        • Trabuco E.C.
        • Gebhart J.B.
        The learning curve of robotic hysterectomy.
        Obstet Gynecol. 2013; 121: 87-95
        • Satava R.M.
        Identification and reduction of surgical error using simulation.
        Minim Invasive Ther Technol. 2005; 14: 257-261
        • Swift S.
        • Morris S.
        • McKinnie V.
        • et al.
        Validation of a simplified technique for using the POPQ pelvic organ prolapse classification system.
        Int Urogynecol J. 2006; 17: 615-620
        • Nezhat F.R.
        • Apostol R.
        • Nezhat C.
        • Pejovic T.
        New insights in the pathophysiology of ovarian cancer and implications for screening and prevention.
        Am J Obstet Gynecol. 2015; 213: 262-267
        • Rimbach S.
        • Schempershofe M.
        In-bag morcellation as a routine for laparoscopic hysterectomy.
        BioMed Res Int. 2017; 2017: 6701916
        • Tan-Kim J.
        • Hartzell K.A.
        • Reinsch C.S.
        • et al.
        Uterine sarcomas and parasitic myomas after laparoscopic hysterectomy with power morcellation.
        Am J Obstet Gynecol. 2015; 212: 594.e1-594.e10
        • Carter-Brooks C.M.
        • Du A.L.
        • Bonidie M.J.
        • Shepherd J.P.
        The impact of a dedicated robotic team on robotic-assisted sacrocolpopexy outcomes.
        Female Pelvic Med Reconstr Surg. 2018; 24: 13-16
        • Akl M.N.
        • Long J.B.
        • Giles D.L.
        • et al.
        Robotic-assisted sacrocolpopexy: technique and learning curve.
        Surg Endosc. 2009; 23: 2390-2394
        • Awad N.
        • Mustafa S.
        • Amit A.
        • Deutsch M.
        • Eldor-Itskovitz J.
        • Lowenstein L.
        Implementation of a new procedure: laparoscopic versus robotic sacrocolpopexy.
        Arch Gynecol Obstet. 2013; 287: 1181-1186
        • Linder B.J.
        • Anand M.
        • Weaver A.L.
        • et al.
        Assessing the learning curve of robotic sacrocolpopexy.
        Int Urogynaecol J. 2016; 27: 239-246
        • Myers E.M.
        • Geller E.J.
        • Connolly A.
        • Bowling J.M.
        • Matthews C.A.
        Robotic sacrocolpopexy performance and cumulative summation analysis.
        Female Pelvic Med Reconstr Surg. 2014; 20: 83-86
        • Sharma S.
        • Calixte R.
        • Finamore P.S.
        Establishing the learning curve of robotic sacral colpopexy in a start-up robotics program.
        J Minim Invasive Gynecol. 2016; 23: 89-93
        • Serati M.
        • Bogani G.
        • Sorice P.
        • et al.
        Robot-assisted sacrocolpopexy for pelvic organ prolapse: a systematic review and meta-analysis of comparative studies.
        Eur Urol. 2014; 66: 303-318
        • Claerhout F.
        • Roovers J.P.
        • Lewi P.
        • Verguts J.
        • De Ridder D.
        • Deprest J.
        Implementation of laparoscopic sacrocolpopexy-a single centre’s experience.
        Int Urogynecol J. 2009; 20: 1119-1125
        • Vandendriessche D.
        • Giraudet G.
        • Lucot J.P.
        • Behal H.
        • Cosson M.
        Impact of laparoscopic sacrocolpopexy learning curve on operative time, perioperative complications and short term results.
        Eur J Obstet Gynecol Reprod Biol. 2015; 191: 84-89
        • Akladios C.Y.
        • Dautun D.
        • Saussine C.
        • Baldauf J.J.
        • Mathelin C.
        • Wattiez A.
        Laparoscopic sacrocolpopexy for female genital organ prolapse: establishment of a learning curve.
        Eur J Obstet Gynecol Reprod Biol. 2010; 149: 218-221
        • Claerhout F.
        • Verguts J.
        • Werbrouck E.
        • Veldman J.
        • Lewi P.
        • Deprest J.
        Analysis of the learning process for laparoscopic sacrocolpopexy: identification of challenging steps.
        Int Urogynecol J. 2014; 25: 1185-1191
        • Moawad G.
        • Tyan P.
        • Corpodean F.
        • Robinson J.
        Ethical considerations arising from surgeon caseload volume in benign gynecologic surgery.
        J Minim Invasive Gynecol. 2018; 25: 749-751
        • Sinha R.
        • Sanjay M.
        • Rupa B.
        • Kumari S.
        Robotic surgery in gynecology.
        J Min Access Surg. 2015; 11: 50-59