Akshay Sood1, Khurshid R Ghani2, Rajesh Ahlawat3, Pranjal Modi4, Ronney Abaza5, Wooju Jeong2, Jesse D Sammon2, Mireya Diaz2, Vijay Kher3, Mani Menon2, Mahendra Bhandari2. 1. Vattikuti Urology Institute, Henry Ford Hospital, Detroit, MI, USA. Electronic address: asood1@hfhs.org. 2. Vattikuti Urology Institute, Henry Ford Hospital, Detroit, MI, USA. 3. Kidney and Urology Institute, Medanta-The Medicity, Gurgaon, India. 4. Department of Urology, H.L. Trivedi Institute of Transplantation Sciences, Ahmedabad, India. 5. Department of Urology, Ohio State University, Columbus, OH, USA.
Abstract
BACKGROUND: Traditional evaluation of the learning curve (LC) of an operation has been retrospective. Furthermore, LC analysis does not permit patient safety monitoring. OBJECTIVES: To prospectively monitor patient safety during the learning phase of robotic kidney transplantation (RKT) and determine when it could be considered learned using the techniques of statistical process control (SPC). DESIGN, SETTING AND PARTICIPANTS: From January through May 2013, 41 patients with end-stage renal disease underwent RKT with regional hypothermia at one of two tertiary referral centers adopting RKT. Transplant recipients were classified into three groups based on the robotic training and kidney transplant experience of the surgeons: group 1, robot trained with limited kidney transplant experience (n=7); group 2, robot trained and kidney transplant experienced (n=20); and group 3, kidney transplant experienced with limited robot training (n=14). INTERVENTION: We employed prospective monitoring using SPC techniques, including cumulative summation (CUSUM) and Shewhart control charts, to perform LC analysis and patient safety monitoring, respectively. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Outcomes assessed included post-transplant graft function and measures of surgical process (anastomotic and ischemic times). CUSUM and Shewhart control charts are time trend analytic techniques that allow comparative assessment of outcomes following a new intervention (RKT) relative to those achieved with established techniques (open kidney transplant; target value) in a prospective fashion. RESULTS AND LIMITATIONS: CUSUM analysis revealed an initial learning phase for group 3, whereas groups 1 and 2 had no to minimal learning time. The learning phase for group 3 varied depending on the parameter assessed. Shewhart control charts demonstrated no compromise in functional outcomes for groups 1 and 2. Graft function was compromised in one patient in group 3 (p<0.05) secondary to reasons unrelated to RKT. In multivariable analysis, robot training was significantly associated with improved task-completion times (p<0.01). Graft function was not adversely affected by either the lack of robotic training (p=0.22) or kidney transplant experience (p=0.72). CONCLUSIONS: The LC and patient safety of a new surgical technique can be assessed prospectively using CUSUM and Shewhart control chart analytic techniques. These methods allow determination of the duration of mentorship and identification of adverse events in a timely manner. A new operation can be considered learned when outcomes achieved with the new intervention are at par with outcomes following established techniques. PATIENT SUMMARY: Statistical process control techniques allowed for robust, objective, and prospective monitoring of robotic kidney transplantation and can similarly be applied to other new interventions during the introduction and adoption phase.
BACKGROUND: Traditional evaluation of the learning curve (LC) of an operation has been retrospective. Furthermore, LC analysis does not permit patient safety monitoring. OBJECTIVES: To prospectively monitor patient safety during the learning phase of robotic kidney transplantation (RKT) and determine when it could be considered learned using the techniques of statistical process control (SPC). DESIGN, SETTING AND PARTICIPANTS: From January through May 2013, 41 patients with end-stage renal disease underwent RKT with regional hypothermia at one of two tertiary referral centers adopting RKT. Transplant recipients were classified into three groups based on the robotic training and kidney transplant experience of the surgeons: group 1, robot trained with limited kidney transplant experience (n=7); group 2, robot trained and kidney transplant experienced (n=20); and group 3, kidney transplant experienced with limited robot training (n=14). INTERVENTION: We employed prospective monitoring using SPC techniques, including cumulative summation (CUSUM) and Shewhart control charts, to perform LC analysis and patient safety monitoring, respectively. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Outcomes assessed included post-transplant graft function and measures of surgical process (anastomotic and ischemic times). CUSUM and Shewhart control charts are time trend analytic techniques that allow comparative assessment of outcomes following a new intervention (RKT) relative to those achieved with established techniques (open kidney transplant; target value) in a prospective fashion. RESULTS AND LIMITATIONS: CUSUM analysis revealed an initial learning phase for group 3, whereas groups 1 and 2 had no to minimal learning time. The learning phase for group 3 varied depending on the parameter assessed. Shewhart control charts demonstrated no compromise in functional outcomes for groups 1 and 2. Graft function was compromised in one patient in group 3 (p<0.05) secondary to reasons unrelated to RKT. In multivariable analysis, robot training was significantly associated with improved task-completion times (p<0.01). Graft function was not adversely affected by either the lack of robotic training (p=0.22) or kidney transplant experience (p=0.72). CONCLUSIONS: The LC and patient safety of a new surgical technique can be assessed prospectively using CUSUM and Shewhart control chart analytic techniques. These methods allow determination of the duration of mentorship and identification of adverse events in a timely manner. A new operation can be considered learned when outcomes achieved with the new intervention are at par with outcomes following established techniques. PATIENT SUMMARY: Statistical process control techniques allowed for robust, objective, and prospective monitoring of robotic kidney transplantation and can similarly be applied to other new interventions during the introduction and adoption phase.
Authors: Nedim Ruhotina; Julien Dagenais; Giorgio Gandaglia; Akshay Sood; Firas Abdollah; Steven L Chang; Jeffrey J Leow; Kola Olugbade; Arun Rai; Jesse D Sammon; Marianne Schmid; Briony Varda; Kevin C Zorn; Mani Menon; Adam S Kibel; Quoc-Dien Trinh Journal: Can Urol Assoc J Date: 2014-09 Impact factor: 1.862
Authors: Alberto Breda; Angelo Territo; Lluis Gausa; Oscar Rodríguez-Faba; Jorge Caffaratti; Javier Ponce de León; Lluis Guirado; Carme Facundo; Marco Guazzieri; Andrea Guttilla; Humberto Villavicencio Journal: World J Urol Date: 2017-02-22 Impact factor: 4.226
Authors: Vejay N Vakharia; Roman Rodionov; Andrew W McEvoy; Anna Miserocchi; Rachel Sparks; Aidan G O'Keeffe; Sebastien Ourselin; John S Duncan Journal: J Neurosurg Date: 2018-02-16 Impact factor: 5.115