title: Application of the statistical process control method for prospective patient safety monitoring during the learning phase: robotic kidney transplantation with regional hypothermia (IDEAL phase 2a-b).
abstract: BACKGROUND
  Traditional evaluation of the learning curve (LC) of an operation has been retrospective. Furthermore, LC analysis does not permit patient safety monitoring.
  
  
  OBJECTIVE
  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).
  
  
  METHODS
  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).
  
  
  METHODS
  We employed prospective monitoring using SPC techniques, including cumulative summation (CUSUM) and Shewhart control charts, to perform LC analysis and patient safety monitoring, respectively.
  
  
  METHODS
  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.
  
  
  CONCLUSIONS
  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.
  
  
  RESULTS
  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.affiliation: Vattikuti Urology Institute, Henry Ford Hospital, Detroit, MI, USA. Electronic address: asood1@hfhs.org.
authors.forename: Akshay
authors.initials: A
authors.lastname: Sood
authors.affiliation: Vattikuti Urology Institute, Henry Ford Hospital, Detroit, MI, USA.
authors.forename: Khurshid R
authors.initials: KR
authors.lastname: Ghani
authors.affiliation: Kidney and Urology Institute, Medanta-The Medicity, Gurgaon, India.
authors.forename: Rajesh
authors.initials: R
authors.lastname: Ahlawat
authors.affiliation: Department of Urology, H.L. Trivedi Institute of Transplantation Sciences, Ahmedabad, India.
authors.forename: Pranjal
authors.initials: P
authors.lastname: Modi
authors.affiliation: Department of Urology, Ohio State University, Columbus, OH, USA.
authors.forename: Ronney
authors.initials: R
authors.lastname: Abaza
authors.affiliation: Vattikuti Urology Institute, Henry Ford Hospital, Detroit, MI, USA.
authors.forename: Wooju
authors.initials: W
authors.lastname: Jeong
authors.affiliation: Vattikuti Urology Institute, Henry Ford Hospital, Detroit, MI, USA.
authors.forename: Jesse D
authors.initials: JD
authors.lastname: Sammon
authors.affiliation: Vattikuti Urology Institute, Henry Ford Hospital, Detroit, MI, USA.
authors.forename: Mireya
authors.initials: M
authors.lastname: Diaz
authors.affiliation: Kidney and Urology Institute, Medanta-The Medicity, Gurgaon, India.
authors.forename: Vijay
authors.initials: V
authors.lastname: Kher
authors.affiliation: Vattikuti Urology Institute, Henry Ford Hospital, Detroit, MI, USA.
authors.forename: Mani
authors.initials: M
authors.lastname: Menon
authors.affiliation: Vattikuti Urology Institute, Henry Ford Hospital, Detroit, MI, USA.
authors.forename: Mahendra
authors.initials: M
authors.lastname: Bhandari
mesh_terms: D000293:Adolescent; D000328:Adult; D000714:Anastomosis, Surgical / Q000592:standards / Q000706:statistics & numerical data; D050377:Cold Ischemia / Q000592:standards / Q000706:statistics & numerical data; D003404:Creatinine / Q000097:blood; D005260:Female; D005919:Glomerular Filtration Rate; D006801:Humans; D007036:Hypothermia, Induced / Q000592:standards; D007668:Kidney / Q000503:physiopathology; D007676:Kidney Failure, Chronic / Q000601:surgery; D016030:Kidney Transplantation / Q000379:methods / Q000592:standards* / Q000706:statistics & numerical data; D059032:Learning Curve; D008297:Male; D008875:Middle Aged; D010043:Outcome and Process Assessment, Health Care / Q000706:statistics & numerical data*; D061214:Patient Safety / Q000706:statistics & numerical data*; D011446:Prospective Studies; D011786:Quality Control; D065287:Robotic Surgical Procedures / Q000193:education / Q000592:standards* / Q000706:statistics & numerical data; D013223:Statistics as Topic / Q000379:methods*; D013996:Time and Motion Studies; D055815:Young Adult
chemical_list: D003404:Creatinine
keywords: CUSUM; Control charts; Kidney transplantation; Learning curve; Patient safety monitoring; Robotics; Statistical process control