Semin Plast Surg 2014; 28(01): 005-010
DOI: 10.1055/s-0034-1368161
Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

Robotic Microsurgical Training and Evaluation

Jesse C. Selber
1   Department of Plastic Surgery, University of Texas, MD Anderson Cancer Center, Houston, Texas
,
Taiba Alrasheed
1   Department of Plastic Surgery, University of Texas, MD Anderson Cancer Center, Houston, Texas
› Author Affiliations
Further Information

Publication History

Publication Date:
07 March 2014 (online)

Abstract

Robotic surgery has expanded rapidly over the past two decades and is in widespread use among the surgical subspecialties. Clinical applications in plastic surgery have emerged gradually over the last few years. One of the promising applications is robotic-assisted microvascular anastomosis. Here the authors first describe a process by which an assessment instrument they developed called the Structured Assessment of Robotic Microsurgical Skills (SARMS) was validated. The instrument combines the previously validated Structured Assessment of Microsurgical Skills (SAMS) with other skill domains in robotic surgery. Interrater reliability for the SARMS instrument was excellent for all skill areas among four expert, blinded evaluators. They then present a process by which the learning curve for robotic-assisted microvascular anastomoses was measured and plotted. Ten study participants performed five robotic microanastomoses each that were recorded, deidentified and scored. Trends in SARMS scores were plotted. All skill areas and overall performance improved significantly for each participant over the five microanastomotic sessions, and operative time decreased for all participants. The results showed an initial steep ascent in technical skill acquisition followed by more gradual improvement, and a steady decrease in operative times for the cohort. Participants at all levels of training, ranging from minimal microsurgical experience to expert microsurgeons gained proficiency over the course of five robotic sessions.

 
  • References

  • 1 Liverneaux PA, Berner SH, Bednar MS , et al. Telemicrosurgery: robot assisted microsurgery. Paris, France: Springer; 2013
  • 2 Balasundaram I, Aggarwal R, Darzi LA. Development of a training curriculum for microsurgery. Br J Oral Maxillofac Surg 2010; 48 (8) 598-606
  • 3 Temple CL, Ross DC. A new, validated instrument to evaluate competency in microsurgery: the University of Western Ontario Microsurgical Skills Acquisition/Assessment instrument [outcomes article]. Plast Reconstr Surg 2011; 127 (1) 215-222
  • 4 Chan W, Niranjan N, Ramakrishnan V. Structured assessment of microsurgery skills in the clinical setting. J Plast Reconstr Aesthet Surg 2010; 63 (8) 1329-1334
  • 5 Chan WY, Matteucci P, Southern SJ. Validation of microsurgical models in microsurgery training and competence: a review. Microsurgery 2007; 27 (5) 494-499
  • 6 Selber JC, Chang EI, Liu J , et al. Tracking the learning curve in microsurgical skill acquisition. Plast Reconstr Surg 2012; 130 (4) 550e-557e
  • 7 Liverneaux P, Nectoux E, Taleb C. The future of robotics in hand surgery. Chir Main 2009; 28 (5) 278-285
  • 8 Dulan G, Rege RV, Hogg DC , et al. Developing a comprehensive, proficiency-based training program for robotic surgery. Surgery 2012; 152 (3) 477-488
  • 9 Cronbach LJ. Coefficient alpha and the internal structure of tests. Psychometrika 1951; 16: 297-334
  • 10 Tavakoli AS , et al. Calculating-multi-rater observation agreement in health care research using the SAS Kappa Statistic. Paper presented at: SAS Global Forum 2012; April 22–25, 2012; Orlando, FL
  • 11 Karamanoukian RL, Bui T, McConnell MP, Evans GR, Karamanoukian HL. Transfer of training in robotic-assisted microvascular surgery. Ann Plast Surg 2006; 57 (6) 662-665
  • 12 Hernandez JD, Bann SD, Munz Y , et al. Qualitative and quantitative analysis of the learning curve of a simulated surgical task on the da Vinci system. Surg Endosc 2004; 18 (3) 372-378
  • 13 Darzi A, Smith S, Taffinder N. Assessing operative skill. Needs to become more objective. BMJ 1999; 318 (7188) 887-888
  • 14 Lee JY, Matter T, Parisi TJ, Carlsen BT, Bishop AT, Shin AY. Learning curve of robotic-assisted microvascular anastomosis in the rat. J Reconstr Microsurg 2012; 28 (7) 451-456
  • 15 Seamon LG, Cohn DE, Richardson DL , et al. Robotic hysterectomy and pelvic-aortic lymphadenectomy for endometrial cancer. Obstet Gynecol 2008; 112 (6) 1207-1213