Training Modalities in Robot-assisted Urologic Surgery: A Systematic Review

Catherine Elizabeth Lovegrove, Oussama Elhage, M Shamim Khan, Giacomo Novara, Alex Mottrie, Prokar Dasgupta, Kamran Ahmed

Research output: Contribution to journalReview articlepeer-review

19 Citations (Scopus)

Abstract

CONTEXT: Novel surgical techniques demand that surgical training adapts to the need for technical and nontechnical skills.

OBJECTIVE: To identify training methods available for robot-assisted surgical (RAS) training in urology, evaluate their effectiveness in terms of validation, educational impact, acceptability, and cost effectiveness, and assess their effect on learning curves (LCs).

EVIDENCE ACQUISITION: A systematic review following Preferred Reporting Items for Systematic Reviews and Meta-analysis guidelines searched Ovid Medline, Embase, PsycINFO, and the Cochrane Library. Results were screened to include appropriate studies. Quality was evaluated. Each method was evaluated, and conclusions were drawn regarding LCs.

EVIDENCE SYNTHESIS: Of 359 records, 24 were included (521 participants). Training methods included dry-lab training (n=7), wet-lab training (n=7), mentored training (n=7), and nonstructured pathways (n=5). Dry-lab training demonstrated educational impact by reducing console time and was acceptable in a study; 100% of participants confirmed face validity. Wet-lab training principally uses human cadaveric material; effectiveness is well rated, although dry-lab training and observation were rated as equally useful. Mentored programmes combine lectures, tutorials, observation, simulation, and proctoring. Minifellowships were linked to greater practice of RAS 1 yr later. LCs vary according to experience. One study found that surgeons from robot-related fellowships demonstrated fewer positive surgical margins than surgeons from laparoscopic-related fellowships (24% vs 34.6%; p=0.05) and reduced time (132 vs 152min; p=0.0003). Five studies examined nonstructured training pathways (clinical practice). Experience correlated with fewer complications (p=0.007), improved continence (p=0.049), and reduced time (p=0.002).

CONCLUSIONS: RAS training methods include dry and wet lab, mentored training, and nonstructured pathways. Limited available evidence suggests that they affect LCs differently and are rarely used alone. The different methods of training appear effective when combined. Their benefits must be explored to facilitate validated acceptable training with educational impact.

PATIENT SUMMARY: Robot-assisted training encompasses several methods used in combination, but more evidence is required to gain the greatest benefit and formulate future training pathways.

Original languageEnglish
Pages (from-to)102-116
Number of pages15
JournalEuropean Urology Focus
Volume3
Issue number1
DOIs
Publication statusPublished - Feb 2017

Keywords

  • Dry lab
  • Mentorship
  • Robot-assisted surgery
  • Simulation
  • Training
  • Wet lab

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