Systematic Review of Learning Curves for Minimally Invasive Abdominal Surgery: A Review of the Methodology of Data Collection, Depiction of Outcomes, and Statistical Analysis : Annals of Surgery

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Meta-Analysis

Systematic Review of Learning Curves for Minimally Invasive Abdominal Surgery

A Review of the Methodology of Data Collection, Depiction of Outcomes, and Statistical Analysis

Harrysson, Iliana J. MD*; Cook, Jonathan PhD; Sirimanna, Pramudith MBBS; Feldman, Liane S. MD§; Darzi, Ara MD, FMedSci, FRCS, FRCSI, FRCSEd, FRCPSG, FACS, FCGI, FRCPE, FRCP; Aggarwal, Rajesh MD, PhD, FRCS

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Annals of Surgery 260(1):p 37-45, July 2014. | DOI: 10.1097/SLA.0000000000000596

Objective: 

To determine how minimally invasive surgical learning curves are assessed and define an ideal framework for this assessment.

Background: 

Learning curves have implications for training and adoption of new procedures and devices. In 2000, a review of the learning curve literature was done by Ramsay et al and it called for improved reporting and statistical evaluation of learning curves. Since then, a body of literature is emerging on learning curves but the presentation and analysis vary.

Methods: 

A systematic search was performed of MEDLINE, EMBASE, ISI Web of Science, ERIC, and the Cochrane Library from 1985 to August 2012. The inclusion criteria are minimally invasive abdominal surgery formally analyzing the learning curve and English language. 592 (11.1%) of the identified studies met the selection criteria.

Results: 

Time is the most commonly used proxy for the learning curve (508, 86%). Intraoperative outcomes were used in 316 (53%) of the articles, postoperative outcomes in 306 (52%), technical skills in 102 (17%), and patient-oriented outcomes in 38 (6%) articles. Over time, there was evidence of an increase in the relative amount of laparoscopic and robotic studies (P < 0.001) without statistical evidence of a change in the complexity of analysis (P = 0.121).

Conclusions: 

Assessment of learning curves is needed to inform surgical training and evaluate new clinical procedures. An ideal analysis would account for the degree of complexity of individual cases and the inherent differences between surgeons. There is no single proxy that best represents the success of surgery, and hence multiple outcomes should be collected.

© 2014 by Lippincott Williams & Wilkins.

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