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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

doi: 10.1097/SLA.0000000000000596
Meta-Analysis

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.

Supplemental Digital Content is Available in the Text.The authors reviewed learning curve literature since 1985 and concluded that in an ideal analysis of the learning curve, multiple outcomes should be collected. In addition, an analysis should account for the degree of complexity of individual cases and the inherent differences between surgeons.

*Department of Pediatrics, Stanford University, Stanford, CA

Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK

Department of Surgery & Cancer, Imperial College London, London, UK

§Department of Surgery, McGill University, Montreal, Canada

Department of Surgery, University of Pennsylvania, Philadelphia, PA.

Reprints: Iliana J. Harrysson, MD, Stanford University, Lucile Packard Children's Hospital, 725 Welch Road, Palo Alto, CA 94304. E-mail: iliana.harrysson@stanford.edu.

Disclosure: This study was supported in part by the Department of Surgery at Imperial College London, the NIHR CSA grant (R.A.; CS-2009-001), and Medical Scholars funding from Stanford University. The authors do not have any conflict of interest.

Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's Web site (www.annalsofsurgery.com).

© 2014 by Lippincott Williams & Wilkins.