World Journal of Surgery

, Volume 36, Issue 8, pp 1723–1731 | Cite as

Evidence-Based Surgery: Barriers, Solutions, and the Role of Evidence Synthesis

  • George Garas
  • Amel Ibrahim
  • Hutan Ashrafian
  • Kamran Ahmed
  • Vanash Patel
  • Koji Okabayashi
  • Petros Skapinakis
  • Ara Darzi
  • Thanos Athanasiou



Surgery is a rapidly evolving field, making the rigorous testing of emerging innovations vital. However, most surgical research fails to employ randomized controlled trials (RCTs) and has particularly been based on low-quality study designs. Subsequently, the analysis of data through meta-analysis and evidence synthesis is particularly difficult.


Through a systematic review of the literature, this article explores the barriers to achieving a strong evidence base in surgery and offers potential solutions to overcome the barriers.


Many barriers exist to evidence-based surgical research. They include enabling factors, such as funding, time, infrastructure, patient preference, ethical issues, and additionally barriers associated with specific attributes related to researchers, methodologies, or interventions. Novel evidence synthesis techniques in surgery are discussed, including graphics synthesis, treatment networks, and network meta-analyses that help overcome many of the limitations associated with existing techniques. They offer the opportunity to assess gaps and quantitatively present inconsistencies within the existing evidence of RCTs.


Poorly or inadequately performed RCTs and meta-analyses can give rise to incorrect results and thus fail to inform clinical practice or revise policy. The above barriers can be overcome by providing academic leadership and good organizational support to ensure that adequate personnel, resources, and funding are allocated to the researcher. Training in research methodology and data interpretation can ensure that trials are conducted correctly and evidence is adequately synthesized and disseminated. The ultimate goal of overcoming the barriers to evidence-based surgery includes the improved quality of patient care in addition to enhanced patient outcomes.


  1. 1.
    Centre for Evidence Based Medicine (2011) Levels of evidence. University of Oxford, OxfordGoogle Scholar
  2. 2.
    Solomon MJ, McLeod RS (1993) Clinical studies in surgical journals–have we improved? Dis Colon Rectum 36:43–48PubMedCrossRefGoogle Scholar
  3. 3.
    Ng TT, McGory ML, Ko CY et al (2006) Meta-analysis in surgery: methods and limitations. Arch Surg 141:1125–1130PubMedCrossRefGoogle Scholar
  4. 4.
    Solomon MJ, McLeod RS (1995) Should we be performing more randomized controlled trials evaluating surgical operations? Surgery 118:459–467PubMedCrossRefGoogle Scholar
  5. 5.
    Ashrafian H, Darzi A, Athanasiou T (2011) Evidence synthesis: evolving methodologies to optimise patient care and enhance policy decisions. In: Evidence synthesis in healthcare, Springer, London, pp 1–46Google Scholar
  6. 6.
    Jansen JP, Crawford B, Bergman G et al (2008) Bayesian meta-analysis of multiple treatment comparisons: an introduction to mixed treatment comparisons. Value Health 11:956–964PubMedCrossRefGoogle Scholar
  7. 7.
    Voils C, Hassselblad V, Crandell J et al (2009) A Bayesian method for the synthesis of evidence from qualitative and quantitative reports: the example of antiretroviral medication adherence. J Health Serv Res Policy 14:226–233PubMedCrossRefGoogle Scholar
  8. 8.
    Ashrafian H, Sevdalis N, Athanasiou T (2010) Evidence-based surgery. In: Key topics in surgical research and methodology. Springer, London, pp 9–26Google Scholar
  9. 9.
    Altman DG (1996) Better reporting of randomised controlled trials: the CONSORT statement. BMJ 313:570–571PubMedCrossRefGoogle Scholar
  10. 10.
    Moher D, Liberati A, Tetzlaff J et al (2009) Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. BMJ 339:b2535PubMedCrossRefGoogle Scholar
  11. 11.
    Atkins D, Best D, Briss P et al (2004) Grading quality of evidence and strength of recommendations. BMJ 328:1490PubMedCrossRefGoogle Scholar
  12. 12.
    Atkins D, Briss PA, Eccles M et al (2005) Systems for grading the quality of evidence and the strength of recommendations. II. Pilot study of a new system. BMC Health Serv Res 5:25PubMedCrossRefGoogle Scholar
  13. 13.
    Rebitzer JB, Rege M, Shepard C (2008) Influence, information overload, and information technology in health care. Adv Health Econ Health Serv Res 19:43–69PubMedCrossRefGoogle Scholar
  14. 14.
    Revere D, Turner AM, Madhavan A et al (2007) Understanding the information needs of public health practitioners: a literature review to inform design of an interactive digital knowledge management system. J Biomed Inform 40:410–421PubMedCrossRefGoogle Scholar
  15. 15.
    Darzi A (2008) Quality care for all: NHS next stage review final report. Department of Health, LondonGoogle Scholar
  16. 16.
    Walker E, Hernandez AV, Kattan MW (2008) Meta-analysis: its strengths and limitations. Clevel Clin J Med 75:431–439CrossRefGoogle Scholar
  17. 17.
    Jadad AR, Moore RA, Carroll D et al (1996) Assessing the quality of reports of randomized clinical trials: is blinding necessary? Control Clin Trials 17:1–12PubMedCrossRefGoogle Scholar
  18. 18.
    Murtuza B, Pepper JR, Jones C et al (2010) Does stentless aortic valve implantation increase perioperative risk? A critical appraisal of the literature and risk of bias analysis. Eur J Cardiothorac Surg 39:643–652PubMedCrossRefGoogle Scholar
  19. 19.
    Ioannidis JP (2009) Integration of evidence from multiple meta-analyses: a primer on umbrella reviews, treatment networks and multiple treatments meta-analyses. CMAJ 181:488–493PubMedCrossRefGoogle Scholar
  20. 20.
    Ioannidis JP (2006) Indirect comparisons: the mesh and mess of clinical trials. Lancet 368:1470–1472PubMedCrossRefGoogle Scholar
  21. 21.
    Salanti G, Higgins JP, Ades AE et al (2008) Evaluation of networks of randomized trials. Stat Methods Med Res 17:279–301PubMedCrossRefGoogle Scholar
  22. 22.
    Song F, Loke YK, Walsh T et al (2009) Methodological problems in the use of indirect comparisons for evaluating healthcare interventions: survey of published systematic reviews. BMJ 338:b1147PubMedCrossRefGoogle Scholar
  23. 23.
    Mauri D, Polyzos NP, Salanti G et al (2008) Multiple-treatments meta-analysis of chemotherapy and targeted therapies in advanced breast cancer. J Natl Cancer Inst 100:1780–1791PubMedCrossRefGoogle Scholar
  24. 24.
    Caldwell DM, Ades AE, Higgins JP (2005) Simultaneous comparison of multiple treatments: combining direct and indirect evidence. BMJ 331:897–900PubMedCrossRefGoogle Scholar
  25. 25.
    Salanti G, Dias S, Welton NJ et al (2010) Evaluating novel agent effects in multiple-treatments meta-regression. Stat Med 29:2369–2383PubMedGoogle Scholar
  26. 26.
    Diener MK, Simon T, Buchler MW et al (2011) Surgical evaluation and knowledge transfer-methods of clinical research in surgery. Langenbecks Arch Surg. doi:10.1007/s00423-011-0775-x
  27. 27.
    Beger HG, Schwarz A (1998) Clinical research in surgery: questions but few answers. Langenbecks Arch Surg 383:300–305PubMedCrossRefGoogle Scholar
  28. 28.
    Concato J, Shah N, Horwitz RI (2000) Randomized, controlled trials, observational studies, and the hierarchy of research designs. N Engl J Med 342:1887–1892PubMedCrossRefGoogle Scholar
  29. 29.
    Cook JA (2009) The challenges faced in the design, conduct and analysis of surgical randomised controlled trials. Trials 10:9PubMedCrossRefGoogle Scholar
  30. 30.
    McCulloch P, Taylor I, Sasako M et al (2002) Randomised trials in surgery: problems and possible solutions. BMJ 324:1448–1451PubMedCrossRefGoogle Scholar
  31. 31.
    Paradis C (2008) Bias in surgical research. Ann Surg 248:180–188PubMedCrossRefGoogle Scholar
  32. 32.
    Prescott RJ, Counsell CE, Gillespie WJ et al (1999) Factors that limit the quality, number and progress of randomised controlled trials. Health Technol Assess 3:1–143PubMedGoogle Scholar
  33. 33.
    Lowrance WT, Tarin TV, Shariat SF (2010) Evidence-based comparison of robotic and open radical prostatectomy. Sci World J 10:2228–2237CrossRefGoogle Scholar
  34. 34.
    Lotan Y (2010) Economics of robotics in urology. Curr Opin Urol 20:92–97PubMedCrossRefGoogle Scholar
  35. 35.
    Binder J, Kramer W (2001) Robotically-assisted laparoscopic radical prostatectomy. BJU Int 87:408–410PubMedCrossRefGoogle Scholar
  36. 36.
    Menon M, Shrivastava A, Tewari A et al (2002) Laparoscopic and robot assisted radical prostatectomy: establishment of a structured program and preliminary analysis of outcomes. J Urol 168:945–999PubMedCrossRefGoogle Scholar
  37. 37.
    Skolarus TA, Zhang Y, Hollenbeck BK (2010) Robotic surgery in urologic oncology: gathering the evidence. Expert Rev Pharmacoecon Outcomes Res 10:421–432PubMedCrossRefGoogle Scholar
  38. 38.
    Steinberg PL, Ghavamian R (2011) Searching robotic prostatectomy online: what information is available? Urology 77:941–945PubMedCrossRefGoogle Scholar
  39. 39.
    Alkhateeb S, Lawrentschuk N (2011) Consumerism and its impact on robotic-assisted radical prostatectomy. BJU Int 108:1874–1878PubMedCrossRefGoogle Scholar
  40. 40.
    Brandina R, Berger A, Kamoi K et al (2009) Critical appraisal of robotic-assisted radical prostatectomy. Curr Opin Urol 19:290–296PubMedCrossRefGoogle Scholar
  41. 41.
    Lotan Y, Cadeddu JA, Gettman MT (2004) The new economics of radical prostatectomy: cost comparison of open, laparoscopic and robot assisted techniques. J Urol 172:1431–1435PubMedCrossRefGoogle Scholar

Copyright information

© Société Internationale de Chirurgie 2012

Authors and Affiliations

  • George Garas
    • 1
  • Amel Ibrahim
    • 1
  • Hutan Ashrafian
    • 1
  • Kamran Ahmed
    • 1
  • Vanash Patel
    • 1
  • Koji Okabayashi
    • 1
  • Petros Skapinakis
    • 1
  • Ara Darzi
    • 1
  • Thanos Athanasiou
    • 1
  1. 1.Department of Surgery and CancerImperial College LondonLondonUK

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