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Clinical Research and Evidence-Based Medicine

  • Dennis V. CokkinosEmail author
Chapter

Abstract

Clinical practice has in the last 25 years been guided by evidence-based medicine. This approach advocates the use of various levels of evidence regarding the source of medical data and classification of indications to offer guideline recommendations on how to treat patients.

The main sources are the result of randomized controlled clinical trials but also systematic reviews. Evidence-based medicine is complemented by personalized medicine, which tailors medical treatment to the individual patient, and precision medicine, which combines larger databases, aided by artificial intelligence and big data, to acquire the means to more efficiently and rapidly process the explosively increasing knowledge.

Keywords

Evidence-Based Medicine Guidelines Levels of Evidence Personalized Medicine Precision Medicine Artificial Intelligence Big Data 

References

  1. 1.
    Sackett DL, Rosenberg WM, Gray JA, Haynes RB, Richardson WS. Evidence based medicine: what it is and what it isn’t. BMJ. 1996;312:71–2.CrossRefPubMedGoogle Scholar
  2. 2.
    Tenny S, Gossman WG. Evidence based medicine (EBM). Treasure Island: StatPearls Publishing, 2018.Google Scholar
  3. 3.
    Masic I, Miokovic M, Muhamedagic B. Evidence based medicine—new approaches and challenges. Acta Inform Med. 2008;16:219–25.CrossRefPubMedGoogle Scholar
  4. 4.
    Farquhar C. Evidence-based medicine—the promise, the reality. Aust N Z J Obstet Gynaecol. 2018;58:17–21.CrossRefGoogle Scholar
  5. 5.
    Guyatt G, Cairns J, Churchill D, et al. Evidence-based medicine: a new approach to teaching the practice of medicine. JAMA. 1992;268:2420–5.CrossRefGoogle Scholar
  6. 6.
    Sackett DL, Rosenberg WM. Evidence based medicine: what it is and what it isn’t. BMJ. 1996;13:71–2.CrossRefGoogle Scholar
  7. 7.
    Guyatt G, Rennie D, editors. Users’ guides to the medical literature: a manual for evidence-based clinical practice. Chicago: American Medical Association; 2002.Google Scholar
  8. 8.
    Burns PB, Rohrich RJ, Chung KC. The levels of evidence and their role in evidence-based medicine. Plast Reconstr Surg. 2011;128:305–10.CrossRefPubMedGoogle Scholar
  9. 9.
    The periodic health examination. Canadian Task Force on the Periodic Health Examination. Can Med Assoc J. 1979;121:1193–254.Google Scholar
  10. 10.
    Sackett DL. Rules of evidence and clinical recommendations on the use of antithrombotic agents. Chest. 1989;95:2S–4S.CrossRefGoogle Scholar
  11. 11.
    Robert Lawrence, US Preventive Services Task Force Edition. Guide to clinical preventive services. Darby: Diane; 1989. ISBN 1568062974. Retrieved 9 Dec 2014.Google Scholar
  12. 12.
    US Preventive Services Task Force. Guide to clinical preventive services: report of the US Preventive Services Task Force. Darby: Diane; 1989. p. 24. ISBN 978-1-56806-297-6. Appendix A.Google Scholar
  13. 13.
    Centre for Evidence-Based Medicine. Oxford Centre for Evidence-Based Medicine—levels of evidence (March 2009). https://www.cebm.net/2009/06/oxford-centre-evidence-based-medicine-levels-evidence-march-2009/. Accessed 25 Mar 2015.
  14. 14.
  15. 15.
    Sackett DL. Evidence-based medicine. Semin Perinatol. 1997;21:3–5.CrossRefGoogle Scholar
  16. 16.
    Dunning J, Prendergast B, Mackway-Jones K. Towards evidence-based medicine in cardiothoracic surgery: best BETS. Interact Cardiovasc Thorac Surg. 2003;2:405–9.CrossRefGoogle Scholar
  17. 17.
    Scanlon PJ, Faxon DP, Audet AM, Carabello B, Dehmer GJ, Eagle KA, et al. ACC/AHA guidelines for coronary angiography: executive summary and recommendations. A report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines (Committee on Coronary Angiography) developed in collaboration with the Society for Cardiac Angiography and Interventions. Circulation. 1999;99:2345–57.CrossRefGoogle Scholar
  18. 18.
    Amsterdam EA, Wenger NK, Brindis RG, et al. 2014 AHA/ACC guideline for the management of patients with non-ST-elevation acute coronary syndromes: executive summary. A report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Circulation. 2014;130:2354–94.CrossRefGoogle Scholar
  19. 19.
    Ibanez B, et al. 2017 ESC guidelines for the management of acute myocardial infarction in patients presenting with ST-segment elevation: the Task Force for the Management of Acute Myocardial Infarction in Patients Presenting with ST-Segment Elevation of the European Society of Cardiology (ESC). Eur Heart J. 2018;39(2):119–77.CrossRefPubMedGoogle Scholar
  20. 20.
    Djulbegovic B, Guyatt GH. Progress in evidence-based medicine: a quarter century on. Lancet. 2017;390:415–23.CrossRefGoogle Scholar
  21. 21.
    Djulbegovic B, Guyatt GH, Ashcroft RE. Epistemologic inquiries in evidence-based medicine. Cancer Control. 2009;16:158–68.CrossRefGoogle Scholar
  22. 22.
    Atkins D, Best D, Briss PA, Eccles M, Falck-Ytter Y, Flottorp S, et al. Grading quality of evidence and strength of recommendations. BMJ. 2004;328:1490.CrossRefGoogle Scholar
  23. 23.
    PROSPERO. International registry of systematic reviews. http://www.crd.york.ac.uk/prospero. Accessed 21 Apr 2016.
  24. 24.
    Chalmers I. The Cochrane collaboration: preparing, maintaining, and disseminating systematic reviews of the effects of health care. Ann N Y Acad Sci. 1993;703:156–63; discussion 163–5.CrossRefGoogle Scholar
  25. 25.
    Wenneberg J. Which rate is right? N Engl J Med. 1986;314:310–1.CrossRefGoogle Scholar
  26. 26.
    Institute of Medicine (US) Committee to Advise the Public Health Service on Clinical Practice Guidelines. In: Field MJ, Lohr KN, editors. Clinical practice guidelines: directions for a new agency. Washington, DC: National Academy Press; 1990.Google Scholar
  27. 27.
    Atkins D, Eccles M, Flottorp S, et al. Systems for grading the quality of evidence and the strength of recommendations I: critical appraisal of existing approaches. The GRADE Working Group. BMC Health Serv Res. 2004;4:38.CrossRefPubMedGoogle Scholar
  28. 28.
    GRADE Working Group. List of GRADE Working Group publications and grants. 2016. http://www.gradeworkinggroup.org. Accessed 21 Aug 2016.
  29. 29.
    Djulbegovic B. A framework to bridge the gaps between evidence-based medicine, health outcomes, and improvement and implementation science. J Oncol Pract. 2014;10:200–2.CrossRefGoogle Scholar
  30. 30.
    Appelt KC, Milch KF, Handgraaf MJJ, Weber EU. The decision making individual deferences inventory and guidelines for the study of individual deferences in judgment and decision-making research. Judgm Decis Mak. 2011;6:252–62.Google Scholar
  31. 31.
    Montori VM, Guyatt GH. Progress in evidence-based medicine. JAMA. 2008;300:1814–6.CrossRefGoogle Scholar
  32. 32.
    Greenhalgh T, Howick J, Maskrey N. Evidence based medicine: a movement in crisis? BMJ. 2014;348:g3725.CrossRefPubMedGoogle Scholar
  33. 33.
    Sun X, Briel M, Walter SD, Guyatt GH. Is a subgroup effect believable? Updating criteria to evaluate the credibility of subgroup analyses. BMJ. 2010;340:c117.CrossRefGoogle Scholar
  34. 34.
    Sheridan DJ, Julian DG. Achievements and limitations of evidence-based medicine. J Am Coll Cardiol. 2016;68:204–13.CrossRefGoogle Scholar
  35. 35.
    Dahm P, Kunz R, Schünemann H. Evidence-based clinical practice guidelines for prostate cancer: the need for a unified approach. Curr Opin Urol. 2007;17:200–7.CrossRefGoogle Scholar
  36. 36.
    Royal College of Physicians. Doctors in society: medical professionalism in a changing world. Report of a working party of the Royal College of Physicians of London. 2005. https://cdn.shopify.com/s/files/1/0924/4392/files/doctors_in_society_reportweb.pdf?15745311214883953343. Accessed 4 May 2016.
  37. 37.
    Guyatt GH. Evidence-based medicine. ACP J Club. 1991;114:A16.Google Scholar
  38. 38.
    Lee PY, Alexander KP, Hammill BG, Pasquali SK, Peterson ED. Representation of elderly persons and women in published randomized trials of acute coronary syndromes. JAMA. 2001;286:708–13.CrossRefGoogle Scholar
  39. 39.
    Jadad AR, To MJ, Emara M, Jones J. Consideration of multiple chronic diseases in randomized controlled trials. JAMA. 2011;306:2670–2.CrossRefGoogle Scholar
  40. 40.
    MIAMI Trial Research Group. Long-term prognosis after early intervention with metoprolol in suspected acute myocardial infarction: experiences from the MIAMI Trial. J Intern Med. 1991;230:233–7.CrossRefGoogle Scholar
  41. 41.
    Roncaglioni MC, Collaborative Group of the Primary Prevention Project (PPP). Low-dose aspirin and vitamin E in people at cardiovascular risk: a randomised trial in general practice. Lancet. 2001;357:89–95.CrossRefGoogle Scholar
  42. 42.
    Reveiz L, Chapman E, Asial S, Munoz S, Bonfill X, Alonso-Coello P. Risk of bias of randomized trials over time. J Clin Epidemiol. 2015;68:1036–45.CrossRefGoogle Scholar
  43. 43.
    Maggioni AP, Latini R, Tognoni G, et al. Pain relief, general management and other adjunctive treatments. In: Yusuf S, Cairns JA, Camm AJ, et al., editors. Evidence based cardiology. 2nd ed. London: BMJ; 2003. p. 482.Google Scholar
  44. 44.
    Freemantle N, Cleland J, Young P, et al. B blockade after myocardial infarction: systematic review and meta regression analysis. BMJ. 1999;318:1730–7.CrossRefPubMedGoogle Scholar
  45. 45.
    Black N. A national strategy for research and development: lessons from England. Annu Rev Public Health. 1997;18:485–505.CrossRefGoogle Scholar
  46. 46.
    Schünemann HJ, Moja L. Reviews: rapid! rapid! rapid!…and systematic. Syst Rev. 2015;4:1–3.CrossRefGoogle Scholar
  47. 47.
    Sim I. Two ways of knowing: big data and evidence-based medicine. Ann Intern Med. 2016;164:562–3.CrossRefGoogle Scholar
  48. 48.
    Eddy DM. Evidence-based medicine: a unified approach. Health Aff (Millwood). 2005;24:9–17.CrossRefGoogle Scholar
  49. 49.
    National Research Council. Washington, toward precision medicine: building a knowledge network for biomedical research and a new taxonomy of disease. Washington, DC: National Academies Press, 2011.Google Scholar
  50. 50.
    Collins FS, Varmus H. A new initiative on precision medicine. N Engl J Med. 2015;372:793–5.CrossRefPubMedGoogle Scholar
  51. 51.
    Collins FS, Green ED, Guttmacher AE, Guyer MS, US National Human Genome Research Institute. A vision for the future of genomics research. Nature. 2003;422:835–47.CrossRefGoogle Scholar
  52. 52.
    Gibson WM. Can personalized medicine survive? Can Fam Physician. 1971;17:29–88.PubMedCentralPubMedGoogle Scholar
  53. 53.
    Giardino A, Gupta S, Olson E, Sepulveda K, Lenchik L, Ivanidze J, et al. Role of imaging in the era of precision medicine. Acad Radiol. 2017;24:639–49.CrossRefGoogle Scholar
  54. 54.
    Jameson JL, Longo DL. Precision medicine—personalized, problematic, and promising. N Engl J Med. 2015;372:2229–34.CrossRefGoogle Scholar
  55. 55.
    Hayden EC. Technology: the $1,000 genome. Nature. 2014;507(7492):294–5.CrossRefGoogle Scholar
  56. 56.
    Hunt S, Jha S. Can precision medicine reduce overdiagnosis? Acad Radiol. 2015;22:1040–1.CrossRefGoogle Scholar
  57. 57.
    Katsnelson A. Momentum grows to make ‘personalized’ medicine more ‘precise’. Nature Med. 2013;19:249.CrossRefGoogle Scholar
  58. 58.
    Krittanawong C, Zhang H, Wang Z, Aydar M, Kitai T. Artificial intelligence in precision cardiovascular medicine. J Am Coll Cardiol. 2017;69:2657–64.CrossRefGoogle Scholar
  59. 59.
    Pitt GS. Cardiovascular precision medicine: hope or hype? Eur Heart J. 2015;36:1842–23.CrossRefGoogle Scholar
  60. 60.
    Joyner MJ. Precision medicine, cardiovascular disease and hunting elephants. Prog Cardiovasc Dis. 2016;58:651–60.CrossRefGoogle Scholar
  61. 61.
    Lord J, Willis S, Eatock J, Tappenden P, Trapero-Bertran M, Miners A, et al. Economic modelling of diagnostic and treatment pathways in National Institute for Health and Care Excellence clinical guidelines: the Modelling Algorithm Pathways in Guidelines (MAPGuide) project. Health Technol Assess. 2013;17(58):v–vi, 1–192.CrossRefGoogle Scholar
  62. 62.
    Burgers LT, Redekop WK, Severens JL. Challenges in modelling the cost effectiveness of various interventions for cardiovascular disease. Pharmacoeconomics. 2014;32:627–37.CrossRefGoogle Scholar
  63. 63.
    Antman EM, Loscalzo J. Precision medicine in cardiology. Nat Rev Cardiol. 2016;13:591–602.CrossRefGoogle Scholar
  64. 64.
    Lenfant C. Prospects of personalized medicine in cardiovascular diseases. Metabolism. 2013;62(Suppl 1):S6–10.CrossRefGoogle Scholar
  65. 65.
    O’Donnell CJ, Nabel EG. Genomics of cardiovascular disease. N Engl J Med. 2011;365:2098–109.CrossRefGoogle Scholar
  66. 66.
    Guttmacher AE, Collins FS. Genomic medicine—a primer. N Engl J Med. 2002;347:1512–20.CrossRefGoogle Scholar
  67. 67.
    Favalli V, Serio A, Giuliani LP, Arbustini E. ‘Precision and personalized medicine,’ a dream that comes true? J Cardiovasc Med (Hagerstown). 2017;18(Suppl 1):e1–6.CrossRefGoogle Scholar
  68. 68.
    Savoia C, Volpe M, Grassi G, Borghi C, Agabiti Rosei E, Touyz RM. Personalized medicine—a modern approach for the diagnosis and management of hypertension. Clin Sci (Lond). 2017;131:2671–85.CrossRefGoogle Scholar
  69. 69.
    Kashyap PC, Chia N, Nelson H, Segal E, Elinav E. Microbiome at the frontier of personalized medicine. Mayo Clin Proc. 2017;92:1855–64.CrossRefPubMedGoogle Scholar
  70. 70.
    DeVries M, Fenchel M, Fogarty RE, Kim BD, Timmons D, White AN. Name it! Store it! Protect it! A systems approach to managing data in research core facilities. J Biomol Tech. 2017;28:137–41.CrossRefPubMedGoogle Scholar
  71. 71.
    Bland JS, Minich DM, Eck BM. A systems medicine approach: translating emerging science into individualized wellness. Adv Med. 2017;2017:1718957.CrossRefPubMedGoogle Scholar
  72. 72.
    Barry EL, Peacock JL, Rees JR, Bostick RM, Robertson DJ, Bresalier RS, et al. Vitamin D receptor genotype, vitamin D3 supplementation, and risk of colorectal adenomas: a randomized clinical trial. JAMA Oncol. 2017;3:628–35.CrossRefPubMedGoogle Scholar
  73. 73.
    Huang T, Qi Q, Li Y, Hu FB, Bray GA, Sacks FM, et al. FTO genotype, dietary protein, and change in appetite: the Preventing Overweight Using Novel Dietary Strategies trial. Am J Clin Nutr. 2014;99:1126–30.CrossRefPubMedGoogle Scholar
  74. 74.
    Gibbons C, Bailador Del Pozo G, Andrés J, Lobstein T, Manco M, Lewy H, et al. Data-as-a-service platform for delivering healthy lifestyle and preventive medicine: concept and structure of the DAPHNE project. JMIR Res Protoc. 2016;5:e222.CrossRefPubMedGoogle Scholar
  75. 75.
    Hood L, Lovejoy JC, Price ND. Integrating big data and actionable health coaching to optimize wellness. BMC Med. 2015;13:4.CrossRefPubMedGoogle Scholar
  76. 76.
    Price ND, Magis AT, Earls JC, Glusman G, Levy R, Lausted C, et al. A wellness study of 108 individuals using personal, dense, dynamic data clouds. Nat Biotechnol. 2017;35:747–56.CrossRefPubMedGoogle Scholar
  77. 77.
    McCue ME, McCoy AM. The scope of big data in one medicine: unprecedented opportunities and challenges. Front Vet Sci. 2017;4:194.CrossRefPubMedGoogle Scholar
  78. 78.
    Miller DD, Brown EW. Artificial intelligence in medical practice: the question to the answer? Am J Med. 2018;131:129–33.CrossRefGoogle Scholar
  79. 79.
    Wang D, Khosa A, Gargeya R, Irshad H, Beck AH. Deep learning for identifying breast cancer. Proceedings of the International Society on Biomedical Imaging. 2016.Google Scholar
  80. 80.
    Krittanawong C. The rise of artificial intelligence and the uncertain future for physicians. Eur J Intern Med. 2018;48:e13–4.CrossRefGoogle Scholar
  81. 81.
    Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism. 2017;69S:S36–40.CrossRefGoogle Scholar
  82. 82.
    Hamburg MA, Collins FS. The path to personalized medicine. N Engl J Med. 2010;363:301–4.CrossRefGoogle Scholar
  83. 83.
    Bonderman D. Artificial intelligence in cardiology. Wien Klin Wochenschr. 2017;129:866–8.CrossRefPubMedGoogle Scholar
  84. 84.
    Johnson KW, Torres Soto J, Glicksberg BS, Shameer K, Miotto R, Ali M, et al. Artificial intelligence in cardiology. J Am Coll Cardiol. 2018;71:2668–79.CrossRefGoogle Scholar
  85. 85.
    Hemingway H, Asselbergs FW, Danesh J, Dobson R, Maniadakis N, Maggioni A, et al. Big data from electronic health records for early and late translational cardiovascular research: challenges and potential. Eur Heart J. 2018;39:1481–95.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Heart and Vessel DepartmentBiomedical Research Foundation, Academy of Athens - Gregory SkalkeasAthensGreece

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