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Personalized perioperative medicine: a scoping review of personalized assessment and communication of risk before surgery

  • Emma P. Harris
  • David B. MacDonald
  • Laura Boland
  • Sylvain Boet
  • Manoj M. Lalu
  • Daniel I. McIsaacEmail author
Reports of Original Investigations

Abstract

Background

Personalized medicine aims to improve outcomes through application of therapy directed by individualized risk profiles. Whether personalized risk assessment is routinely applied in practice is unclear; the impact of personalized preoperative risk prediction and communication on outcomes has not been synthesized. Our objective was to perform a scoping review to examine the extent, range, and nature of studies where personalized risk was evaluated preoperatively and communicated to the patient and/or healthcare professional.

Methods

A systematic search was developed, peer-reviewed, and applied to Embase, Medline, CINAHL, and Cochrane databases to identify studies of individuals having or considering surgery, where a process to assess personalized risk was applied and where these estimates were communicated to a patient and/or healthcare professional. All stages of the review were completed in duplicate. We narratively synthesized and described identified themes.

Results

We identified 796 studies; 24 underwent full-text review. Seven studies were included; one communicated personalized risk to patients, four to a healthcare professional, and two to both. Cardiac (n = 2) and orthopedic surgery (n = 2) were the most common surgical specialties. Four studies used electronic risk calculators, and three used paper-based tools. Personalized preoperative risk assessment and communication may improve accuracy of information provided to patients, improve consent processes, and influence length of stay. Methodologic weaknesses in study design were common.

Conclusions

Personalized preoperative risk assessment and communication may improve patient and system outcomes. This evidence is limited, however, by weaknesses in study design. Appropriately powered, low risk of bias evaluation of personalized risk communication before surgery is needed.

La médecine périopératoire personnalisée : une étude de portée sur l’évaluation et la communication des risques personnalisées avant la chirurgie

Résumé

Contexte

La médecine personnalisée a pour objectif d’améliorer les pronostics en appliquant un traitement basé sur des profils de risque individualisés. Nous ne savons pas si l’évaluation du risque personnalisé est fréquemment utilisée dans la pratique; il n’existe pas de synthèse concernant l’impact de la prédiction et de la communication personnalisée du risque préopératoire sur les pronostics. Notre objectif était de réaliser une étude de portée (scoping review) afin d’examiner l’ampleur, l’envergure et la nature des études dans lesquelles le risque personnalisé avait été évalué en préopératoire et communiqué au patient et/ou au professionnel de la santé.

Méthode

Une recherche systématique a été mise au point, révisée par les pairs et appliquée aux bases de données Embase, Medline, CINAHL et Cochrane afin d’identifier les études portant sur des personnes ayant subi ou envisageant de subir une chirurgie et pour lesquelles un processus d’évaluation personnalisée des risques avait été appliqué, et que ces estimations avaient été communiquées au patient et/ou à un professionnel de la santé. Toutes les étapes de révision ont été réalisées en double. Nous avons effectué une synthèse narrative et décrit les thèmes identifiés.

Résultats

Nous avons identifié 796 études; le texte intégral de 24 d’entre elles a été passé en revue. Sept études ont été incluses; le risque personnalisé a été communiqué aux patients dans une étude, à un professionnel de la santé dans quatre études, et aux deux instances dans deux études. Les spécialités chirurgicales les plus courantes étaient la chirurgie cardiaque (n = 2) et orthopédique (n = 2). Quatre études ont utilisé des calculatrices de risque électroniques, et trois se sont appuyées sur des outils sur papier. L’évaluation et la communication personnalisées du risque préopératoire pourraient améliorer la précision des informations présentées aux patients, améliorer les processus de consentement et influencer la durée de séjour. Les faiblesses méthodologiques dans la conception des études étaient fréquentes.

Conclusion

L’évaluation et la communication personnalisées du risque préopératoire pourraient améliorer les pronostics individuels des patients et systémiques. Les données probantes sont toutefois limitées en raison de faiblesses dans la conception des études. Il est nécessaire de procéder à une évaluation de la communication personnalisée du risque avant une chirurgie comportant un faible risque de biais et bénéficiant d’un échantillon adéquat.

Notes

Acknowledgements

We acknowledge the assistance of Ms. Sascha Davis, Learning Services, The Ottawa Hospital, for help with our search strategy and execution. Drs. Boet, Lalu, and McIsaac are supported by The Ottawa Hospital Anaesthesia Alternate Funds Association. Dr. Lalu is supported by the University of Ottawa Scholarship Protected Time Program. Dr. McIsaac is supported by the Canadian Anesthesiologists’ Society Career Scientist Award and the University of Ottawa Junior Clinical Research Chair in Perioperative Health Systems and Outcomes Research.

Conflicts of interest and financial disclosures

None declared.

Editorial responsiblity

This submission was handled by Dr. Steven Backman, Associate Editor, Canadian Journal of Anesthesia.

Author contributions

Daniel McIsaac, Emma Harris, and David MacDonald contributed to all aspects of this manuscript, including study conception and design; acquisition, analysis, and interpretation of data; and drafting the article. Laura Boland, Manoj Lalu, and Sylvain Boet contributed to the analysis and interpretation of data.

References

  1. 1.
    Schleidgen S, Klingler C, Bertram T, Rogowski WH, Marckmann G. What is personalized medicine: sharpening a vague term based on a systematic literature review. BMC Med Ethics 2013; 14: 55.CrossRefGoogle Scholar
  2. 2.
    Cesuroglu T, Syurina E, Feron F, Krumeich A. Other side of the coin for personalised medicine and healthcare: content analysis of ‘personalised’ practices in the literature. BMJ Open 2016; 6: e010243.CrossRefGoogle Scholar
  3. 3.
    Ginsburg GS, Phillips KA. Precision medicine: from science to value. Health Aff (Millwood) 2018; 37: 694-701.CrossRefGoogle Scholar
  4. 4.
    King A, Bottle A, Faiz O, Aylin P. Investigating adverse event free admissions in Medicare inpatients as a patient safety indicator. Ann Surg 2017; 265: 910-5.CrossRefGoogle Scholar
  5. 5.
    Grewal K, Wijeysundera DN, Carroll J, Tait G, Beattie WS. Gender differences in mortality following non-cardiovascular surgery: an observational study. Can J Anesth 2012; 59: 255-62.CrossRefGoogle Scholar
  6. 6.
    Wijeysundera DN, Beattie WS, Austin PC, Hux JE, Laupacis A. Non-invasive cardiac stress testing before elective major non-cardiac surgery: population based cohort study. BMJ 2010; 340: b5526.CrossRefGoogle Scholar
  7. 7.
    Bilimoria KY, Liu Y, Paruch JL, et al. Development and evaluation of the universal ACS NSQIP surgical risk calculator: a decision aid and informed consent tool for patients and surgeons. J Am Coll Surg 2013; 217: 833-42.e1-3.Google Scholar
  8. 8.
    Varelius J. The value of autonomy in medical ethics. Med Health Care Philos 2006; 9: 377-88.CrossRefGoogle Scholar
  9. 9.
    Leclercq WK, Keulers BJ, Scheltinga MR, Spauwen PH, van der Wilt GJ. A review of surgical informed consent: past, present, and future. A quest to help patients make better decisions. World J Surg 2010; 34: 1406-15.Google Scholar
  10. 10.
    Keulers BJ, Scheltinga MR, Houterman S, Van Der Wilt GJ, Spauwen PH. Surgeons underestimate their patients’ desire for preoperative information. World J Surg 2008; 32: 964-70.CrossRefGoogle Scholar
  11. 11.
    Taher T, Khan NA, Devereaux PJ, et al. Assessment and reporting of perioperative cardiac risk by Canadian general internists: art or science? J Gen Intern Med 2002; 17: 933-6.CrossRefGoogle Scholar
  12. 12.
    Gainer RA, Curran J, Buth KJ, David JG, Légaré JF, Hirsch GM. Toward optimal decision making among vulnerable patients referred for cardiac surgery: a qualitative analysis of patient and provider perspectives. Med Decis Mak 2017; 37: 600-10.CrossRefGoogle Scholar
  13. 13.
    Mulley AG Jr. The role of shared decision making in achieving allocative efficiency in health systems. In: Elwyn G, Edwards A, Thompson R, editors. Shared Decision Making in Healthcare: Achieving Evidence-Based Patient Choice. 2nd ed. Oxford, UK: Oxford University Press; 2016 .Google Scholar
  14. 14.
    Gattellari M, Ward JE. Will men attribute fault to their GP for adverse effects arising from controversial screening tests? An Australian study using scenarios about PSA screening. J Med Screen 2004; 11: 165-9.CrossRefGoogle Scholar
  15. 15.
    Wijeysundera DN. Predicting outcomes: is there utility in risk scores? Can J Anesth 2015; 63: 148-58.CrossRefGoogle Scholar
  16. 16.
    Levac D, Colquhoun H, O’Brien KK. Scoping studies: advancing the methodology. Implement Sci 2010; 5: 69.CrossRefGoogle Scholar
  17. 17.
    Tricco AC, Lillie E, Zarin W, et al. PRISMA Extension for Scoping Reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med 2018; 169: 467-73.CrossRefGoogle Scholar
  18. 18.
    Arksey H, O’Malley L. Scoping studies: towards a methodological framwork. Int J Soc Res Methodol 2005; 8: 19-32.CrossRefGoogle Scholar
  19. 19.
    Stiefel M, Nolan K. A Guide to Measuring the Triple Aim: Population Health, Experience of Care, and Per Capita Cost. Institute for Healthcare Improvement. Cambridge, MA; 2012. Available from URL: http://www.ihi.org/resources/Pages/IHIWhitePapers/AGuidetoMeasuringTripleAim.aspx (accessed March 2019).
  20. 20.
    McGowan J, Sampson M, Salzwedel DM, Cogo E, Foerster V, Lefebvre C. PRESS peer review of electronic search strategies: 2015 guideline statement. J Clin Epidemiol 2016; 75: 40-6.CrossRefGoogle Scholar
  21. 21.
    Levac D, Colquhoun H, O’Brien KK. Scoping studies: advancing the methodology. Implement Sci 2010; 5: 69.CrossRefGoogle Scholar
  22. 22.
    Malenka DJ, Ross CS, Langner C, et al. Can a Customized quantitative informed consent document improve decision quality and be integrated into the routine process of care Circulation 2011; 4: P308 (abstract).Google Scholar
  23. 23.
    Gainer R, Begum J, Wilson-Pease E, Hirsch G. A formalized shared decision making process with individualized decision aids improves comprehension and decisional quality among frail, elderly cardiac surgery patients. Can J Cardiol 2016; 32: S266-7.CrossRefGoogle Scholar
  24. 24.
    MacDonald V, Arthur B, Parent S. The Vancouver General Hospital joint replacement rapid recovery program: optimizing outcomes through focused pathways. J Orthop Nurs 2005; 9: 95-102.CrossRefGoogle Scholar
  25. 25.
    Punt IM, van der Most R, Bongers BC, et al. Improving pre- and perioperative hospital care: major elective surgery (German) Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz 2017; 60: 410-8.Google Scholar
  26. 26.
    Miranne JM, Gutman RE, Sokol AI, Park AJ, Iglesia CB. Effect of a new risk calculator on patient satisfaction with the decision for concomitant midurethral sling during prolapse surgery: a randomized controlled trial. Female Pelvic Med Reconstr Surg 2017; 23: 17-22.CrossRefGoogle Scholar
  27. 27.
    Aggarwal S, Stewart P, Eccersley J. Use of real time mortality risk assessment to inform standard of sugical care in a District General Hospital. Color Dis 2014; 116: 169.Google Scholar
  28. 28.
    Bihorac A, Cobb R, Wang DZ, et al. Computer algorithms are superior to physician assessment of the risk for postoperative complications. Crit Care Med 2013; DOI:  https://doi.org/10.1097/01.ccm.0000439930.22840.bc.
  29. 29.
    Moonesinghe SR, Mythen MG, Das P, Rowan KM, Grocott MP. Risk stratification tools for predicting morbidity and mortality in adult patients undergoing major surgery: qualitative systematic review. Anesthesiology 2013; 119: 959-81.CrossRefGoogle Scholar
  30. 30.
    Oliver CM, Walker E, Giannaris S, Grocott MP, Moonesinghe SR. Risk assessment tools validated for patients undergoing emergency laparotomy: a systematic review. Br J Anaesth 2015; 115: 849-60.CrossRefGoogle Scholar
  31. 31.
    Jelovsek JE, Chagin K, Brubaker L, et al. A model for predicting the risk of de novo stress urinary incontinence in women undergoing pelvic organ prolapse surgery. Obstet Gynecol 2014; 123(2 Pt 1): 279-87.CrossRefGoogle Scholar
  32. 32.
    Ford MK, Beattie WS, Wijeysundera DN. Systematic review: prediction of perioperative cardiac complications and mortality by the revised cardiac risk index. Ann Intern Med 2010; 152: 26-35.CrossRefGoogle Scholar
  33. 33.
    Ahmed H, Naik G, Willoughby H, Edwards AG. Communicating risk. BMJ 2012; 344: e3996.CrossRefGoogle Scholar
  34. 34.
    Neuner-Jehle S, Senn O, Wegwarth O, Rosemann T, Steurer J. How do family physicians communicate about cardiovascular risk? Frequencies and determinants of different communication formats. BMC Fam Pract 2011; 12: 15.CrossRefGoogle Scholar
  35. 35.
    Wegwarth O, Schwartz LM, Woloshin S, Gaissmaier W, Gigerenzer G. Do physicians understand cancer screening statistics? A national survey of primary care physicians in the United States. Ann Intern Med 2012; 156: 340-9.CrossRefGoogle Scholar
  36. 36.
    Trevena LJ, Davey HM, Barratt A, Butow P, Caldwell P. A systematic review on communicating with patients about evidence. J Eval Clin Pract 2006; 12: 13-23.CrossRefGoogle Scholar
  37. 37.
    Harrell FE, Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med 1996; 15: 361-87.CrossRefGoogle Scholar
  38. 38.
    Fagerlin A, Zikmund-Fisher BJ, Ubel PA. Helping patients decide: ten steps to better risk communication. J Natl Cancer Inst 2011; 103: 1436-43.CrossRefGoogle Scholar
  39. 39.
    Zikmund-Fisher BJ, Ubel PA, Smith DM, et al. Communicating side effect risks in a tamoxifen prophylaxis decision aid: the debiasing influence of pictographs. Patient Educ Couns 2008; 73: 209-14.CrossRefGoogle Scholar
  40. 40.
    Edwards AG, Naik G, Ahmed H, et al. Personalised risk communication for informed decision making about taking screening tests. Cochrane Database of Syst Rev 2013; 2: CD001865.Google Scholar
  41. 41.
    Beggs T, Sepehri A, Szwajcer A, Tangri N, Arora RC. Frailty and perioperative outcomes: a narrative review. Can J Anesth 2015; 62: 143-57.CrossRefGoogle Scholar
  42. 42.
    Lin HS, Watts JN, Peel NM, Hubbard RE. Frailty and post-operative outcomes in older surgical patients: a systematic review. BMC Geriatr 2016; 16: 157.CrossRefGoogle Scholar
  43. 43.
    Rodseth RN, Lurati Buse GA, Bolliger D, et al. The predictive ability of pre-operative B-type natriuretic peptide in vascular patients for major adverse cardiac events: an individual patient data meta-analysis. J Am Coll Cardiol 2011; 58: 522-9.CrossRefGoogle Scholar
  44. 44.
    Nan J, Li J, Li X, Guo G, Wen X, Tian Y. Preoperative serum carcinoembryonic antigen as a marker for predicting the outcome of three cancers. Biomark Cancer 2017; 9: 1-7.CrossRefGoogle Scholar
  45. 45.
    Moulton BW, Pope TM. Emerging legal issues for providers in the US. In: Elwyn G, Edwards A, Thompson R, editors. Shared Decision Making in Healthcare: Achieving Evidence-Based Patient Choice. 2nd ed. Oxford, UK: Oxford University Press; 2016 .Google Scholar
  46. 46.
    Barry MJ, Edgman-Levitan S. Shared decision making–pinnacle of patient-centered care. N Engl J Med 2012; 366: 780-1.CrossRefGoogle Scholar
  47. 47.
    Hargraves I, LeBlanc A, Shah ND, Montori VM. Shared decision making: the need for patient-clinician conversation, not just information. Health Aff (Millwood) 2016; 35: 627-9.CrossRefGoogle Scholar
  48. 48.
    Joseph-Williams N, Elwyn G, Edwards A. Knowledge is not power for patients: a systematic review and thematic synthesis of patient-reported barriers and facilitators to shared decision making. Patient Educ Couns 2014; 94: 291-309.CrossRefGoogle Scholar

Copyright information

© Canadian Anesthesiologists' Society 2019

Authors and Affiliations

  1. 1.Departments of Anesthesiology & Pain MedicineUniversity of Ottawa and The Ottawa HospitalOttawaCanada
  2. 2.Department of Anesthesia, Pain Management and Perioperative MedicineDalhousie UniversityHalifaxCanada
  3. 3.Population Health, Faculty of Health SciencesUniversity of OttawaOttawaCanada
  4. 4.Ottawa Hospital Research InstituteOttawaCanada
  5. 5.Department of Innovation in Medical EducationUniversity of OttawaOttawaCanada
  6. 6.Department of Cellular and Molecular MedicineUniversity of OttawaOttawaCanada
  7. 7.School of Epidemiology, & Public HealthUniversity of OttawaOttawaCanada
  8. 8.Departments of Anesthesiology & Pain MedicineThe Ottawa HospitalOttawaCanada

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