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Postoperative shared-care for patients undergoing non-cardiac surgery: a systematic review and meta-analysis

  • Sasha Mazzarello
  • Daniel I. McIsaac
  • Joshua Montroy
  • Dean A. Fergusson
  • Dalal Yateem
  • P. J. Devereaux
  • Manoj M. LaluEmail author
Review Articles/Brief Reviews

Abstract

Background

Collaborative (“shared-care”) models of postoperative care improve outcomes in patients undergoing surgery for hip fracture. Despite being widely adopted, it is unclear if similar benefits of shared-care models exist for other at-risk surgical patient populations. Thus, we performed a systematic review to understand the impact of shared-care models.

Methods

EMBASE, MEDLINE, CINAHL, and Cochrane Central Register databases were searched for prospective studies examining an in-hospital shared-care approach to postoperative management of adult non-cardiac surgery patients. The primary outcome was a composite of in-hospital mortality and mortality of up to 30 days. Secondary outcomes were long-term mortality (> 90 days) and hospital length of stay. Tertiary outcomes included quality of life and health utility measures. Risk of bias was assessed using Cochrane Collaboration tools.

Results

Six thousand eight hundred and ninety-six citations were reviewed and four studies (n = 987 patients) met the inclusion criteria—two randomized-controlled trials (RCT, n = 729 patients) and two non-randomized-controlled trials (NRCT, n = 258 patients). All studies were conducted in the elective surgical setting. There was no association between shared-care and control groups for in-hospital mortality (Peto odds ratio, 1.76; 95% confidence interval [CI], 0.65 to 4.80), or hospital length of stay (mean difference, −1.41; 95% CI, −3.18 to 0.35). Reporting of other outcomes was limited. Both RCTs were judged to be at high risk of bias for blinding and both NRCTs were judged to be at moderate risk of bias for reported outcomes.

Conclusion

Overall, there was limited high-quality evidence to evaluate the effect of postoperative shared-care. Well-designed interventional studies, perhaps targeting higher risk surgical populations, are needed.

Registration

PROSPERO (CRD42018094943); registered 16 May, 2018.

Soins partagés postopératoires pour les patients subissant une chirurgie non cardiaque: revue systématique et méta-analyse

Résumé

Contexte

Les modèles de soins postopératoires collaboratifs (« soins partagés ») améliorent le pronostic des patients subissant une chirurgie pour fracture de la hanche. Malgré l’adoption répandue de ce modèle de soins partagés, nous ne savons pas s’ils offrent des avantages semblables pour d’autres populations chirurgicales de patients à risque. Nous avons par conséquent réalisé une revue systématique afin de comprendre l’impact des modèles de soins partagés.

Méthode

Les bases de données EMBASE, MEDLINE, CINAHL et Cochrane Central Register ont été examinées afin d’en extraire les études prospectives examinant l’utilisation de soins partagés en milieu hospitalier pour la prise en charge postopératoire de patients chirurgicaux adultes hors chirurgie cardiaque. Le critère d’évaluation principal était un composé de la mortalité hospitalière et de la mortalité jusqu’à 30 jours. Les critères d’évaluation secondaires étaient la mortalité à long terme (> 90 jours) et la durée de séjour hospitalier. Les critères tertiaires comprenaient des mesures de la qualité de vie et de la santé. Le risque de biais a été évalué à l’aide d’outils du Cochrane Collaboration.

Résultats

Six mille huit cent quatre-vingt-seize citations ont été passées en revue et quatre études (n = 987 patients) ont répondu à nos critères d’inclusion, soit deux études randomisées contrôlées (ERC, n = 729 patients) et deux études non randomisées contrôlées (ENRC, n = 258 patients). Toutes les études ont été réalisées dans un contexte de chirurgie non urgente. Aucune association entre les soins partagés et les groupes témoin n’a été observée en ce qui touchait à la mortalité hospitalière (rapport de cotes de Peto, 1,76; intervalle de confiance [IC] 95 %, 0,65 à 4,80) ou à la durée de séjour hospitalier (différence moyenne, −1,41; IC 95 %, −3,18 à 0,35). La communication de nos autres critères d’évaluation était limitée. Il a été estimé que les deux ERC affichaient un risque élevé de biais de non-respect de l’insu et les deux ENRC affichaient un risque modéré de biais dans la communication des résultats.

Conclusion

Globalement, les données probantes de qualité élevée étaient limitées pour évaluer l’effet de soins postopératoires partagés. Des études d’intervention bien conçues, ciblant peut-être des populations chirurgicales à risque plus élevé, sont nécessaires.

Enregistrement de l’étude

PROSPERO (CRD42018094943); enregistrée le 16 mai 2018.

Notes

Acknowledgements

Manoj M. Lalu and Daniel I. McIsaac are supported by The Ottawa Hospital Anesthesia Alternate Funds Association and the Scholarship Protected Time Program, Department of Anesthesiology and Pain Medicine, uOttawa. The authors would like to thank Risa Shorr, MLIS, Learning Services, The Ottawa Hospital for assistance in generating the systematic search strategy.

Conflicts of interest

None declared.

Editorial responsibility

This submission was handled by Dr. Hilary P. Grocott, Editor-in-Chief, Canadian Journal of Anesthesia.

Author contributions

Sasha Mazzarello and Manoj M. Lalu helped with study conception and design, acquisition of data, analysis and interpretation of data, and drafting of the manuscript. Daniel I. McIsaac helped with study conception and design, analysis and interpretation of data, drafting of the manuscript, and critical revision of the manuscript. Josh Montroy helped with the acquisition, analysis, and interpretation of data. Dean A. Fergusson helped with study conception and design, analysis and interpretation of data, and critical revision of the manuscript. Dalal Yateem helped with acquisition of data and critical revision of the manuscript. P.J. Devereaux helped with study conception and design, analysis and interpretation of data, and critical revision of the manuscript.

Financial disclosures

None.

Supplementary material

12630_2019_1433_MOESM1_ESM.pdf (1.5 mb)
Supplementary material 1 (PDF 1509 kb)

References

  1. 1.
    Weiser TG, Regenbogen SE, Thompson KD, et al. An estimation of the global volume of surgery: a modelling strategy based on available data. Lancet 2008; 372: 139-44.CrossRefGoogle Scholar
  2. 2.
    Bickler SW, Spiegel DA. Global surgery–defining a research agenda. Lancet 2008; 372: 90-2.CrossRefGoogle Scholar
  3. 3.
    Botto F, Alonso-Coello P, Chan MT, et al. Myocardial injury after noncardiac surgery: a large, international, prospective cohort study establishing diagnostic criteria, characteristics, predictors, and 30-day outcomes. Anesthesiology 2014; 120: 564-78.CrossRefGoogle Scholar
  4. 4.
    Kristensen SD, Knuuti J, Saraste A, et al. 2014 ESC/ESA Guidelines on non-cardiac surgery: cardiovascular assessment and management: The Joint Task Force on non-cardiac surgery: cardiovascular assessment and management of the European Society of Cardiology (ESC) and the European Society of Anaesthesiology (ESA). Eur Heart J 2014; 35: 2383-431.CrossRefGoogle Scholar
  5. 5.
    Writing Committee for the Vision Study Investigators, Devereaux PJ, Biccard BM, et al. Association of postoperative high-sensitivity troponin levels with myocardial injury and 30-day mortality among patients undergoing noncardiac surgery. JAMA 2017; 317: 1642-51.CrossRefGoogle Scholar
  6. 6.
    Vascular Events In Noncardiac Surgery Patients Cohort Evaluation (VISION) Study Investigators, Devereaux PJ, Chan MT, et al. Association between postoperative troponin levels and 30-day mortality among patients undergoing noncardiac surgery. JAMA 2012; 307: 2295-304.CrossRefGoogle Scholar
  7. 7.
    Devereaux PJ, Sessler DI. Cardiac complications and major noncardiac surgery. N Engl J Med 2016; 374: 1394-5.Google Scholar
  8. 8.
    Devereaux PJ, Sessler DI, Leslie K, et al. Clonidine in patients undergoing noncardiac surgery. N Engl J Med 2014; 370: 1504-13.CrossRefGoogle Scholar
  9. 9.
    Grigoryan KV, Javedan H, Rudolph JL. Orthogeriatric care models and outcomes in hip fracture patients: a systematic review and meta-analysis. J Orthop Trauma 2014; 28: e49-55.CrossRefGoogle Scholar
  10. 10.
    Duceppe E, Parlow J, MacDonald P, et al. Canadian Cardiovascular Society Guidelines on perioperative cardiac risk assessment and management for patients who undergo noncardiac surgery. Can J Cardiol 2017; 33: 17-32.CrossRefGoogle Scholar
  11. 11.
    Moher D, Liberati A, Tetzlaff J, Altman DG, PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. BMJ 2009; 339: b2535.CrossRefGoogle Scholar
  12. 12.
    Mendis N, Hamilton GM, McIsaac DI, et al. A systematic review of the impact of surgical special care units on patient outcomes and health care resource utilization. Anesth Analg 2019; 128: 533-42.CrossRefGoogle Scholar
  13. 13.
    Stiefel M, Nolan K. A Guide to Measuring the Triple Aim: Population Health, Experience of Care, and Per Capita Cost. IHI Innovation Series white paper Cambridge, Massachusetts: Institute for Healthcare Improvement; 2012 .Google Scholar
  14. 14.
    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
  15. 15.
    Higgins JP, Altman DG, Gotzsche PC, et al. The Cochrane Collaboration’s tool for assessing risk of bias in randomised trials. BMJ 2011; 343: d5928.CrossRefGoogle Scholar
  16. 16.
    Sterne JA, Hernan MA, Reeves BC, et al. ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions. BMJ 2016; 355: i4919.CrossRefGoogle Scholar
  17. 17.
    Brockhaus AC, Grouven U, Bender R. Performance of the Peto odds ratio compared to the usual odds ratio estimator in the case of rare events. Biom J 2016; 58: 1428-44.CrossRefGoogle Scholar
  18. 18.
    Higgins JP, Green S. Cochrane Handbook for Systematic Reviews of Interventions 2011. Available from URL: http://handbook-5-1.cochrane.org/ (accessed March 2019).
  19. 19.
    Guyatt GH, Thorlund K, Oxman AD, et al. GRADE guidelines: 13. Preparing summary of findings tables and evidence profiles-continuous outcomes. J Clin Epidemiol 2013; 66: 173-83.CrossRefGoogle Scholar
  20. 20.
    Hempenius L, Slaets JP, van Asselt D, de Bock GH, Wiggers T, van Leeuwen BL. Outcomes of a geriatric liaison intervention to prevent the development of postoperative delirium in frail elderly cancer patients: report on a multicentre, randomized, controlled trial. PLoS One 2013; 8: e64834.CrossRefGoogle Scholar
  21. 21.
    Hempenius L, Slaets JP, van Asselt D, de Bock TH, Wiggers T, van Leeuwen BL. Long term outcomes of a geriatric liaison intervention in frail elderly cancer patients. PLoS One 2016; 11: e0143364.CrossRefGoogle Scholar
  22. 22.
    Huddleston JM, Long KH, Naessens JM, et al. Medical and surgical comanagement after elective hip and knee arthroplasty: a randomized, controlled trial. Ann Intern Med 2004; 141: 28-38.CrossRefGoogle Scholar
  23. 23.
    Harari D, Hopper A, Dhesi J, Babic-Illman G, Lockwood L, Martin F. Proactive care of older people undergoing surgery (‘POPS’): designing, embedding, evaluating and funding a comprehensive geriatric assessment service for older elective surgical patients. Age Ageing 2007; 36: 190-6.CrossRefGoogle Scholar
  24. 24.
    Chen W, Chang CC, Chiu HC, Shabbir A, Perng DS, Huang CK. Use of individual surgeon versus surgical team approach: surgical outcomes of laparoscopic Roux-en-Y gastric bypass in an Asian Medical Center. Surg Obes Relat Dis 2012; 8: 214-9.CrossRefGoogle Scholar
  25. 25.
    McHorney CA, Ware JE Jr, Lu JF, Sherbourne CD. The MOS 36-item Short-Form Health Survey (SF-36): III. Tests of data quality, scaling assumptions, and reliability across diverse patient groups. Med Care 1994; 32: 40-66.CrossRefGoogle Scholar
  26. 26.
    Sweeting MJ, Sutton AJ, Lambert PC. What to add to nothing? Use and avoidance of continuity corrections in meta-analysis of sparse data. Stat Med 2004; 23: 1351-75.CrossRefGoogle Scholar
  27. 27.
    Keus F, Wetterslev J, Gluud C, Gooszen HG, van Laarhoven CJ. Robustness assessments are needed to reduce bias in meta-analyses that include zero-event randomized trials. Am J Gastroenterol 2009; 104: 546-51.CrossRefGoogle Scholar
  28. 28.
    Bradburn MJ, Deeks JJ, Berlin JA, Russell Localio A. Much ado about nothing: a comparison of the performance of meta-analytical methods with rare events. Stat Med 2007; 26: 53-77.CrossRefGoogle Scholar

Copyright information

© Canadian Anesthesiologists' Society 2019

Authors and Affiliations

  1. 1.Clinical Epidemiology Program, Blueprint Translational Research GroupOttawa Hospital Research InstituteOttawaCanada
  2. 2.School of Epidemiology and Public HealthUniversity of OttawaOttawaCanada
  3. 3.Department of Anesthesiology and Pain MedicineThe Ottawa Hospital Research InstituteOttawaCanada
  4. 4.Faculty of MedicineUniversity of OttawaOttawaCanada
  5. 5.Population Health Research Institute, David Braley Cardiac, Vascular, and Stroke Research InstituteMcMaster UniversityHamiltonCanada
  6. 6.Regenerative Medicine ProgramThe Ottawa Hospital Research InstituteOttawaCanada
  7. 7.Department of Cellular and Molecular MedicineUniversity of OttawaOttawaCanada

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