Skip to main content

Advertisement

SpringerLink
Log in

Intensive care utilization following major noncardiac surgical procedures in Ontario, Canada: a population-based study

  • Original
  • Published:
Intensive Care Medicine Aims and scope Submit manuscript

Abstract

Purpose

Patients are sometimes admitted to intensive care units (ICU) after elective noncardiac surgery for advanced monitoring and treatments not available on a general postsurgical ward. However, patterns of ICU utilization are poorly understood. Our aims were to assess the incidence and determinants of ICU utilization after elective noncardiac surgical procedures.

Methods

Population-based cohort study included adult patients who underwent 13 types of major elective noncardiac surgical procedures between 2006 and 2014 in Ontario, Canada. Primary outcome was early admission to ICU within 24 h after surgery. A prespecified analysis using multilevel logistic regression modeling separately examined patient- and hospital-level factors associated with early ICU admission within distinct groups of surgical procedures.

Results

Early ICU admission occurred in 9.6% of the included 541,524 patients. Patients admitted early to ICU showed higher median age (68 vs. 65 years), burden of prehospital comorbidities (Charlson comorbidity index score ≥ 2, 33.1 vs. 10.4%), 30-day mortality rates (2.4 vs. 0.3%), and longer median postoperative hospital stays (6 vs. 4 days) than patients admitted to a ward. There was wide variation in proportions of patients admitted early to ICU across different surgery types (0.9% for hysterectomy to 90.8% for open abdominal aortic aneurysm repair) with generally low 30-day mortality across procedures (0.1–2.8%). Within individual procedures, there was wide interhospital variation in the range of early ICU admission rates (hysterectomy 0.07–14.4%, lower gastrointestinal resection 1.3–95%, endovascular aortic aneurysm 1.3–95.2%). The individual hospital where surgery was performed accounted for a large proportion of the variation in early ICU admission rates, with the median odds ratio ranging from 2.3 for hysterectomy to 21.5 for endovascular aortic aneurysm.

Conclusions

There is a wide variation in early ICU admission across and within surgical procedures. The individual hospital accounts for a large proportion of this variation. Further research is required to identify the basis for this variation and to develop better methods for allocating ICU resources for postoperative management of surgical patients.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

  1. Weiser TG, Haynes AB, Molina G, Lipsitz SR, Esquivel MM, Uribe-Leitz T, Fu R, Azad T, Chao TE, Berry WR, Gawande AA (2015) Estimate of the global volume of surgery in 2012: an assessment supporting improved health outcomes. Lancet 385:S11

    Article  PubMed  Google Scholar 

  2. Gillies MA, Harrison EM, Pearse RM, Garrioch S, Haddow C, Smyth L, Parks R, Walsh TS, Lone NI (2017) Intensive care utilization and outcomes after high-risk surgery in Scotland: a population-based cohort study. Br J Anaesth 118:123–131

    Article  PubMed  CAS  Google Scholar 

  3. Biccard BM, Madiba TE, South African Surgical Outcomes Study Investigators (2015) The South African Surgical Outcomes Study: a 7-day prospective observational cohort study. S Afr Med J 105:465

    Article  PubMed  CAS  Google Scholar 

  4. Pearse RM, Moreno RP, Bauer P, Pelosi P, Metnitz P, Spies C, Vallet B, Vincent J-L, Hoeft A, Rhodes A (2012) Mortality after surgery in Europe: a 7 day cohort study. Lancet 380:1059–1065

    Article  PubMed  PubMed Central  Google Scholar 

  5. Canadian Institute for Health Information (CIHI). Care in Canadian ICUs. August 2016

  6. Wunsch H, Gershengorn HB, Cooke CR, Guerra C, Angus DC, Rowe JW, Li G (2016) Use of intensive care services for medicare beneficiaries undergoing major surgical procedures. Anesthesiology 124:899–907

    Article  PubMed  PubMed Central  Google Scholar 

  7. Kahan BC, Koulenti D, Arvaniti K, Beavis V, Campbell D, Chan M, Moreno R, Pearse RM, International Surgical Outcomes Study Group (2017) Critical care admission following elective surgery was not associated with survival benefit: prospective analysis of data from 27 countries. Intensive Care Med 43:971–979

    Article  PubMed  Google Scholar 

  8. Gillies MA, Pearse RM (2016) Intensive care after high-risk surgery: what’s in a name? Anesthesiology 124:761–762

    Article  PubMed  Google Scholar 

  9. Ghaffar S, Pearse RM, Gillies MA (2017) ICU admission after surgery: who benefits? Curr Opin Crit Care 23:424–429

    Article  PubMed  Google Scholar 

  10. Gillies MA, Power GS, Harrison DA, Fleming A, Cook B, Walsh TS, Pearse RM, Rowan KM (2015) Regional variation in critical care provision and outcome after high-risk surgery. Intensive Care Med 41:1809–1816

    Article  PubMed  Google Scholar 

  11. Wunsch H, Angus DC, Harrison DA, Collange O, Fowler R, Hoste EA, de Keizer NF, Kersten A, Linde-Zwirble WT, Sandiumenge A, Rowan KM (2008) Variation in critical care services across North America and Western Europe. Crit Care Med 36(2787–2793):e2781–e2789

    Google Scholar 

  12. Wunsch H, Angus DC, Harrison DA, Linde-Zwirble WT, Rowan KM (2011) Comparison of medical admissions to intensive care units in the United States and United Kingdom. Am J Respir Crit Care Med 183:1666–1673

    Article  PubMed  Google Scholar 

  13. International Surgical Outcomes Study Group (2016) Global patient outcomes after elective surgery: prospective cohort study in 27 low-, middle- and high-income countries. Br J Anaesth 117:601–609

    Article  Google Scholar 

  14. Charlson ME, Pompei P, Ales KL, MacKenzie CR (1987) A new method of classifying prognostic comorbidity in longitudanal studies: development and validation. J Chron Dis 40:373–383

    Article  PubMed  CAS  Google Scholar 

  15. Charlson ME, Szatrowski TP, Peterson J, Gold J (1994) Validation of a combined comorbidity index. J Clin Epidemiol 47:1245–1251

    Article  PubMed  CAS  Google Scholar 

  16. Hux JE, Ivis F, Flintoft V, Bica A (2002) Diabetes in Ontario: determination of prevalence and incidence using a validated administrative data algorithm. Diabetes Care 25:512–516

    Article  PubMed  Google Scholar 

  17. Tu K, Campbell NRC, Chen Z, Cauch-Dudek KJ, McAlister FA (2007) Accuracy of administrative databases in identifying patients with hypertension. Open Med 1:E18–E26

    PubMed  PubMed Central  Google Scholar 

  18. Gershon AS, Wang C, Guan J, Vasilevska-Ristovska J, Cicutto L, To T (2009) Identifying individuals with physician diagnosed COPD in health administrative databases. Copd 6:388–394

    Article  PubMed  CAS  Google Scholar 

  19. Quan H, Khan N, Hemmelgarn BR, Tu K, Chen G, Campbell N, Hill MD, Ghali WA, McAlister FA (2009) Validation of a case definition to define hypertension using administrative data. Hypertension 54:1423–1428

    Article  PubMed  CAS  Google Scholar 

  20. Garland A, Yogendran M, Olafson K, Scales DC, McGowan KL, Fransoo R (2012) The accuracy of administrative data for identifying the presence and timing of admission to intensive care units in a Canadian province. Med Care 50:e1–e6

    Article  PubMed  Google Scholar 

  21. Scales DC, Guan J, Martin CM, Redelmeier DA (2006) Administrative data accurately identified intensive care unit admissions in Ontario. J Clin Epidemiol 59:802–807

    Article  PubMed  Google Scholar 

  22. Juurlink D, Preyra C, Croxford R, Chong A, Austin P (2006) Canadian Institute for health information discharge abstract database: a validation study. ICES investigative report, Toronto

    Google Scholar 

  23. Canadian Institute for Health Information (CIHI). CIHI data quality study of the 2009–2010. Discharge Abstract Database. Revised June 2012

  24. Mukaka MM (2012) Statistics corner: a guide to appropriate use of correlation coefficient in medical research. Malawi Med J 24:69–71

    PubMed  PubMed Central  CAS  Google Scholar 

  25. Harrell FE Jr (2015) Regression modeling strategies. Springer, Cham

    Book  Google Scholar 

  26. Austin PC, Merlo J (2017) Intermediate and advanced topics in multilevel logistic regression analysis. Stat Med 36:3257–3277

    Article  PubMed  PubMed Central  Google Scholar 

  27. Austin PC, Steyerberg EW (2014) Graphical assessment of internal and external calibration of logistic regression models by using loess smoothers. Stat Med 33:517–535

    Article  PubMed  Google Scholar 

  28. Admon AJ, Wunsch H, Iwashyna TJ, Cooke CR (2017) Hospital contributions to variability in the use of ICUs among elderly Medicare recipients. Crit Care Med 45:75–84

    Article  PubMed  PubMed Central  Google Scholar 

  29. Gershengorn HB, Iwashyna TJ, Cooke CR, Scales DC, Kahn JM, Wunsch H (2012) Variation in use of intensive care for adults with diabetic ketoacidosis. Crit Care Med 40:2009–2015

    Article  PubMed  PubMed Central  Google Scholar 

  30. Chen R, Strait KM, Dharmarajan K, Li SX, Ranasinghe I, Martin J, Fazel R, Masoudi FA, Cooke CR, Nallamothu BK, Krumholz HM (2015) Hospital variation in admission to intensive care units for patients with acute myocardial infarction. Am Heart J 170:1161–1169

    Article  PubMed  PubMed Central  Google Scholar 

  31. Gershengorn HB, Garland A, Gong MN (2015) Patterns of daily costs differ for medical and surgical intensive care unit patients. Ann Am Thorac Soc 12:1831–1836

    Article  PubMed  Google Scholar 

  32. Sjoding MW, Prescott HC, Wunsch H, Iwashyna TJ, Cooke CR (2015) Hospitals with the highest intensive care utilization provide lower quality pneumonia care to the elderly. Crit Care Med 43:1178–1186

    Article  PubMed  PubMed Central  Google Scholar 

  33. Valley TS, Sjoding MW, Ryan AM, Iwashyna TJ, Cooke CR (2015) Association of intensive care unit admission with mortality among older patients with pneumonia. JAMA 314:1272–1279

    Article  PubMed  PubMed Central  CAS  Google Scholar 

Download references

Acknowledgements

This study was supported by a grant from the Academic Health Science Centre Alternative Funding Plan (AHSC AFP) Innovation Fund. DNW is supported in part by a New Investigator Award from the Canadian Institutes of Health Research. HW and DNW are supported in part by Merit Awards from the Department of Anesthesia at the University of Toronto. AL holds a Canada Research Chair in Health Policy and Citizen Engagement. PCA is supported by a Career Investigator Award from the Heart and Stroke Foundation.

Author information

Authors and Affiliations

  1. Department of Anesthesia and Pain Management, Toronto General Hospital, 3-EN 200 Elizabeth St, Toronto, ON, M5G 2C4, Canada

    Angela Jerath & Duminda N. Wijeysundera

  2. Department of Anesthesia, University of Toronto, Room 1201, 123 Edward St, Toronto, ON, M5G 1E2, Canada

    Angela Jerath, Hannah Wunsch & Duminda N. Wijeysundera

  3. Institute for Clinical Evaluative Sciences, 2075 Bayview Avenue, G-Wing, Toronto, ON, M4N 3M5, Canada

    Angela Jerath, Andreas Laupacis, Peter C. Austin & Duminda N. Wijeysundera

  4. Toronto General Hospital Research Institute, 200 Elizabeth St, Toronto, ON, M5G 2C4, Canada

    Angela Jerath

  5. Li Ka Shing Knowledge Institute, St. Michael’s Hospital, 30 Bond St, Toronto, ON, M5B 1W8, Canada

    Andreas Laupacis & Duminda N. Wijeysundera

  6. Sunnybrook Health Sciences Centre, 2075 Bayview Avenue, Toronto, ON, M4N 3M5, Canada

    Hannah Wunsch

Authors
  1. Angela Jerath
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andreas Laupacis
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Peter C. Austin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hannah Wunsch
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Duminda N. Wijeysundera
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Angela Jerath.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 3538 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jerath, A., Laupacis, A., Austin, P.C. et al. Intensive care utilization following major noncardiac surgical procedures in Ontario, Canada: a population-based study. Intensive Care Med 44, 1427–1435 (2018). https://doi.org/10.1007/s00134-018-5330-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00134-018-5330-6

Keywords

Search

Navigation