Skip to main content
SpringerLink
Log in

Preoperative and surgical factors associated with postoperative intensive care unit admission following operative treatment for degenerative lumbar spine disease

  • Original Article
  • Published:
European Spine Journal Aims and scope Submit manuscript

Abstract

Purpose

Evaluate the factors associated with postoperative ICU admission in patients undergoing surgical management of degenerative lumbar spine disease.

Methods

Patients undergoing surgery for degenerative lumbar spine disease were enrolled into a prospective registry over a 2-year period. Preoperative variables (age, gender, ASA grade, ODI %, CAD, HTN, MI, CHF, DM, BMI, depression, anxiety) and surgical variables (instrumentation, arthrodesis, estimated blood loss, length of surgery) were collected prospectively. Postoperative ICU admission details were retrospectively determined from the electronic medical record. Student’s t test (continuous variables) and Chi-square test (categorical variables) were used to determine the association of each preoperative and surgical variable with ICU admission.

Results

808 Patients (273 laminectomy, 535 laminectomy and fusion) were evaluated. Forty-one (5.1 %) patients were found to have postoperative ICU admissions. Reasons for admission included blood loss (12.2 %), cardiac (29.3 %), respiratory (19.5 %), neurologic (31.7 %), and other (7.3 %). For preoperative variables, female gender (P < 0.001), history of CAD (P = 0.003), history of MI (P = 0.008), history of CHF (P = 0.001), age (P = 0.025), and ASA grade (P = 0.008) were significantly associated with ICU admission. For surgical variables, estimated blood loss (P < 0.001) and length of surgery (P < 0.001) were significantly associated with ICU admission.

Conclusions

Age, female gender, ASA grade, cardiac comorbidities, intraoperative blood loss, and length of surgery were associated with increased risk of postoperative ICU admission. Knowledge of these factors can aid surgeons in patient selection and preoperative discussion with patients about potential need for unexpected admission to the ICU.

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

Similar content being viewed by others

References

  1. NHE Fact Sheet (2014) http://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/NationalHealthExpendData/NHE-Fact-Sheet.html. Accessed 11 March 2015

  2. Pastores S, Dakwar J, Halpern NA (2012) Costs of critical care medicine. Crit Care Clin 28(1):1–10

    Article  PubMed  Google Scholar 

  3. Halpern N, Pastores S (2010) Critical care medicine in the United States 2000–2005: an analysis of bed numbers, occupancy rates, payer mix, and costs. Crit Care Med 38(1):65–71

    Article  PubMed  Google Scholar 

  4. Coopersmith C, Wunsch H, Fink M et al (2012) A comparison of critical care research funding and the financial burden of critical illness in the United States. Crit Care Med 40(4):1072–1079

    Article  PubMed  Google Scholar 

  5. Halpern N (2009) Can the costs of critical care be controlled? Curr Opin Crit Care 15(6):591–596

    Article  PubMed  Google Scholar 

  6. Patient Protection and Affordable Care Act (2010) In: Congress US (ed) 111–148

  7. Delisle D (2013) Big things come in bundled packages: implications of bundled payment systems in health care reimbursement reform. Am J Med Qual 28(4):339–344

    Article  PubMed  Google Scholar 

  8. Froimson M, Rana A, White RJ et al (2013) Bundled payments for care improvement initiative: the next evolution of payment formulations: AAHKS Bundled Payment Task Force. J Arthroplast 28(8 suppl):157–165

    Article  Google Scholar 

  9. Bushnell B (2015) Bundled payments in orthopedic surgery. Orthopedics 38(2):128–135

    Article  PubMed  Google Scholar 

  10. Schutzer S (2015) Bundled payment programs: how to get started: assessing readiness and bringing the stakeholders to the table. J Arthroplast 30(3):343–345

    Article  Google Scholar 

  11. Hamilton M, Cecconi M, Rhodes A (2011) A systematic review and meta-analysis on the use of preemptive hemodynamic intervention to improve postoperative outcomes in moderate and high-risk surgical patients. Anesth Analg 112(6):1392–1402

    Article  PubMed  Google Scholar 

  12. Ghaferi A, Birkmeyer J, Dimick J (2009) Variation in hospital mortality associated with inpatient surgery. N Engl J Med 361:1368–1375

    Article  CAS  PubMed  Google Scholar 

  13. Chang C, Jiang C (1997) Evaluation of critical postoperative situations in orthopedic patients. J Formos Med Assoc 96(12):990–995

    CAS  PubMed  Google Scholar 

  14. AbdelSalam H, Restrepo C, Tarity T, Sangster W, Parvizi J (2012) Predictors of intensive care unit admission after total joint arthroplasty. J Arthroplast 27(5):720–725

    Article  Google Scholar 

  15. Kamath A, McAuliffe C, Gutsche J et al (2013) Intensive care monitoring after total joint replacement. Bone Joint J 11(Suppl A(95-B)):74–76

    Article  Google Scholar 

  16. Pinheiro L, Santoro I, Perfeito J, Izbicki M, Ramos R, Faresin S (2015) Preoperative predictive factors for intensive care unit admission after pulmonary resection. J Bras Pneumol 41(1):31–38

    Article  PubMed  PubMed Central  Google Scholar 

  17. Sudarshan M, Feldman L, St Louis E et al (2015) Predictors of mortality and morbidity for acute care surgery patients. J Surg Res 193(2):868–873

    Article  PubMed  Google Scholar 

  18. Helling T, Martin L, Martin M, Mitchell M (2014) Failure events in transition of care for surgical patients. J Am Coll Surg 218(4):723–731

    Article  PubMed  Google Scholar 

  19. Weinstein J, Lurie J, Olson P, Bronner K, Fisher E (2006) United States’ trends and regional variations in lumbar spine surgery: 1992–2003. Spine (Phila Pa 1976) 31:2707–2714

    Article  Google Scholar 

  20. Deyo R, Mirza S, Bartin B, Kreuter W, Goodman D, Jarvik J (2010) Trends, major medical complications, and charges associated with surgery for lumbar spinal stenosis in older adults. JAMA 303:1259–1265

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Menke H, Klein A, John KD et al (1993) Predictive value of ASA classification for the assessment of the perioperative risk. Int Surg 78(3):266–270

    CAS  PubMed  Google Scholar 

  22. Wolters U, Wolf T, Stützer H et al (1996) ASA classification and perioperative variables as predictors of postoperative outcome. Br J Anaesth 77(2):217–222

    Article  CAS  PubMed  Google Scholar 

  23. Garcia AE, Bonnaig JV, Yoneda ZT et al (2012) Patient variables which may predict length of stay and hospital costs in elderly patients with hip fracture. J Orthop Trauma 26(11):620–623

    Article  PubMed  Google Scholar 

  24. Joshi Y, Ali M, Pradhan N et al (2012) Correlation between ASA grade, BMI, and length of stay of patients undergoing primary hip and knee arthroplasty. Does ‘cherry picking’ by the ITC affect NHS hospitals? J Bone Joint Surg Br 94-B no(SUPP IV):9

    Google Scholar 

  25. Fairbank JC, Couper J, Davies JB, O’Brien JP (1980) The Oswestry low back pain disability questionnaire. Physiotherapy 66:271–273

    CAS  PubMed  Google Scholar 

  26. Fairbank JC, Pynsent PB (2000) The Oswestry disability index. Spine 25:2940–2952 ; discussion 2952

    Article  CAS  PubMed  Google Scholar 

  27. McAlinden NM, Oei TP (2006) Validation of the quality of life inventory for patients with anxiety and depression. Compr Psychiatry 47:307–314

    Article  PubMed  Google Scholar 

  28. Zung WW, Richards CB, Short MJ (1965) Self-rating depression scale in an outpatient clinic. Further validation of the SDS. Arch Gen Psychiatry 13:508–515

    Article  CAS  PubMed  Google Scholar 

  29. Thurber S, Snow M, Honts CR (2002) The Zung Self-Rating Depression Scale: convergent validity and diagnostic discrimination. Assessment 9:401–405

    Article  PubMed  Google Scholar 

  30. Donaldson MB, Learman K, Wright A et al (2011) Factor structure and concurrent/convergent validity of the modified somatic perception questionnaire and pain beliefs instrument. J Manipulative Physiol Ther 34(1):30–36

    Article  PubMed  Google Scholar 

  31. Deyo RA, Walsh NE, Schoenfeld LS, Ramamurthy S (1989) Studies of the modified somatic perceptions Questionnaire (MSPQ) in patients with back pain: Psychometric and predictive properties. Spine (Phila Pa 1976) 14(5):507–510

    Article  CAS  Google Scholar 

  32. Rhodes A, Moreno RP, Metnitz B et al (2011) Epidemiology and outcome following post-surgical admission to critical care. Intensive Care Med 37(9):1466–1472

    Article  CAS  PubMed  Google Scholar 

  33. Bos MM, Bakhshi-Raiez F, Dekker JW et al (2013) Outcomes of intensive care unit admissions after elective cancer surgery. Eur J Surg Oncol 39(6):584–592

    Article  CAS  PubMed  Google Scholar 

  34. Haynes SR, Lawler PG (1995) An assessment of the consistency of ASA physical status classification allocation. Anaesthesia 50(3):195–199

    Article  CAS  PubMed  Google Scholar 

  35. Owens WD, Felts JA, Spitznagel EL (1978) ASA physical status classification: a study of consistency of ratings. Anaesthesia 49(4):239–243

    Article  CAS  Google Scholar 

  36. Sobol JB, Gershengorn HB, Wunsch H, Li G (2013) The surgical Apgar score is strongly associated with intensive care unit admission after high-risk intraabdominal surgery. Anesth Analg 117(2):438–446

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Vanderbilt Orthopaedic Institute, Medical Center East, South Tower - Suite 4200, Nashville, TN, 37232, USA

    Harrison F. Kay, Silky Chotai, Joseph B. Wick, David P. Stonko, Matthew J. McGirt & Clinton J. Devin

Authors
  1. Harrison F. Kay
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Silky Chotai
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Joseph B. Wick
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. David P. Stonko
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Matthew J. McGirt
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Clinton J. Devin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Clinton J. Devin.

Ethics declarations

Conflict of interest

None.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kay, H.F., Chotai, S., Wick, J.B. et al. Preoperative and surgical factors associated with postoperative intensive care unit admission following operative treatment for degenerative lumbar spine disease. Eur Spine J 25, 843–849 (2016). https://doi.org/10.1007/s00586-015-4175-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00586-015-4175-8

Keywords

Search

Navigation