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A prognostic model for 1-month mortality in the postoperative intensive care unit

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Abstract

Purposes

Recognizing which patients admitted postsurgically to the intensive care unit (ICU) are at greater risk of mortality assists medical staff to identify who will benefit most from the care. We developed a prediction model for the 1-month mortality of postsurgical ICU patients.

Methods

From May, 2019 to May, 2020, we conducted a prospective cohort study in the postsurgical ICU of a teaching hospital affiliated with our University of Medical Sciences. The outcome was death within 1 month of admission and the predictors were a variety of anthropometric and clinical features. The subjects of this analysis were 805 consecutive adult postsurgical patients with a mean (SD) age of 54.8 (18.9) years.

Results

Overall, the resulted logistic model was well-fitted [χ2 (26) = 772.097, p < 0.001, Nagelkerke R2 = 0.814] accurate (88%), and specific (92%). The adjusted odds ratio for body temperature was 0.51, p < 0.001. Patients with comorbidities and those undergoing multiple operations were at a greater risk of mortality, odds = 10.00 and 10.65 (both p < 0.001).

Conclusions

Higher body temperature at the time of postoperative ICU admission is a protective factor against 1-month mortality. Our study found that patients with several comorbidities and those who have undergone multiple operations are at a greater risk of a poor outcome.

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References

  1. Sun Y, Li S, Wang S, Li C, Li G, Xu J, et al. Predictors of 1-year mortality in patients on prolonged mechanical ventilation after surgery in intensive care unit: a multicenter, retrospective cohort study. BMC Anesthesiol. 2020;20:44.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Soares Pinheiro FGM, Santana Santos E, Barreto IDC, Weiss C, Vaez AC, Oliveira JC, et al. Mortality predictors and associated factors in patients in the intensive care unit: a cross-sectional study. Crit Care Res Pract. 2020;2020:1483827.

    PubMed  PubMed Central  Google Scholar 

  3. Maley JH, Wanis KN, Young JG, Celi LA. Mortality prediction models, causal effects, and end-of-life decision making in the intensive care unit. BMJ Health Care Inf. 2020. https://doi.org/10.1136/bmjhci-2020-100220.

    Article  Google Scholar 

  4. Tan EM, Kashyap R, Olson IC, O’Horo JC. Validation of a retrospective computing model for mortality risk in the intensive care unit. Mayo Clin Proc Innov Qual Outcomes. 2020;4:575–82.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Tak Kyu O, Ji E, Ahn S, Kim DJ, Song IA. Admission to surgical intensive care unit in time with intensivist coverage and its association with postoperative 30-day mortality: the role of intensivists in a surgical intensive care unit. Anaesth Crit Care Pain Med. 2019;38:259–63.

    Article  PubMed  Google Scholar 

  6. Markazi-Moghaddam N, Jame SZB, Tofighi E. Evaluating patient flow in the operating theater: an exploratory data analysis of length of stay components. Inf Med Unlocked. 2020;19:100354.

    Article  Google Scholar 

  7. Markazi-Moghaddam N, Fathi M, Ramezankhani A. Risk prediction models for intensive care unit readmission: a systematic review of methodology and applicability. Aust Crit Care. 2020;33:367–74.

    Article  PubMed  Google Scholar 

  8. Miller PE, Thomas A, Breen TJ, Chouairi F, Kunitomo Y, Aslam F, et al. Prevalence of non-cardiac multimorbidity in patients admitted to two cardiac intensive care units and their association with mortality. Am J Med. 2021;134:653-661.e5.

    Article  PubMed  Google Scholar 

  9. Investigators P-S. Six-month mortality and functional outcomes in aneurysmal sub-arachnoid haemorrhage patients admitted to intensive care units in Australia and New Zealand: a prospective cohort study. J Clin Neurosci. 2020;80:92–9.

    Article  Google Scholar 

  10. Fahim M, Visser RA, Dijksman LM, Biesma DH, Noordzij PG, Smits AB. Routine postoperative intensive care unit admission after colorectal cancer surgery for the elderly patient reduces postoperative morbidity and mortality. Colorectal Dis. 2020;22:408–15.

    Article  CAS  PubMed  Google Scholar 

  11. Banda J, Chenga N, Nambaya S, Bulaya T, Siziya S. Predictors of acute kidney injury and mortality in intensive care unit at a teaching tertiary hospital_ID. Indian J Crit Care Med. 2020;24:116–21.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Mazzeffi M, Zivot J, Buchman T, Halkos M. In-hospital mortality after cardiac surgery: patient characteristics, timing, and association with postoperative length of intensive care unit and hospital stay. Ann Thorac Surg. 2014;97:1220–5.

    Article  PubMed  Google Scholar 

  13. Uscinska E, Sobkowicz B, Lisowska A, Sawicki R, Dabrowska M, Szmitkowski M, et al. Predictors of long-term mortality in patients hospitalized in an intensive cardiac care unit. Int Heart J. 2016;57:67–72.

    Article  CAS  PubMed  Google Scholar 

  14. Vasilevskis EE, Kuzniewicz MW, Cason BA, Lane RK, Dean ML, Clay T, et al. Predictors of early postdischarge mortality in critically ill patients: a retrospective cohort study from the California Intensive Care Outcomes project. J Crit Care. 2011;26:65–75.

    Article  PubMed  Google Scholar 

  15. Ulvik A, Wentzel-Larsen T, Flaatten H. Trauma patients in the intensive care unit: short- and long-term survival and predictors of 30-day mortality. Acta Anaesthesiol Scand. 2007;51:171–7.

    Article  CAS  PubMed  Google Scholar 

  16. Oh TK, Ji E, Na HS, Min B, Jeon YT, Do SH, et al. C-reactive protein to albumin ratio predicts 30-day and 1-year mortality in postoperative patients after admission to the intensive care unit. J Clin Med. 2018;7:39.

    Article  PubMed Central  Google Scholar 

  17. Ediboglu O, Kirakli SC, Yazicioglu Mocin O, Gungor G, Anar C, Cimen P, et al. Predictors of mortality in cancer patients who need intensive care unit support: a two center cohort study. Turk J Med Sci. 2018;48:744–9.

    Article  PubMed  Google Scholar 

  18. Diaz-Diaz D, Villanova Martinez M, Palencia Herrejon E. Oncological patients admitted to an intensive care unit. Analysis of predictors of in-hospital mortality. Med Intensiv. 2018;42:346–53.

    Article  CAS  Google Scholar 

  19. Renuka MK, Kalaiselvan MS, Arunkumar AS. An analysis of the predictors of mortality and morbidity in patients admitted after suicidal hanging to an Indian multidisciplinary Intensive Care Unit. Indian J Anaesth. 2017;61:538–42.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Elsayem AF, Merriman KW, Gonzalez CE, Yeung SC, Chaftari PS, Reyes-Gibby C, et al. Presenting symptoms in the emergency department as predictors of intensive care unit admissions and hospital mortality in a comprehensive cancer center. J Oncol Pract. 2016;12:e554–63.

    Article  PubMed  Google Scholar 

  21. Fathi M, Markazi-Moghaddam N, Ramezankhani A. A systematic review on risk factors associated with sepsis in patients admitted to intensive care units. Aust Crit Care. 2019;32:155–64.

    Article  PubMed  Google Scholar 

  22. Kim J, Oh TK, Lee J, Kim S, Song IA. Association of immediate postoperative temperature in the surgical intensive care unit with 1-year mortality: retrospective analysis using digital axillary thermometers. Acute Crit Care. 2019;34:53–9.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Sandri M, Zuccolotto P. Variable selection using random forests. Berlin: Springer; 2006. p. 263–70.

    Google Scholar 

  24. Miller ME, Hui SL, Tierney WM. Validation techniques for logistic regression models. Stat Med. 1991;10:1213–26.

    Article  CAS  PubMed  Google Scholar 

  25. Karalapillai D, Story DA, Calzavacca P, Licari E, Liu YL, Hart GK. Inadvertent hypothermia and mortality in postoperative intensive care patients: retrospective audit of 5050 patients. Anaesthesia. 2009;64:968–72.

    Article  CAS  PubMed  Google Scholar 

  26. Kongsayreepong S, Chaibundit C, Chadpaibool J, Komoltri C, Suraseranivongse S, Suwannanonda P, et al. Predictor of core hypothermia and the surgical intensive care unit. Anesth Analg. 2003;96:826–33.

    Article  PubMed  Google Scholar 

  27. Frank SM, Fleisher LA, Breslow MJ, Higgins MS, Olson KF, Kelly S, et al. Perioperative maintenance of normothermia reduces the incidence of morbid cardiac events. A randomized clinical trial. JAMA. 1997;277:1127–34.

    Article  CAS  PubMed  Google Scholar 

  28. Abelha FJ, Castro MA, Neves AM, Landeiro NM, Santos CC. Hypothermia in a surgical intensive care unit. BMC Anesthesiol. 2005;5:7.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Stretch R, Della Penna N, Celi LA, Landon BE. Effect of boarding on mortality in ICUs. Crit Care Med. 2018;46:525–31.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Kim MM, Barnato AE, Angus DC, Fleisher LA, Kahn JM. The effect of multidisciplinary care teams on intensive care unit mortality. Arch Intern Med. 2010;170:369–76.

    Article  PubMed  PubMed Central  Google Scholar 

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Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations

  1. Critical Care Quality Improvement Research Center, Shahid Modarres Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran

    Mohammad Fathi & Nader Markazi Moghaddam

  2. Department of Anesthesiology, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran

    Mohammad Fathi

  3. Department of Health Management and Economics, Faculty of Medicine, AJA University of Medical Sciences, Shahid Etemadzadeh St., Western Fatemi, Tehran, 1411718541, Iran

    Nader Markazi Moghaddam

  4. Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran

    Saba Naderian Jahromi

Authors
  1. Mohammad Fathi
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  2. Nader Markazi Moghaddam
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Contributions

MF conceived the original idea, conceptualized and supervised the study, and helped with developing the protocols and interpretation of the results. NMM contributed to the concept and design of the study, literature review, data analyses, and interpretation of the results. SNJ coordinated the research process, supervised data record and entry, and helped in literature review and statistical analyses. All the authors participated in drafting and its final approval.

Corresponding author

Correspondence to Nader Markazi Moghaddam.

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Conflict of interest

We have no competing interests to declare.

Ethical approval

Shahid Beheshti University of Medical Sciences with the ethics code of IR.SBMU.RETECH.REC.1397.533.

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Fathi, M., Moghaddam, N.M. & Jahromi, S.N. A prognostic model for 1-month mortality in the postoperative intensive care unit. Surg Today 52, 795–803 (2022). https://doi.org/10.1007/s00595-021-02391-6

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  • DOI: https://doi.org/10.1007/s00595-021-02391-6

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