Skip to main content

Advertisement

SpringerLink
Log in

PIRO-CIC model can predict mortality and futility of care in critically ill cirrhosis patients in the intensive care unit

  • Original Article
  • Published:
Hepatology International Aims and scope Submit manuscript

Abstract

Background

Dynamic assessment of critically ill patients with cirrhosis (CICs) is required for accurate prognostication.

Objective

Development of a dynamic model for prediction of mortality and decision on futility of care in CICs.

Design and setting

In a prospective cohort study, we developed the PIRO-CIC model (predisposition, injury, response, organ failure for critically ill cirrhotics)] in a derivative cohort (n = 360) and validated it (n = 240) for patients admitted to the Liver ICU.

Patients

Decompensated cirrhosis admitted to ICU. The model was developed using Cox-regression analysis, and futility was performed by decision-curve analysis.

Results

CICs aged 48 ± 11.5 years, 87% males, majority being alcoholics, were enrolled, of which 73.5% were alive at one month. Factors significant for P component were INR [hazard ratio 1.12, 95% confidence interval 1.07–1.18] and CystatinC [2.25, 1.70–2.97]; for I component were sepsis [4.69, 1.90–11.57], arterial lactate[1.40, 1.02–1.93] and alcohol as etiology [2.78, 1.85–4.18]; for R component-systemic inflammatory response syndrome [1.97, 1.14–3.42] and urine neutrophil-gelatinase-associated lipocalin [HR 2.37, 1.59–3.53]; for O component-low PaO2/FiO2 ratio and need of mechanical ventilation [7.41, 4.63–11.86]. The PIRO-CIC model predicted one-month mortality with a C-index of 0.83 in the derivation and 0.80 in the validation cohorts. It predicted futility of care better than other prognostic scores. The immediate risk of mortality increased by 39% with each unit increase in PIRO-CIC score.

Limitations

Not applicable for acute-on-chronic liver failure and patients requiring emergency liver transplant.

Conclusions

Assessment and stratification of CICs with the dynamic PIRO-CIC model could determine one-month mortality and futility in the first week. Targeted and aggressive management of coagulation, kidneys, sepsis, and severe systemic inflammation may improve outcomes of CICs.

Graphical abstract

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
Fig. 2
Fig. 3

Similar content being viewed by others

Availability of data and material

On request.

Code availability

NA.

Abbreviations

ICU:

ICU

MELD:

Model for end-stage liver disease

SOFA:

Sequential organ failure assessment

AARC:

Asian pacific association for the study of liver disease acute on chronic liver failure research consortium

INR:

International normalized ratio

CICs:

Critically ill patients with cirrhosis

PIRO:

Predisposition, injury, response, and organ failure

AKI:

Acute kidney injury; uNGAL, urine neutrophil gelatinase-associated lipocalin

CysC:

Cystatin

CKD:

Chronic kidney disease

CAD:

Coronary artery disease

KIDGO:

Kidney disease improving global outcome criteria

SIRS:

Systemic inflammatory response syndrome (SIRS)

ROC:

Receiver operating curve

AUROC:

Area under receiver operating characteristic curve

DCA:

Decision curve analysis

HR:

Hazard ratio

C-index:

Concordance index

CI:

Confidence interval

TLC:

Total leukocyte count

CTP:

Child-Turcotte Pugh

MDRD:

Modification of diet in renal disease

References

  1. Olson JC, Wendon JA, Kramer DJ, et al. Intensive care of the patient with cirrhosis. Hepatology. 2011;54:1864–1872

    Article  PubMed  Google Scholar 

  2. Cholongitas E, Senzolo M, Patch D, et al. Risk factors, sequential organ failure assessment and model for end-stage liver disease scores for predicting short term mortality in cirrhotic patients admitted to intensive care unit. Aliment Pharmacol Ther. 2006;23:883–893

    Article  CAS  PubMed  Google Scholar 

  3. Das V, Boelle PY, Galbois A, et al. Cirrhotic patients in the medical intensive care unit: early prognosis and long-term survival. Crit Care Med. 2010;38:2108–2116

    Article  PubMed  Google Scholar 

  4. Gustot T, Fernandez J, Garcia E, et al. Clinical Course of acute-on-chronic liver failure syndrome and effects on prognosis. Hepatology. 2015;62:243–252

    Article  PubMed  Google Scholar 

  5. Fu CM, Chang CH, Fan PC, et al. Prognosis of critically ill cirrhotic versus non-cirrhotic patients: a comprehensive score-matched study. BMC Anesthesiol. 2014;14:123

    Article  PubMed  PubMed Central  Google Scholar 

  6. Fernández J, Aracil C, Solà E, et al. Evaluación y tratamiento del paciente cirrótico crítico [evaluation and treatment of the critically ill cirrhotic patient]. Gastroenterol Hepatol. 2016;39(9):607–626

    Article  PubMed  Google Scholar 

  7. Maiwall R, Pasupuleti SSR, Chandel SS, Narayan A, Jain P, Mitra LG, et al. Co-orchestration of acute kidney injury and non-kidney organ failures in critically ill patients with cirrhosis. Liver Int. 2021. https://doi.org/10.1111/liv.14809

    Article  PubMed  Google Scholar 

  8. Weiss E, Saner F, Asrani SK, et al. When is a critically ill cirrhotic patient too sick to transplant? Development of consensus criteria by a multidisciplinary panel of 35 international experts. Transplantation. 2021;105:561–568

    Article  PubMed  Google Scholar 

  9. Fernández J, Saliba F. Liver transplantation in patients with ACLF and multiple organ failure: time for priority after initial stabilization. J Hepatol. 2018;69:1004–1006

    Article  PubMed  Google Scholar 

  10. Ariza X, Graupera I, Coll M, et al. Neutrophil gelatinase-associated lipocalin is a biomarker of acute-on-chronic liver failure and prognosis in cirrhosis. J Hepatol. 2016;65:57–65

    Article  CAS  PubMed  Google Scholar 

  11. Drolz A, Horvatits T, Rutter K, et al. Lactate improves prediction of short-term mortality in critically ill patients with cirrhosis: a multinational study. Hepatology. 2019;69:258–269

    Article  CAS  PubMed  Google Scholar 

  12. Choudhury A, Jindal A, Maiwall R, et al. Liver failure determines the outcome in patients of acute-on-chronic liver failure (ACLF): comparison. Hepatol Int. 2017;11:461–471

    Article  CAS  PubMed  Google Scholar 

  13. Edmark C, McPhail MJW, Bell M, Whitehouse T, Wendon J, LiFe CKB. a liver injury score to predict outcome in critically ill patients. Intensive Care Med. 2016;42:361–369

    Article  PubMed  Google Scholar 

  14. Maiwall R, Sarin SK, Kumar S, et al. Development of predisposition, injury, response, organ failure model for predicting acute kidney injury in acute on chronic liver failure. Liver Int. 2017;37:1497–1507

    Article  CAS  PubMed  Google Scholar 

  15. Jalan R, Stadlbauer V, Sen S, Cheshire L, Chang YM, Mookerjee RP. Role of predisposition, injury, response and organ failure in the prognosis of patients with acute-on-chronic liver failure: a prospective cohort study. Crit Care. 2012;16:R227

    Article  PubMed  PubMed Central  Google Scholar 

  16. Sarin SK, Choudhury A, Sharma MK, Maiwall R, Al Mahtab M, Rahman S, et al. Acute-on-chronic liver failure: consensus recommendations of the Asian Pacific association for the study of the liver (APASL): an update. Hepatol Int. 2019;13(4):353–390

    Article  PubMed  Google Scholar 

  17. Contal C, O’Quigley J. An application of changepoint methods in studying the effect of age on survival in breast cancer. Comput Stat Data Anal. 1999;30(3):253–270

    Article  Google Scholar 

  18. Levi M, van der Poll T. Coagulation and sepsis. Thromb Res. 2017;149:38–44

    Article  CAS  PubMed  Google Scholar 

  19. Kumar M, Ahmad J, Maiwall R, et al. Thromboelastography-guided blood component use in patients with cirrhosis with nonvariceal bleeding: a randomized controlled trial. Hepatology. 2020;71:235–246

    Article  PubMed  Google Scholar 

  20. Lee SA, et al. Distant organ dysfunction in acute kidney injury: a review”. Am J Kidney Dis. 2018;72:846–856. https://doi.org/10.1053/j.ajkd.2018.03.028

    Article  PubMed  PubMed Central  Google Scholar 

  21. Andrade-Oliveira V, et al. Inflammation in renal diseases: new and old players. Front Pharmacol. 2019. https://doi.org/10.3389/fphar.2019.01192

    Article  PubMed  PubMed Central  Google Scholar 

  22. Amathieu R, Al-Khafaji A, Sileanu FE, et al. Significance of oliguria in critically ill patients with chronic liver disease. Hepatology. 2017;66:1592–1600

    Article  PubMed  Google Scholar 

  23. Maiwall R, Pasupuleti SSR, Bihari C, et al. Incidence, risk factors, and outcomes of transition of acute kidney injury to chronic kidney disease in cirrhosis: a prospective cohort study. Hepatology. 2020;71:1009–1022

    Article  CAS  PubMed  Google Scholar 

  24. Xu G, Ahn J, Chang S, et al. Lipocalin-2 induces cardiomyocyte apoptosis by increasing intracellular iron accumulation. J Biol Chem. 2012;287:4808–4817

    Article  CAS  PubMed  Google Scholar 

  25. Michelena J, Altamirano J, Abraldes JG, et al. Systemic inflammatory response and serum lipopolysaccharide levels predict multiple organ failure and death in alcoholic hepatitis. Hepatology. 2015;62:762–772

    Article  CAS  PubMed  Google Scholar 

  26. Meersseman P, Langouche L, du Plessis J, et al. The intensive care unit course and outcome in acute-on-chronic liver failure are comparable to other populations. J Hepatol. 2018;69:803–809

    Article  PubMed  Google Scholar 

  27. Saliba F, Ichai P, Levesque E, Samuel D. Cirrhotic patients in the intensive care unit: prognostic markers and outcome. Curr Opin Crit Care. 2013;19:154–160

    Article  PubMed  Google Scholar 

  28. Levesque E, Saliba F, Ichaï P, Samuel D. Outcome of patients with cirrhosis requiring mechanical ventilation in intensive care unit. J Hepatol. 2014;60:570–578

    Article  CAS  PubMed  Google Scholar 

  29. Campbell J, McPeake J, Shaw M, et al. Validation and analysis of prognostic scoring systems for critically ill patients with cirrhosis admitted to intensive care unit. Crit Care. 2015;13(19):364

    Article  Google Scholar 

  30. Cardoso FS. Acute-on-chronic liver failure defined by NACSELD: the impact on prognosis and futility of care. Hepatology. 2018;68:2046

    Article  PubMed  Google Scholar 

  31. Berry P, Cheent K, Lewis H, Peck M. Navigating the benefits and burdens of life-saving treatment in severely decompensated cirrhosis: an illustrative, multisourced narrative. BMJ Case Rep. 2017. https://doi.org/10.1136/bcr-2017-219320

    Article  PubMed  PubMed Central  Google Scholar 

  32. Michard B, Artzner T, Lebas B, Besch C, Guillot M, Faitot F, et al. Liver transplantation in critically ill patients: preoperative predictive factors of post-transplant mortality to avoid futility. Clin Transplant. 2017;31(12):e13115

    Article  Google Scholar 

  33. Choudhury A, Vijayaraghavan R, Maiwall R, Kumar M, Duan Z, Yu C, et al. “First week” is the crucial period for deciding living donor liver transplantation in patients with acute-on-chronic liver failure. Hepatol Int. 2021;15(6):1376–1388

    Article  PubMed  Google Scholar 

  34. Moreau R, Jalan R, Gines P, Pavesi M, Angeli P, Cordoba J, et al. Acute-on-chronic liver failure is a distinct syndrome that develops in patients with acute decompensation of cirrhosis. Gastroenterology. 2013;144(7):1426–1437

    Article  PubMed  Google Scholar 

  35. Sarin SK, Choudhury A, Sharma MK, et al. Acute-on-chronic liver failure: consensus recommendations of the Asian Pacific association for the study of the liver (APASL): an update. Hepatol Int. 2019;13:353–390

    Article  PubMed  Google Scholar 

  36. Piano S, Singh V, Caraceni P, et al. International club of ascites global study group. epidemiology and effects of bacterial infections in patients with cirrhosis worldwide. Gastroenterology. 2019;156:1368–1380

    Article  PubMed  Google Scholar 

Download references

Funding

None to declare.

Author information

Authors and Affiliations

  1. Department of Hepatology, Institute of Liver and Biliary Sciences, New Delhi, 110070, India

    Rakhi Maiwall, Harsh Vardhan Tevethia & Shiv Kumar Sarin

  2. Department of Statistics, Mizoram University (A Central University), Pachhunga University College Campus, Aizawl, India

    Samba Siva Rao Pasupuleti

Authors
  1. Rakhi Maiwall
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Samba Siva Rao Pasupuleti
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Harsh Vardhan Tevethia
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shiv Kumar Sarin
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

RM: made the study concept and design; acquisition of data done by RM, HVT, statistical analysis done by SSP; drafting of manuscript done by RM and SSP; Critical revision of manuscript done for important intellectual content done by SKS, administrative and technical support by SKS. All authors provided final approval of the version submitted for publication.

Corresponding author

Correspondence to Shiv Kumar Sarin.

Ethics declarations

Conflict of interest

Rakhi Maiwall, Samba Siva Rao Pasupuleti, Harsh Vardhan Tevethia,Shiv Kumar Sarin declares that they have no conflict of interest..

Ethical approval

Taken.

Informed consent

Informed consent was obtained from either the patient or the legal representative from the family.

Consent for publication

All authors approved the final version of the manuscript.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Maiwall, R., Pasupuleti, S.S.R., Tevethia, H.V. et al. PIRO-CIC model can predict mortality and futility of care in critically ill cirrhosis patients in the intensive care unit. Hepatol Int 17, 476–487 (2023). https://doi.org/10.1007/s12072-022-10426-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12072-022-10426-4

Keywords

Search

Navigation