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J Crit Care. 2012 Dec;27(6):740.e1-7. doi: 10.1016/j.jcrc.2012.08.017. Epub 2012 Oct 9.

Predicting in-hospital mortality among critically ill patients with end-stage liver disease.

Author information

1
Division of Pulmonary and Critical Care, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA. Alex.Balekian@med.usc.edu

Abstract

PURPOSE:

Critically-ill patients with end-stage liver disease (ESLD) are at high risk for death during intensive care unit hospitalization, and currently available prognostic models have limited accuracy in this population. We aimed to identify variables associated with in-hospital mortality among critically ill ESLD patients and to develop and validate a simple, parsimonious model for bedside use.

MATERIALS AND METHODS:

We performed a retrospective chart review of 653 intensive care unit admissions for ESLD patients; modeled in-hospital mortality using multivariable logistic regression; and compared the predictive ability of several different models using the area under receiver operating characteristic (AU-ROC) curves.

RESULTS:

Multivariable predictors of in-hospital mortality included Model for End-stage Liver Disease (MELD) score, Acute Physiology and Chronic Health Evaluation (APACHE) II score, mechanical ventilation, and gender; there was also an interaction between MELD score and gender (P < .02). MELD alone had better discrimination (AU-ROC 0.83) than APACHE II alone (AU-ROC 0.76), and adding mechanical ventilation to MELD achieved the single largest increase in model discrimination (AU-ROC 0.85; P < .01). In a parsimonious, 2-predictor model, higher MELD scores (OR 1.14 per 1-point increase; 95% CI 1.11-1.16), and mechanical ventilation (OR 6.20; 95% CI 3.05-12.58) were associated with increased odds of death. Model discrimination was also excellent in the validation cohort (AU-ROC 0.90).

CONCLUSIONS:

In critically ill ESLD patients, a parsimonious model including only MELD and mechanical ventilation is more accurate than APACHE II alone for predicting in-hospital mortality. This simple bedside model can provide clinicians and patients with valuable prognostic information for medical decision-making.

PMID:
23059012
PMCID:
PMC4405501
DOI:
10.1016/j.jcrc.2012.08.017
[Indexed for MEDLINE]
Free PMC Article

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