Cross-validation of a Sequential Organ Failure Assessment score-based model to predict mortality in patients with cancer admitted to the intensive care unit

Marylou Cárdenas-Turanzas, Joe Ensor, Chris Wakefield, Karen Zhang, Susannah Kish Wallace, Kristen J. Price, Joseph L. Nates

Research output: Contribution to journalArticle

23 Scopus citations

Abstract

Purpose: This study aims to validate the performance of the Sequential Organ Failure Assessment (SOFA) score to predict death of critically ill patients with cancer. Material and methods: We conducted a retrospective observational study including adults admitted to the intensive care unit (ICU) between January 1, 2006, and December 31, 2008. We randomly selected training and validation samples in medical and surgical admissions to predict ICU and in-hospital mortality. By using logistic regression, we calculated the probabilities of death in the training samples and applied them to the validation samples to test the goodness-of-fit of the models, construct receiver operator characteristics curves, and calculate the areas under the curve (AUCs). Results: In predicting mortality at discharge from the unit, the AUC from the validation group of medical admissions was 0.7851 (95% confidence interval [CI], 0.7437-0.8264), and the AUC from the surgical admissions was 0.7847 (95% CI, 0.6319-0.937). The AUCs of the SOFA score to predict mortality in the hospital after ICU admission were 0.7789 (95% CI, 0.74-0.8177) and 0.7572 (95% CI, 0.6719-0.8424) for the medical and surgical validations groups, respectively. Conclusions: The SOFA score had good discrimination to predict ICU and hospital mortality. However, the observed underestimation of ICU deaths and unsatisfactory goodness-of-fit test of the model in surgical patients to indicate calibration of the score to predict ICU mortality is advised in this group.

Original languageEnglish (US)
Pages (from-to)673-680
Number of pages8
JournalJournal of Critical Care
Volume27
Issue number6
DOIs
StatePublished - Dec 2012

Keywords

  • Cancer
  • Critically ill
  • Mortality
  • Probability
  • ROC curve analysis
  • Validity of results

ASJC Scopus subject areas

  • Critical Care and Intensive Care Medicine

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