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Simplified Acute Physiology Score II as Predictor of Mortality in Intensive Care Units: A Decision Curve Analysis

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  • Jérôme Allyn
  • Cyril Ferdynus
  • Michel Bohrer
  • Cécile Dalban
  • Dorothée Valance
  • Nicolas Allou

Abstract

Background: End-of-life decision-making in Intensive care Units (ICUs) is difficult. The main problems encountered are the lack of a reliable prediction score for death and the fact that the opinion of patients is rarely taken into consideration. The Decision Curve Analysis (DCA) is a recent method developed to evaluate the prediction models and which takes into account the wishes of patients (or surrogates) to expose themselves to the risk of obtaining a false result. Our objective was to evaluate the clinical usefulness, with DCA, of the Simplified Acute Physiology Score II (SAPS II) to predict ICU mortality. Methods: We conducted a retrospective cohort study from January 2011 to September 2015, in a medical-surgical 23-bed ICU at University Hospital. Performances of the SAPS II, a modified SAPS II (without AGE), and age to predict ICU mortality, were measured by a Receiver Operating Characteristic (ROC) analysis and DCA. Results: Among the 4.370 patients admitted, 23.3% died in the ICU. Mean (standard deviation) age was 56.8 (16.7) years, and median (first-third quartile) SAPS II was 48 (34–65). Areas under ROC curves were 0.828 (0.813–0.843) for SAPS II, 0.814 (0.798–0.829) for modified SAPS II and of 0.627 (0.608–0.646) for age. DCA showed a net benefit whatever the probability threshold, especially under 0.5. Conclusion: DCA shows the benefits of the SAPS II to predict ICU mortality, especially when the probability threshold is low. Complementary studies are needed to define the exact role that the SAPS II can play in end-of-life decision-making in ICUs.

Suggested Citation

  • Jérôme Allyn & Cyril Ferdynus & Michel Bohrer & Cécile Dalban & Dorothée Valance & Nicolas Allou, 2016. "Simplified Acute Physiology Score II as Predictor of Mortality in Intensive Care Units: A Decision Curve Analysis," PLOS ONE, Public Library of Science, vol. 11(10), pages 1-11, October.
    • Handle: RePEc:plo:pone00:0164828
    DOI: 10.1371/journal.pone.0164828
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    References listed on IDEAS

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    1. Andrew J. Vickers & Elena B. Elkin, 2006. "Decision Curve Analysis: A Novel Method for Evaluating Prediction Models," Medical Decision Making, , vol. 26(6), pages 565-574, November.
    2. Vickers, Andrew J, 2008. "Decision Analysis for the Evaluation of Diagnostic Tests, Prediction Models, and Molecular Markers," The American Statistician, American Statistical Association, vol. 62(4), pages 314-320.
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