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Acad Emerg Med. 2012 Sep;19(9):993-1003. doi: 10.1111/j.1553-2712.2012.01424.x.

Validation of a clinical prediction model for early admission to the intensive care unit of patients with pneumonia.

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1
Quality of Care Unit, Grenoble University Hospital, Grenoble, France. jlabarere@chu-grenoble.fr

Abstract

OBJECTIVES:

The Risk of Early Admission to the Intensive Care Unit (REA-ICU) index is a clinical prediction model that was derived based on 4,593 patients with community-acquired pneumonia (CAP) for predicting early admission to the intensive care unit (ICU; i.e., within 3 days following emergency department [ED] presentation). This study aimed to validate the REA-ICU index in an independent sample.

METHODS:

The authors retrospectively stratified 850 CAP patients enrolled in a multicenter prospective randomized trial conducted in Switzerland, using the REA-ICU index, alternate clinical prediction models of severe pneumonia (SMART-COP, CURXO-80, and the 2007 IDSA/ATS minor severity criteria), and pneumonia severity assessment tools (the Pneumonia Severity Index [PSI] and CURB-65).

RESULTS:

  The rate of early ICU admission did not differ between the validation and derivation samples within each risk class of the REA-ICU index, ranging from 1.1% to 1.8% in risk class I to 27.1% to 27.6% in risk class IV. The areas under the receiver operating characteristic (ROC) curve were 0.76 (95% confidence interval [CI] = 0.70 to 0.83) and 0.80 (95% CI = 0.77 to 0.83) in the validation and derivation samples, respectively. In the validation sample, the REA-ICU index performed better than the pneumonia severity assessment tools, but failed to demonstrate an accuracy advantage over alternate prediction models in predicting ICU admission.

CONCLUSIONS:

The REA-ICU index reliably stratifies CAP patients into four categories of increased risk for early ICU admission within 3 days following ED presentation. Further research is warranted to determine whether inflammatory biomarkers may improve the performance of this clinical prediction model.

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