• Journal of critical care · Oct 2016

    Observational Study

    Predicting success of high-flow nasal cannula in pneumonia patients with hypoxemic respiratory failure: The utility of the ROX index.

    • Oriol Roca, Jonathan Messika, Berta Caralt, Marina García-de-Acilu, Benjamin Sztrymf, Jean-Damien Ricard, and Joan R Masclans.
    • Critical Care Department, Vall d'Hebron University Hospital, Vall d'Hebron Research Institute, Barcelona, Spain; Ciber Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, Madrid, Spain. Electronic address: oroca@vhebron.net.
    • J Crit Care. 2016 Oct 1; 35: 200-5.

    PurposeThe purpose of the study is to describe early predictors and to develop a prediction tool that accurately identifies the need for mechanical ventilation (MV) in pneumonia patients with hypoxemic acute respiratory failure (ARF) treated with high-flow nasal cannula (HFNC).Materials And MethodsThis is a 4-year prospective observational 2-center cohort study including patients with severe pneumonia treated with HFNC. High-flow nasal cannula failure was defined as need for MV. ROX index was defined as the ratio of pulse oximetry/fraction of inspired oxygen to respiratory rate.ResultsOne hundred fifty-seven patients were included, of whom 44 (28.0%) eventually required MV (HFNC failure). After 12 hours of HFNC treatment, the ROX index demonstrated the best prediction accuracy (area under the receiver operating characteristic curve 0.74 [95% confidence interval, 0.64-0.84]; P<.002). The best cutoff point for the ROX index was estimated to be 4.88. In the Cox proportional hazards model, a ROX index greater than or equal to 4.88 measured after 12 hours of HFNC was significantly associated with a lower risk for MV (hazard ratio, 0.273 [95% confidence interval, 0.121-0.618]; P=.002), even after adjusting for potential confounding.ConclusionsIn patients with ARF and pneumonia, the ROX index can identify patients at low risk for HFNC failure in whom therapy can be continued after 12 hours.Copyright © 2016 Elsevier Inc. All rights reserved.

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