• Marc Moss, S David White, Heather Warner, Daniel Dvorkin, Daniel Fink, Stephanie Gomez-Taborda, Carrie Higgins, Gintas P Krisciunas, Joseph E Levitt, Jeffrey McKeehan, Edel McNally, Alix Rubio, Rebecca Scheel, Jonathan M Siner, Rosemary Vojnik, and Susan E Langmore.
• Division of Pulmonary Sciences and Critical Care Medicine, University of Colorado Denver, Aurora, CO. Electronic address: marc.moss@CUAnschutz.edu.
• Chest. 2020 Nov 1; 158 (5): 1923-1933.

BackgroundThe bedside swallowing evaluation (BSE) is an assessment of swallowing function and airway safety during swallowing. After extubation, the BSE often is used to identify the risk of aspiration in acute respiratory failure (ARF) survivors.Research QuestionWe conducted a multicenter prospective study of ARF survivors to determine the accuracy of the BSE and to develop a decision tree algorithm to identify aspiration risk.Study Design And MethodsPatients extubated after ≥ 48 hours of mechanical ventilation were eligible. Study procedures included the BSE followed by a gold standard evaluation, the flexible endoscopic evaluation of swallowing (FEES).ResultsOverall, 213 patients were included in the final analysis. Median time from extubation to BSE was 25 hours (interquartile range, 21-45 hours). The FEES was completed 1 hour after the BSE (interquartile range, 0.5-2 hours). A total of 33% (70/213; 95% CI, 26.6%-39.2%) of patients aspirated on at least one FEES bolus consistency test. Thin liquids were the most commonly aspirated consistency: 27% (54/197; 95% CI, 21%-34%). The BSE detected any aspiration with an accuracy of 52% (95% CI, 45%-58%), a sensitivity of 83% (95% CI, 74%-92%), and negative predictive value (NPV) of 81% (95% CI, 72%-91%). Using recursive partitioning analyses, a five-variable BSE-based decision tree algorithm was developed that improved the detection of aspiration with an accuracy of 81% (95% CI, 75%-87%), sensitivity of 95% (95% CI, 90%-98%), and NPV of 97% (95% CI, 95%-99%).InterpretationThe BSE demonstrates variable accuracy to identify patients at high risk for aspiration. Our decision tree algorithm may enhance the BSE and may be used to identify patients at high risk for aspiration, yet requires further validation.Trial RegistryClinicalTrials.gov; No.: NCT02363686; URL: www.clinicaltrials.gov.Copyright © 2020 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.

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