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Development of an Accurate Bedside Swallowing Evaluation Decision Tree Algorithm for Detecting Aspiration in Acute Respiratory Failure Survivors.
Moss, Marc; White, S David; Warner, Heather; Dvorkin, Daniel; Fink, Daniel; Gomez-Taborda, Stephanie; Higgins, Carrie; Krisciunas, Gintas P; Levitt, Joseph E; McKeehan, Jeffrey; McNally, Edel; Rubio, Alix; Scheel, Rebecca; Siner, Jonathan M; Vojnik, Rosemary; Langmore, Susan E.
Afiliación
  • Moss M; Division of Pulmonary Sciences and Critical Care Medicine, University of Colorado Denver, Aurora, CO. Electronic address: marc.moss@CUAnschutz.edu.
  • White SD; University of Colorado Denver Rehabilitation Therapy Services, University of Colorado Hospital, Aurora, CO.
  • Warner H; Section of Otolaryngology, Department of Surgery, Yale School of Medicine, New Haven, CT; Department of Communication Disorders, Southern Connecticut State University, New Haven, CT.
  • Dvorkin D; Division of Pulmonary Sciences and Critical Care Medicine, University of Colorado Denver, Aurora, CO; The Bioinformatics CRO, Inc, Niceville, FL.
  • Fink D; Department of Otolaryngology, University of Colorado School of Medicine, Aurora, CO.
  • Gomez-Taborda S; Department of Otolaryngology, Boston Medical Center, Boston, MA.
  • Higgins C; Division of Pulmonary Sciences and Critical Care Medicine, University of Colorado Denver, Aurora, CO.
  • Krisciunas GP; Department of Otolaryngology, Boston Medical Center, Boston, MA; Department of Otolaryngology, Boston University School of Medicine, Boston, MA.
  • Levitt JE; Division of Pulmonary and Critical Care, Stanford University, Stanford, CA.
  • McKeehan J; Division of Pulmonary Sciences and Critical Care Medicine, University of Colorado Denver, Aurora, CO.
  • McNally E; Department of Otolaryngology, Boston Medical Center, Boston, MA; Sargent College of Health and Rehabilitation Sciences, Boston University, Boston, MA.
  • Rubio A; Department of Otolaryngology, Boston Medical Center, Boston, MA.
  • Scheel R; Sargent College of Health and Rehabilitation Sciences, Boston University, Boston, MA; Division of Speech Language Pathology, Massachusetts General Hospital, Boston, MA.
  • Siner JM; Section of Pulmonary, Critical Care, and Sleep Medicine, Yale School of Medicine, New Haven, CT.
  • Vojnik R; Division of Pulmonary and Critical Care, Stanford University, Stanford, CA.
  • Langmore SE; Department of Otolaryngology, Boston Medical Center, Boston, MA; Department of Otolaryngology, Boston University School of Medicine, Boston, MA; Sargent College of Health and Rehabilitation Sciences, Boston University, Boston, MA.
Chest ; 158(5): 1923-1933, 2020 11.
Article en En | MEDLINE | ID: mdl-32721404
ABSTRACT

BACKGROUND:

The 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 QUESTION We 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

METHODS:

Patients 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).

RESULTS:

Overall, 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%).

INTERPRETATION:

The 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 REGISTRY ClinicalTrials.gov; No. NCT02363686; URL www.clinicaltrials.gov.
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Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Insuficiencia Respiratoria / Deglución / Aspiración Respiratoria / Extubación Traqueal / Evaluación de Síntomas / Pruebas en el Punto de Atención Tipo de estudio: Clinical_trials / Diagnostic_studies / Etiology_studies / Health_economic_evaluation / Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Female / Humans / Male País/Región como asunto: America do norte Idioma: En Revista: Chest Año: 2020 Tipo del documento: Article

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Insuficiencia Respiratoria / Deglución / Aspiración Respiratoria / Extubación Traqueal / Evaluación de Síntomas / Pruebas en el Punto de Atención Tipo de estudio: Clinical_trials / Diagnostic_studies / Etiology_studies / Health_economic_evaluation / Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Female / Humans / Male País/Región como asunto: America do norte Idioma: En Revista: Chest Año: 2020 Tipo del documento: Article