Development of an Accurate Bedside Swallowing Evaluation Decision Tree Algorithm for Detecting Aspiration in Acute Respiratory Failure Survivors.
Airway Extubation
/ adverse effects
Algorithms
Decision Trees
Deglutition
Female
Humans
Male
Point-of-Care Testing
Predictive Value of Tests
Reproducibility of Results
Respiration, Artificial
/ methods
Respiratory Aspiration
/ diagnosis
Respiratory Insufficiency
/ diagnosis
Risk Assessment
Survivors
/ statistics & numerical data
Symptom Assessment
/ methods
United States
/ epidemiology
acute respiratory failure
aspiration
dysphagia
intubation
speech language pathologist
Journal
Chest
ISSN: 1931-3543
Titre abrégé: Chest
Pays: United States
ID NLM: 0231335
Informations de publication
Date de publication:
11 2020
11 2020
Historique:
received:
27
01
2020
revised:
06
07
2020
accepted:
12
07
2020
pubmed:
30
7
2020
medline:
28
5
2021
entrez:
30
7
2020
Statut:
ppublish
Résumé
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. 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. 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). 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%). 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. ClinicalTrials.gov; No.: NCT02363686; URL: www.clinicaltrials.gov.
Sections du résumé
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.
Identifiants
pubmed: 32721404
pii: S0012-3692(20)32076-6
doi: 10.1016/j.chest.2020.07.051
pmc: PMC7674978
pii:
doi:
Banques de données
ClinicalTrials.gov
['NCT02363686']
Types de publication
Clinical Trial
Journal Article
Multicenter Study
Langues
eng
Sous-ensembles de citation
IM
Pagination
1923-1933Subventions
Organisme : NHLBI NIH HHS
ID : K24 HL089223
Pays : United States
Organisme : NINR NIH HHS
ID : R21 NR015886
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR001863
Pays : United States
Commentaires et corrections
Type : CommentIn
Informations de copyright
Copyright © 2020 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.
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