Clinical trial NCT06637774: The Aspirometer: A Noninvasive Tool for Detecting Aspiration Aim 3
Official title: The Aspirometer: A Noninvasive Tool for Detecting Aspiration
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: NA
Conditions: Deglutition Disorders
Keywords: dysphagia
artificial intelligence
machine learning
Lead sponsor: University of Pittsburgh
Sponsor class: OTHER
Collaborators: name: University of Toronto
name: Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD)
First posted: 2024-10-15
Enrollment: 50
Sex: ALL
Minimum age (years): 18
Maximum age (years): 100
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: NA
Intervention model: SINGLE_GROUP
Intervention model description: Prospective study of patients referred for videofluoroscopic swallow study, consenting to parallel data collection of signals. No control group.
Primary purpose: SCREENING

Brief summary: This investigation evaluates the effectiveness of a device called the Aspirometer, which uses high resolution cervical auscultation (HRCA), in detecting when food or liquids enter the airway (aspiration) of the person swallowing, whether the person swallowing shows signs of aspiration (coughing) or not.

Detailed description: This aim of the project seeks to discriminate normal from abnormal airway protection and kinematic functions noninvasively via machine-learning analysis of Aspirometer/HRCA (high resolution cervical auscultation) signals, with similar accuracy as human judgment of VF. Hypothesis: Advanced data analytics can detect pathological airway protection in HRCA signal signatures with 90% accuracy when compared to a human expert's airway protection ratings from VF images. Analytical algorithms that can learn from data (e.g., Bayes' learning) will be used to infer about the continuum of abnormal airway protection during swallowing.

Eligibility criteria: Inclusion Criteria:

- referred by an attending physician as an inpatient, for a modified barium swallow examination due to suspected dysphagia and aspiration

Exclusion Criteria:

* tracheostomy
* pregnant
* unable to follow verbal commands
* anatomic disruption of head neck
* radiation therapy to head or neck

Primary outcomes:
measure: Numbers of Participants for Which the Aspirometer/HRCA Predicted the Passed Versus Failed Swallow Test Result
timeFrame: day 1
measure: Percentage of PAS Scores That Were Accurately Predicted by the Aspirometer/HRCA, in Comparison to Modified Barium Swallow Test.
timeFrame: day 1
Secondary outcomes:
Other outcomes:
Locations:
city: Pittsburgh
country: United States
facility: University of Pittsburgh Medical Center Oakland campus
state: Pennsylvania
Study officials:
affiliation: University of Pittsburgh
name: James Coyle
role: PRINCIPAL_INVESTIGATOR
References:
citation: Khalifa Y, Mahoney AS, Lucatorto E, Coyle JL, Sejdic E. Non-Invasive Sensor-Based Estimation of Anterior-Posterior Upper Esophageal Sphincter Opening Maximal Distension. IEEE J Transl Eng Health Med. 2023 Feb 20;11:182-190. doi: 10.1109/JTEHM.2023.3246919. eCollection 2023.
citation: Mahoney AS, Khalifa Y, Lucatorto E, Sejdic E, Coyle JL. Cervical Vertebral Height Approximates Hyoid Displacement in Videofluoroscopic Images of Healthy Adults. Dysphagia. 2022 Dec;37(6):1689-1696. doi: 10.1007/s00455-022-10414-8. Epub 2022 Mar 1.
citation: Schwartz R, Khalifa Y, Lucatorto E, Perera S, Coyle J, Sejdic E. A Preliminary Investigation of Similarities of High Resolution Cervical Auscultation Signals Between Thin Liquid Barium and Water Swallows. IEEE J Transl Eng Health Med. 2021 Dec 10;10:4900109. doi: 10.1109/JTEHM.2021.3134926. eCollection 2022.
citation: Shu K, Coyle JL, Perera S, Khalifa Y, Sabry A, Sejdic E. Anterior-posterior distension of maximal upper esophageal sphincter opening is correlated with high-resolution cervical auscultation signal features. Physiol Meas. 2021 Apr 6;42(3). doi: 10.1088/1361-6579/abe7cb.
citation: Donohue C, Khalifa Y, Perera S, Sejdic E, Coyle JL. A Preliminary Investigation of Whether HRCA Signals Can Differentiate Between Swallows from Healthy People and Swallows from People with Neurodegenerative Diseases. Dysphagia. 2021 Aug;36(4):635-643. doi: 10.1007/s00455-020-10177-0. Epub 2020 Sep 5.
citation: Sejdic E, Malandraki GA, Coyle JL. Computational deglutition: Signal and image processing methods to understand swallowing and associated disorders. IEEE Signal Process Mag. 2019 Jan;36(1):138-146. doi: 10.1109/MSP.2018.2875863. Epub 2018 Dec 25. No abstract available.
citation: Khalifa Y, Donohue C, Coyle JL, Sejdic E. Autonomous Swallow Segment Extraction Using Deep Learning in Neck-Sensor Vibratory Signals From Patients With Dysphagia. IEEE J Biomed Health Inform. 2023 Feb;27(2):956-967. doi: 10.1109/JBHI.2022.3224323. Epub 2023 Feb 3.
citation: Donohue C, Khalifa Y, Mao S, Perera S, Sejdic E, Coyle JL. Characterizing Swallows From People With Neurodegenerative Diseases Using High-Resolution Cervical Auscultation Signals and Temporal and Spatial Swallow Kinematic Measurements. J Speech Lang Hear Res. 2021 Sep 14;64(9):3416-3431. doi: 10.1044/2021_JSLHR-21-00134. Epub 2021 Aug 24.
citation: Donohue C, Khalifa Y, Mao S, Perera S, Sejdic E, Coyle JL. Establishing Reference Values for Temporal Kinematic Swallow Events Across the Lifespan in Healthy Community Dwelling Adults Using High-Resolution Cervical Auscultation. Dysphagia. 2022 Jun;37(3):664-675. doi: 10.1007/s00455-021-10317-0. Epub 2021 May 20.
citation: Sabry A, Mahoney AS, Mao S, Khalifa Y, Sejdic E, Coyle JL. Automatic Estimation of Laryngeal Vestibule Closure Duration Using High- Resolution Cervical Auscultation Signals. Perspect ASHA Spec Interest Groups. 2020 Dec;5(6):1647-1656. doi: 10.1044/2020_persp-20-00073. Epub 2020 Dec 14.
citation: Mao S, Sabry A, Khalifa Y, Coyle JL, Sejdic E. Estimation of laryngeal closure duration during swallowing without invasive X-rays. Future Gener Comput Syst. 2021 Feb;115:610-618. doi: 10.1016/j.future.2020.09.040. Epub 2020 Sep 30.
citation: Coyle JL, Sejdic E. High-Resolution Cervical Auscultation and Data Science: New Tools to Address an Old Problem. Am J Speech Lang Pathol. 2020 Jul 10;29(2S):992-1000. doi: 10.1044/2020_AJSLP-19-00155. Epub 2020 Jul 10.
citation: Khalifa Y, Coyle JL, Sejdic E. Non-invasive identification of swallows via deep learning in high resolution cervical auscultation recordings. Sci Rep. 2020 May 26;10(1):8704. doi: 10.1038/s41598-020-65492-1.
citation: Donohue C, Mao S, Sejdic E, Coyle JL. Tracking Hyoid Bone Displacement During Swallowing Without Videofluoroscopy Using Machine Learning of Vibratory Signals. Dysphagia. 2021 Apr;36(2):259-269. doi: 10.1007/s00455-020-10124-z. Epub 2020 May 17.
citation: Zhang Z, Perera S, Donohue C, Kurosu A, Mahoney AS, Coyle JL, Sejdic E. The Prediction of Risk of Penetration-Aspiration Via Hyoid Bone Displacement Features. Dysphagia. 2020 Feb;35(1):66-72. doi: 10.1007/s00455-019-10000-5. Epub 2019 Mar 27.