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Acoustic Cough Monitoring for the Management of Patients With Known Respiratory Disease

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ClinicalTrials.gov Identifier: NCT05042063
Recruitment Status : Recruiting
First Posted : September 13, 2021
Last Update Posted : November 10, 2021
Sponsor:
Collaborators:
Centre de Recherche du Centre Hospitalier de l'Université de Montréal
Hyfe, Inc
Information provided by (Responsible Party):
Clinica Universidad de Navarra, Universidad de Navarra

Brief Summary:
This study pretends to evaluate the potential use of Hyfe Cough Tracker (Hyfe) to screen for, diagnose, and support the clinical management of patients with respiratory diseases, while enriching a dataset of disease-specific annotated coughs, for further refinement of similar systems.

Condition or disease Intervention/treatment
Cough COPD GERD Asthma Tuberculosis Non-Tuberculous Mycobacterial Pneumonia COVID-19 Pneumonia Device: Hyfe Cough Tracker Device: Hyfe Air

Detailed Description:

This is an observational study that will take place in the two campuses of the Clínica Universidad de Navarra, located in Pamplona and Madrid (Spain).

An Artificial-Intelligence system (AI) that detects and records explosive putative cough sounds and identifies human cough based on acoustic characteristics will be used to automatically monitor cough. Potential participants either attending the outpatient clinic or hospitalised with a complaint of cough will be invited by their treating physician, or a member of the research team, and included in the study by part of the research team. A researcher will instruct participants on how to install and use Hyfe Cough Tracker in their smartphones. Participants will be monitored for 30 days (outpatients) or until discharged from the hospital (inpatients). Participants will be asked to complete a daily, online, standardised 100 mm visual analogue scale (VAS) to register changes in the subjective intensity of their cough, while using Hyfe to objectively monitor changes in its frequency.

In parallel, a dataset of annotated cough sounds will be constructed and retrospectively used to assess differences in acoustic patterns of cough, and to evaluate the performance of the system detecting them.

A first subgroup of participants will be recruited outside the clinical setting and asked to provide a series of elicited sounds, including coughs, which will then be used to determine the system's performance accurately discriminating coughs from non-cough sounds, and compared to trained human listeners.

A second subgroup of participants will be will be instructed to use Hyfe, and the related Hyfe Air wearable device continuously for a period between 6 and 24 hours, while they record themselves using a MP3 recorder connected to a lapel microphone. This group will be used to evaluate the performance of Hyfe and Hyfe Air in a real-life setting, with spontaneous coughs.

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Study Type : Observational
Estimated Enrollment : 100 participants
Observational Model: Cohort
Time Perspective: Prospective
Official Title: Acoustic Cough Monitoring for the Management of Patients With Known Respiratory Disease
Actual Study Start Date : September 15, 2021
Estimated Primary Completion Date : April 15, 2022
Estimated Study Completion Date : September 15, 2026

Resource links provided by the National Library of Medicine

MedlinePlus related topics: Cough

Group/Cohort Intervention/treatment
Participants with cough as a symptom
This group will be composed of patients at the Clínica Universidad de Navarra that complain of having cough as a remarkable symptom.
Device: Hyfe Cough Tracker
Hyfe Cough Tracker is a digital acoustic surveillance system that uses an artificial intelligence system to discriminate cough from non-cough sounds. Hyfe is an AI-enabled mobile app that records short snippets (<0.5 seconds) of putative cough explosive sounds and then classifies them as cough or non-cough using a convolutional neural network (CNN) model. Briefly, the acoustic characteristics of recorded sounds are converted into an image file, which is then processed by an algorithm trained to identify graphical differences in images. This creates an adjustable prediction score, with values above it, resulting in a sound being classified as "cough", and those below being classified as "non-cough.

Validation subgroup 1
This subgroup will be composed by both, patients belonging to the main study group, as well as voluntaries, who will be asked to provide a series of elicited cough and non-cough sounds for validation purposes.
Device: Hyfe Cough Tracker
Hyfe Cough Tracker is a digital acoustic surveillance system that uses an artificial intelligence system to discriminate cough from non-cough sounds. Hyfe is an AI-enabled mobile app that records short snippets (<0.5 seconds) of putative cough explosive sounds and then classifies them as cough or non-cough using a convolutional neural network (CNN) model. Briefly, the acoustic characteristics of recorded sounds are converted into an image file, which is then processed by an algorithm trained to identify graphical differences in images. This creates an adjustable prediction score, with values above it, resulting in a sound being classified as "cough", and those below being classified as "non-cough.

Validation subgroup 2
This subgroup will be composed by inpatients admitted to the Clínica Universidad de Navarra with a diagnosis of respiratory disease, or presenting cough as a symptom, as well as healthy individuals. This group will be monitored with Hyfe Cough Tracker and Hyfe Air for a variable period of 6-24 hours, while they are recorded with a MP3 recorder connected to a lapel microphone.
Device: Hyfe Cough Tracker
Hyfe Cough Tracker is a digital acoustic surveillance system that uses an artificial intelligence system to discriminate cough from non-cough sounds. Hyfe is an AI-enabled mobile app that records short snippets (<0.5 seconds) of putative cough explosive sounds and then classifies them as cough or non-cough using a convolutional neural network (CNN) model. Briefly, the acoustic characteristics of recorded sounds are converted into an image file, which is then processed by an algorithm trained to identify graphical differences in images. This creates an adjustable prediction score, with values above it, resulting in a sound being classified as "cough", and those below being classified as "non-cough.

Device: Hyfe Air
Hyfe Air is a wearable device with an incorporated wireless lapel microphone. The device´s recordings can be run through the same cough-detection algorithm used by Hyfe Cough Tracker, while its results are directly stored in a remote database and are not displayed to participants.




Primary Outcome Measures :
  1. Correlation between subjective perception of cough and objective frequency [ Time Frame: 6 months. ]
    The daily VAS score of participants will be compared to the cough frequency registered by the cough surveillance system. These data will be used to fit a linear regression model to compare self-reported VAS scores to daily cough frequency and calculate a correlation coefficient (r).


Secondary Outcome Measures :
  1. Sensitivity of the system discriminating coughs [ Time Frame: 6 months. ]
    The sensitivity of Hyfe for the discrimination of coughs from other explosive sounds will be compared to that of trained human listeners. Sensitivity will be reported as the proportion of sounds correctly identified as coughs (true positives), from the total cough sounds produced (true positives + false negatives).

  2. Specificity of the system discriminating coughs [ Time Frame: 6 months. ]
    The specificity of Hyfe for the discrimination of coughs from other explosive sounds will be compared to that of trained human listeners. Specificity will be defined as the proportion of non-cough sounds correctly identified by the system (true negatives) from the total non-cough sounds produced (true negatives + false positives)

  3. Positive predictive value (PPV) of the system discriminating coughs [ Time Frame: 6 months. ]
    The PPV of Hyfe for the discrimination of coughs from other explosive sounds will be compared to that of trained human listeners. PPV will be defined as the proportion of cough sounds correctly identified by the system (true positives) from the total sounds labelled as coughs (true positives + false positives).

  4. Negative predictive value (NPV) of the system discriminating coughs [ Time Frame: 6 months. ]
    The NPV of Hyfe for the discrimination of coughs from other explosive sounds will be compared to that of trained human listeners. NPV will be defined as the proportion of non-cough sounds correctly identified by the system (true negatives) from the total of sounds labelled as non-coughs (true negatives+ false negatives).

  5. Construction of an annotated cough dataset [ Time Frame: 5 years. ]
    Cough registries of participants with an etiologic diagnosis will be annotated and stored to create a dataset that can be used for further algorithm training and refinement.

  6. Sensitivity of the system differentiating coughs caused by different conditions [ Time Frame: 5 years. ]
    The records obtained from participants for which an etiologic diagnosis is reached before the end of the study will be analysed to detect differential acoustic patterns, which will in turn be used to train the system's convolutional neural network to perform respiratory disease cough classification. The performance of this system will be retrospectively evaluated by determining its sensitivity for the diagnosis of different respiratory conditions, compared to clinical diagnoses made by a physician. Sensitivity will be defined as the proportion of participants in which Hyfe reaches a correct diagnoses based on cough acoustic patterns (true positives) from the total number of participants diagnosed with a certain condition (true positives + false negatives).

  7. Specificity of the system differentiating coughs caused by different conditions [ Time Frame: 5 years. ]
    The records obtained from participants for which an etiologic diagnosis is reached before the end of the study will be analysed to detect differential acoustic patterns, which will in turn be used to train the system's convolutional neural network to perform respiratory disease cough classification. The performance of this system will be retrospectively evaluated by determining its specificity for the diagnosis of different respiratory conditions, compared to clinical diagnoses made by a physician. Specificity will be defined as the proportion of participants in which Hyfe correctly identifies the absence of acoustic cough patterns associated to a certain disease (true negatives), from the total of participants without that specific condition (true negatives+ false positives).


Biospecimen Retention:   Samples Without DNA
Voice records of participants taking part in the validation sub-studies.


Information from the National Library of Medicine

Choosing to participate in a study is an important personal decision. Talk with your doctor and family members or friends about deciding to join a study. To learn more about this study, you or your doctor may contact the study research staff using the contacts provided below. For general information, Learn About Clinical Studies.


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Ages Eligible for Study:   5 Years to 100 Years   (Child, Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   Yes
Sampling Method:   Non-Probability Sample
Study Population

For the main study group, the population are patients with respiratory disease treated in the Clínica Universidad de Navarra (Pamplona and Madrid campuses).

Since the validation sub-study 1 only requires elicited sounds, a group of participants from a previous study will be directly invited to participate. For the validation sub-study 2, participants will include both, inpatients admitted to the Clínica Universidad de Navarra and presenting cough, and healthy individuals directly invited to participate by the study team.

Criteria

Inclusion Criteria:

For participants in the main study group

  • Outpatient or inpatients at the Clinical Universidad de Navarra with a complaint of cough.
  • The patient or his/her legal representative, have given consent to participate in the study.

For participants in the sub-study groups:

  • Being 18 years or older.
  • Providing consent for the sub-study

Exclusion Criteria:

  • Inability to accept the privacy policy and terms of use of Hyfe.
  • Lack of access to a Wi-Fi network at the site of residence (for the main study group).
  • Unwillingness to regularly use the cough-surveillance system throughout the monitoring period

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT05042063


Contacts
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Contact: Carlos Chaccour, MD, PhD +34948255400 cchaccour@unav.es
Contact: Juan Gabaldon, MD, MSc jgabaldonfi@unav.es

Locations
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Spain
Clinica Universidad de Navarra Recruiting
Pamplona, Navarra, Spain, 31008
Contact: Carlos Chaccour, MD       carlos.chaccour@isglobal.org   
Contact: Juan Gabaldon, MD       jgabaldonfi@unav.es   
Sponsors and Collaborators
Clinica Universidad de Navarra, Universidad de Navarra
Centre de Recherche du Centre Hospitalier de l'Université de Montréal
Hyfe, Inc
Investigators
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Principal Investigator: Carlos Chaccour, MD, PhD Clinica Universidad de Navarra
  Study Documents (Full-Text)

Documents provided by Clinica Universidad de Navarra, Universidad de Navarra:
Informed Consent Form  [PDF] October 4, 2021

Publications:

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Responsible Party: Clinica Universidad de Navarra, Universidad de Navarra
ClinicalTrials.gov Identifier: NCT05042063    
Other Study ID Numbers: PI_2021/72
First Posted: September 13, 2021    Key Record Dates
Last Update Posted: November 10, 2021
Last Verified: September 2021
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: Yes
Plan Description: Datasets with anonymized IPD, including cough registries and VAS scores will be shared at the end of the study.
Supporting Materials: Study Protocol
Informed Consent Form (ICF)
Clinical Study Report (CSR)
Time Frame: Data will become available at the completion of the study (2026) and will remain available from that moment onward.
Access Criteria: Upon request to researchers

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Additional relevant MeSH terms:
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Pneumonia
Tuberculosis
Cough
Respiration Disorders
Respiratory Tract Diseases
Respiratory Tract Infections
Infections
Lung Diseases
Mycobacterium Infections
Actinomycetales Infections
Gram-Positive Bacterial Infections
Bacterial Infections
Bacterial Infections and Mycoses
Signs and Symptoms, Respiratory