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CTRI Number  CTRI/2021/09/036581 [Registered on: 16/09/2021] Trial Registered Prospectively
Last Modified On: 01/10/2021
Post Graduate Thesis  No 
Type of Trial  Observational 
Type of Study   Observational Study 
Study Design  Other 
Public Title of Study   A Pilot Study for the Collection Of Vocalized Individual Digital Cough Sounds from patients with suspected COVID-19 in India 
Scientific Title of Study   A Pilot Study for the Collection Of Vocalized Individual Digital Cough Sounds from patients with suspected COVID-19 in India  
Trial Acronym   
Secondary IDs if Any  
Secondary ID  Identifier 
NIL  NIL 
 
Details of Principal Investigator or overall Trial Coordinator (multi-center study)  
Name   
Designation   
Affiliation   
Address 




 
Phone    
Fax    
Email    
 
Details of Contact Person
Scientific Query
 
Name  Sunil Yadav 
Designation  Sr. Manager Clinical Operations 
Affiliation  Triomics Healthcare Private Limited 
Address  Villa No. D-103, Plot No. Bgh-A, UPSIDC Housing Sector, Surajpur, Gautam Buddha Nagar - 201310, India

Gautam Buddha Nagar
UTTAR PRADESH
201310
India 
Phone  9310816793  
Fax    
Email  sunil@triomics.in  
 
Details of Contact Person
Public Query
 
Name  Sunil Yadav 
Designation  Sr. Manager Clinical Operations 
Affiliation  Triomics Healthcare Private Limited 
Address  Villa No. D-103, Plot No. Bgh-A, UPSIDC Housing Sector, Surajpur, Gautam Buddha Nagar - 201310, India

Gautam Buddha Nagar
UTTAR PRADESH
201310
India 
Phone  9310816793  
Fax    
Email  sunil@triomics.in  
 
Source of Monetary or Material Support  
ResApp Health Limited Level 12, 100 Creek Street Brisbane QLD 4000 Australia  
 
Primary Sponsor  
Name  ResApp Health Limited 
Address  Level 12, 100 Creek Street Brisbane QLD 4000 Australia  
Type of Sponsor  Other [Technology Company] 
 
Details of Secondary Sponsor  
Name  Address 
NIL  NIL 
 
Countries of Recruitment     India  
Sites of Study  
No of Sites = 1  
Name of Principal Investigator  Name of Site  Site Address  Phone/Fax/Email 
Dr Mohammed Mirvaz Zulfikar   Malabar Medical College Hospital & Research Centre   Department of General Medicine, P.O.Modakkallur, Atholi, Kozhikode, KERALA, 673623
Kozhikode
KERALA 
04962701800

mohammedmzulfikar@gmail.com 
 
Details of Ethics Committee
Modification(s)  
No of Ethics Committees= 2  
Name of Committee  Approval Status 
Institutional Ethics Committee for Sehgal Nursing Home  Approved 
Institutional Ethics Committee Malabar Medical College Hospital  Approved 
 
Regulatory Clearance Status from DCGI  
Status 
Not Applicable 
 
Health Condition / Problems Studied  
Health Type  Condition 
Patients  (1) ICD-10 Condition: B972||Coronavirus as the cause of diseases classified elsewhere,  
 
Intervention / Comparator Agent  
Type  Name  Details 
 
Inclusion Criteria  
Age From  18.00 Year(s)
Age To  99.00 Year(s)
Gender  Both 
Details  1. Be aged 18 years and older;
2. Be able to provide informed consent;
3. Be willing to follow study procedures;
4. Be able to provide at least 5 coughs (voluntary
and/or spontaneous); and
5. Is either:
(i)an in-patient at a study site who are
experiencing mild to moderate symptoms of COVID-
19 based on the ICMR guidelines, and has
undergone a positive COVID-19 rt-PCR or rt-qPCR
test in the preceding 48 hours; or
(ii)at a study site and is needing a COVID-19
rt-PCR or rt-qPCR test.
 
 
ExclusionCriteria 
Details  Participant has one or more medical contraindication to voluntary cough, including the following:
1. Severe respiratory distress;
2. History of pneumothorax; Eye, chest, or abdominal surgery within 3 months of enrolling in the study;
3. Patients requiring continuous oxygen or ventilator support; and
4. Hemoptysis (coughing up of blood) within 1 month of enrolling for the study; or
5. Patients requiring continuous oxygen or ventilator support;
 
 
Method of Generating Random Sequence   Not Applicable 
Method of Concealment   Not Applicable 
Blinding/Masking   Not Applicable 
Primary Outcome  
Outcome  TimePoints 
Collection of cough sound recordings, current medical symptoms, and medical history on a single occasion from 120 COVID-19 negative or positive participants as identified by PCR.

Collection of cough sound recordings, current medical symptoms, medical history, and medical treatment information on 3 distinct occasions (day 0, day 2, day 4) from 100 individuals with a known positive COVID-19 PCR result.
To develop a post data collection algorithm to detect COVID-19 and the severity of COVID-19 and determine the accuracy of the developed algorithm with a combination of collected cough sound analysis and medical symptoms to detect COVID-19 and the severity of COVID-19 using PCR as a reference standard. 
Screening/Baseline Visit, Day 2 and Day 4. 
 
Secondary Outcome  
Outcome  TimePoints 
N/A  N/A 
 
Target Sample Size   Total Sample Size="220"
Sample Size from India="220" 
Final Enrollment numbers achieved (Total)= "Applicable only for Completed/Terminated trials"
Final Enrollment numbers achieved (India)="Applicable only for Completed/Terminated trials" 
Phase of Trial   N/A 
Date of First Enrollment (India)   22/09/2021 
Date of Study Completion (India) Applicable only for Completed/Terminated trials 
Date of First Enrollment (Global)  Date Missing 
Date of Study Completion (Global) Applicable only for Completed/Terminated trials 
Estimated Duration of Trial   Years="0"
Months="1"
Days="15" 
Recruitment Status of Trial (Global)
Modification(s)  
Open to Recruitment 
Recruitment Status of Trial (India)  Open to Recruitment 
Publication Details   N/A 
Individual Participant Data (IPD) Sharing Statement

Will individual participant data (IPD) be shared publicly (including data dictionaries)?  

Response - NO
Brief Summary  

The novel coronavirus (SARS-CoV-2) has spread rapidly around the globe and caused widescale physical, mental, and economic damage. A key component for countries to effectively navigate this pandemic is the ability to screen for COVID-19 disease and triage individuals en masse rapidly and effectively. This ability to rapidly screen for COVID-19 is beginning to emerge with widespread testing capacity, however cost and access is a major rate limiting factor – particularly for developing world countries – and ultimately these tests can’t predict disease severity. 

The technology currently exists to effectively screen for diseases such as pneumonia, asthma, and COPD using a combination of unique sound patterns contained within cough sounds and subject reported symptoms, and thus we believe the same machine learning technology can offer similar efficacy for COVID-19. If so, this screening tool would provide a rapid, safe, and low-cost mechanism to identify COVID-19 positive individuals.

This study therefore seeks to gather cough sound samples from eligible study participants and using rt-PCR and rt-qPCR as our reference standards, analyze their cough sound recordings along with subject reported symptoms to explore four hypotheses:

  1. There is a unique sound pattern observable in the cough sounds of COVID-19 positive individuals that is distinguishable from COVID-19 negative individuals and cough sounds of COVID-19 positive individuals change in a predictable manner over time

  2. An artificial intelligence algorithm with a high degree of sensitivity and specificity can be developed to indicate the presence of COVID-19 using cough sounds alone, or a combination of cough sounds and subject reported symptoms

  3. An algorithm with a high degree of sensitivity and specificity can be developed to triage COVID-19 positive individuals based on their likely need for medical treatment using longitudinal cough sounds alone, or a combination of longitudinal cough sounds and subject reported symptoms

  4. An algorithm with a high degree of sensitivity and specificity can be developed to predict the COVID-19 rt-qPCR Cycle Threshold value for COVID-19 positive individuals

 
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