Tuberculosis
Conditions
Keywords
electronic-nose, breath test, tuberculosis, screening, gender, remote area
Brief summary
An electronic-nose (e-nose) is being investigated as a diagnostic tool for tuberculosis by examining exhaled breath of the patients. Universitas Gadjah Mada has developed an e-nose device for TB diagnostic tool. Here the investigators test the device in order to analyze the sensitivity and specificity electronic-nose as a screening tool for tuberculosis particularly in remote area. Various factors (gender, age, race, and location) are considered in the algorithm training to develop an inclusive eNose. Access barriers, especially those faced by women, are also assessed.
Detailed description
The study population consists of 2 groups: Group 1 - presumptive TB patients. Group 2 - residents of area with high risk of TB. Study participants provide written informed consent. The participants are asked to breathe normally using a mask for 2 times then inhale and exhale in a forced expiratory volume to the air collecting bag until the collecting bag is full (two times). The collecting bag is sealed and connected to the e-nose machine via a collecting hose and HEPA-filter protecting the e-nose from microbes. The breath pattern will be recorded in the e-nose device, which is connected to a laptop that will display the recorded breath pattern. Other data are collected: clinical symptoms, results of chest X-ray, smear microscopic, and Xpert MTB/Rif examinations. Demographic (gender, age, race, and location) and clinical data (symptoms, physical examination, laboratory examinations) are collected. Access barriers, especially for women, are also assessed through questionnaire and interviews.
Interventions
The participants are requested to quietly sit and breathe to the air collecting bag until the collecting bag is full. The collecting bag is sealed and connected to the e-nose machine via a collecting hose and HEPA-filter. The data are read and stored in the e-nose machine.
Sponsors
Gadjah Mada University
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
SCREENING
Masking
NONE
Masking description
The research subjects, breath sample takers, and laboratory sample examiners do not know the results of each sampling that has been done. The final data processor is also blinded to the results of Xpert or other laboratory examinations. The breath sampling data is saved in graphic form which interpretation will be carried out later by the data processor at the final stage.
Intervention model description
The Validation phase involves presumptive TB patients (Group 1) and the Screening phase involves residents of area with high risk of TB (Group 2)
Eligibility
Sex/Gender
ALL
Age
4 Years to No maximum
Healthy volunteers
Yes
Inclusion criteria
\- Inclusion Criteria: Validation Phase (Group 1): * Adult and children * Suspected of having TB * Agree to participate in the study * Able to produce exhaled air samples * Able to produce samples for Xpert MTB/Rif examination Screening Phase (Group 2): * Adult and children * Agree to participate in the study * Able to produce exhaled air samples * Currently not in TB treatment \-
Exclusion criteria
* Invalid measurements of breath tests * Incomplete CXR data * Missing specimens * Unable to breath normally for 2 minutes due to respiratory illness
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Diagnostic accuracy of electronic nose signal in screening tuberculosis that is measured through sensitivity, specificity, PPV, NPV | 2 years | sensitivity, specificity, positive predictive value, negative predictive value of e-nose signal in diagnosing TB, factors influencing the diagnostic accuracy |
| Access barriers, measured through questionnaire, interview, and focus group discussion | 2 years | access barriers to the TB screening that are measured through questionnaire, interview, and focus group discussion |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Time of a screening algorithm with eNose-TB, measured in days | 2 years | Time of a screening algorithm with eNose-TB to obtain additional detection of one TB case, measured in days |
Countries
Indonesia
Outcome results
None listed