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For sponsor reasons
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| Name | Class |
|---|---|
| Belarusian Medical Academy of Post-Graduate Education | OTHER |
| University of Manchester | OTHER |
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This observational study evaluates whether lung sound analysis with LungPass device can be used to differentiate upper and lower respiratory tract infections (URTI and LRTI)
This is a prospective observational study comprised patients presenting with acute respiratory tract infection symptoms in primary care settings. Patients will undergo routine clinical examination including lung auscultation with conventional stethoscope and LungPass device. Moreover, symptoms and anamnesis data, result of diagnostic questionnaire from the LungPass App, complete blood count, C-reactive protein, procalcitonin (PCT) level, SARS-CoV-2 PCR test and X-ray chest examination or CT chest (in case of preliminary clinical suspicion of LRTI). Patients will be followed up within 5-7 days and at the end of the disease in person or by phone for clinical course of the disease assessment to confirm the final diagnosis (URTI or LRTI). Final diagnosis will be considered as Gold standard for accuracy assessment.
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Lung auscultation with LungPass device (electronic auscultation) | Device | Lung auscultation with electronic stethoscope |
| Measure | Description | Time Frame |
|---|---|---|
| Accuracy of identifying LRTI by abnormal lung sounds | Accuracy of differentiation between ULRTI and LRTI by abnormal lung sounds detected with LungPass device | 16.11.2021- 30.11.2021 |
| Measure | Description | Time Frame |
|---|---|---|
| Accuracy of identifying LRTI by abnormal lung sounds in subgroups with COVID-19 and non-COVID-19 LRTI | Accuracy of differentiation between ULRTI and LRTI by abnormal lung sounds detected with LungPass device in subgroups with COVID-19 and non-COVID-19 LRTI | 16.11.2021- 30.11.2021 |
| Lung sounds and clinical signs of LRTI correlation in patients with LRTI |
| Measure | Description | Time Frame |
|---|---|---|
| Accuracy of LRTI detection by LungPass App output | Accuracy of of identifying LRTI by LungPass App output which based on abnormal lung sounds detected with LungPass device together with symptoms collected using a questionnaire in the LungPass app*. * Final LungPass App output is possible patient condition (acute respiratory infection, acute bronchitis, pneumonia, etc) | 16.11.2021- 30.11.2021 |
Inclusion Criteria:
Exclusion Criteria:
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Participants will be recruited in primary care settings upon presentation with symptoms of acute respiratory infection.
It is planned to start recruiting 100 adult patients and 100 children with URTI and 100 adults and 100 children with LRTI. The final calculation of the sample size will be carried out after receiving the preliminary results of the study. After that, the sample size will be adjusted.
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| Name | Affiliation | Role |
|---|---|---|
| Helena Binetskaya | CEO | Study Director |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| 19th Сentral District Polyclinic of Pervomaisky District of Minsk | Minsk | 220114 | Belarus |
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Correlation between presence of pathological pulmonary sounds detected with LungPass device and presence of clinical signs of LRTI (clinical parameters/radiology results) in patients with LRTI |
| 16.11.2021- 30.11.2021 |
| Accuracy of bacterial LRTI detection by abnormal lung sounds using procalcitonin level as Gold Standard | Accuracy of identifying bacterial LRTI by abnormal lung sounds detected with LungPass device using procalcitonin level as Gold Standard | 16.11.2021- 30.11.2021 |