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Respiratory rate counters in paediatric pneumonia diagnosis
Population Size
Not reported
Years
2021
Associated BioSamples
None/not available
Geographic coverage
Bangladesh
Lead time
Other
Summary
The study aims to develop a process of videography of child’s chest movements and interpretation of respiratory rate from video recording by a video expert panel to evaluate automated respiratory rate counters to diagnose paediatric pneumonia.
Documentation
Fast breathing is the most common sign of childhood pneumonia. It is identified by observing the child’s chest and counting respiratory rate (RR). However, manual count of RR is challenging for the health workers often resulting in misdiagnosis of pneumonia. The availability of novel RR counters (e.g., ChARM, Masimo Rad-G, uPM60) can support health workers by detecting fast breathing automatically. However, the absence of an appropriate reference standard to evaluate the performance of these devices is a challenge. The commonly used reference standard is manual RR counts by a human expert which might be biased. If good quality videos could be captured and RR interpretation from these videos could be systematically conducted, it could be an ideal and non-biased reference standard. This study is designed to develop a video expert panel (VEP) as a reference standard to evaluate automated RR counters to identify pneumonia in children. The study will assess the performance of ChARM device in terms of accuracy of counting RR, duration to take the count and any potential influence of the device on RR counts. The study will record the child’s chest movements, and the recorded videos will be interpreted by the VEP. A mechanism to interpret RR from the recorded videos and maintain ongoing quality control will be established. This study will be carried out in Bangladesh in two phases. Eligible children will be 0-2 months old presenting with any illness and 2-59 months old presenting with suspected pneumonia (cough and/or breathing difficulty). In Phase-I, we will develop a process of videography of the child’s chest movements and interpretation of RR by a VEP. We will establish the process of capturing good quality videos, making a set of reference videos and will train and standardise the VEP members using those reference videos. We will record videos from a hospital in Dhaka. We will take videos of child’s chest movements both with and without using of ChARM. In Phase-II, we will conduct a cross-sectional study in rural Sylhet for evaluation of the performance of ChARM device. We will enrol children presenting at first level health facilities and a sub-district hospital. This study will provide evidence to establish the videography of child’s chest movements and its interpretation by a VEP as an appropriate and non-biased reference standard to evaluate the performance of novel RR counters. For further information please visit https://www.ed.ac.uk/usher/respire/phd-studentships/ahad-mahmud-khan.
Dataset type
Health and disease
Dataset sub-type
Not applicable
Keywords
pneumonary, video expert, respiratory rate counter, automated counter, children
Observations
Observed Node | Disambiguating Description | Measured Value | Measured Property | Observation Date |
|---|---|---|---|---|
Findings | 1 | Count | 01 Dec 2021 |
Provenance
Purpose of dataset collection
Study
Source of data extraction
Paper-based
Collection source setting
Primary care - Clinic, Secondary care - Outpatients, Secondary care - In-patients
Image contrast
Not stated
Biological sample availability
None/not available
Details
Publishing frequency
Static
Version
1.0.0
Modified
08/10/2024
Citation Requirements
RESPIRE Collaboration
Coverage
Start date
01/12/2021
Time lag
Not applicable
Geographic coverage
Bangladesh
Maximum age range
5
Accessibility
Language
en
Controlled vocabulary
LOCAL
Format
csv, mp4
Data Access Request
Dataset pipeline status
Not available
Access rights
Time to dataset access
Other
Access request cost
None
Access method category
TRE/SDE
Access service description
Datashare
Jurisdiction
BD, GB-GBN
Data Controller
University of Edinburgh
Data Processor
Projahnmo Research Foundation