ArticleBMJ open respiratory research2017
Listening panel agreement and characteristics of lung sounds digitally recorded from children aged 1-59 months enrolled in the Pneumonia Etiology Research for Child Health (PERCH) case-control study.
Article in BMJ open respiratory research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 22 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
The BLAAAST Trial: Bangladesh Lung Auscultation AI for Antibiotic Stewardship Randomized Controlled Trial
The LalelaLung Study: Digital Stethoscope Clinical Evaluation
Who cites it
22 citing papers in PubMed, 32 citations in OpenAlex.
- Digital stethoscope use by non-physician primary care health workers on children under five years of age in rural Bangladesh: a feasibility study.Scientific reports · 2026Article
- Performance of an artificial intelligence algorithm for interpreting lung sounds from children hospitalised with pneumonia in Malawi.Journal of global health · 2025Article
- A MEMS seismometer respiratory monitor for work of breathing assessment and adventitious lung sounds detection via deep learning.Scientific reports · 2025Article
- Breaking ICD Codes: Identifying Ambiguous Respiratory Infection Codes via Regional Diagnosis Heterogeneity.Annals of family medicine · 2025Article
- Robust Anomaly Detection of Adventitious Auscultation Signals using Bayesian Belief Tracking.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2024Article
- Exploring the link between a novel approach for computer aided lung sound analysis and imaging biomarkers: a cross-sectional study.Respiratory research · 2024Article
- Feasibility and Acceptability of Pediatric Smartphone Lung Auscultation by Parents: Cross-Sectional Study.JMIR pediatrics and parenting · 2024Article
- Reference free auscultation quality metric and its trends.Biomedical signal processing and control · 2023Article
- Constrained Synthetic Sampling for Augmentation of Crackle Lung Sounds.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2023Article
- A machine learning approach to the development and prospective evaluation of a pediatric lung sound classification model.Scientific reports · 2023Article
- Digitally recorded and remotely classified lung auscultation compared with conventional stethoscope classifications among children aged 1-59 months enrolled in the Pneumonia Etiology Research for Child Health (PERCH) case-control study.BMJ open respiratory research · 2022Article
- Derivation and validation of a novel risk assessment tool to identify children aged 2-59 months at risk of hospitalised pneumonia-related mortality in 20 countries.BMJ global health · 2022Article
- Observational
- Back to Basics in Paediatric Pneumonia-Defining a Breath and Setting Reference Standards to Innovate Respiratory Rate Counting.Journal of tropical pediatrics · 2021Article
- Diagnostic Errors Are Common in Acute Pediatric Respiratory Disease: A Prospective, Single-Blinded Multicenter Diagnostic Accuracy Study in Australian Emergency Departments.Frontiers in pediatrics · 2021Article
- Real-World Verification of Artificial Intelligence Algorithm-Assisted Auscultation of Breath Sounds in Children.Frontiers in pediatrics · 2021Article
- Digital auscultation in PERCH: Associations with chest radiography and pneumonia mortality in children.Pediatric pulmonology · 2020Article
- Factors affecting airway compliance and resistance in children receiving general anesthesia during adenotonsillectomy.Medicine · 2020Observational
- Diagnosis and management of community-acquired pneumonia in children: South African Thoracic Society guidelines.African journal of thoracic and critical care medicine · 2020Article
- Challenges in the diagnosis of paediatric pneumonia in intervention field trials: recommendations from a pneumonia field trial working group.The Lancet. Respiratory medicine · 2019Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
24 authors at 15 institutions in 8 countries.
Funding
Abstract
introductionPaediatric lung sound recordings can be systematically assessed, but methodological feasibility and validity is unknown, especially from developing countries. We examined the performance of acoustically interpreting recorded paediatric lung sounds and compared sound characteristics between cases and controls.
methodsPneumonia Etiology Research for Child Health staff in six African and Asian sites recorded lung sounds with a digital stethoscope in cases and controls. Cases aged 1-59 months had WHO severe or very severe pneumonia; age-matched community controls did not. A listening panel assigned examination results of normal, crackle, wheeze, crackle and wheeze or uninterpretable, with adjudication of discordant interpretations. Classifications were recategorised into any crackle, any wheeze or abnormal (any crackle or wheeze) and primary listener agreement (first two listeners) was analysed among interpretable examinations using the prevalence-adjusted, bias-adjusted kappa (PABAK). We examined predictors of disagreement with logistic regression and compared case and control lung sounds with descriptive statistics.
resultsPrimary listeners considered 89.5% of 792 case and 92.4% of 301 control recordings interpretable. Among interpretable recordings, listeners agreed on the presence or absence of any abnormality in 74.9% (PABAK 0.50) of cases and 69.8% (PABAK 0.40) of controls, presence/absence of crackles in 70.6% (PABAK 0.41) of cases and 82.4% (PABAK 0.65) of controls and presence/absence of wheeze in 72.6% (PABAK 0.45) of cases and 73.8% (PABAK 0.48) of controls. Controls, tachypnoea, >3 uninterpretable chest positions, crying, upper airway noises and study site predicted listener disagreement. Among all interpretable examinations, 38.0% of cases and 84.9% of controls were normal (p<0.0001); wheezing was the most common sound (49.9%) in cases.
conclusionsListening panel and case-control data suggests our methodology is feasible, likely valid and that small airway inflammation is common in WHO pneumonia. Digital auscultation may be an important future pneumonia diagnostic in developing countries.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.