Digital auscultation in PERCH: Associations with chest radiography and pneumonia mortality in children

McCollum, Eric D.; Park, Daniel E.; Watson, Nora; Fancourt, Nicholas; Focht, Christopher; Baggett, Henry C.; Brooks, W. Abdullah; Howie, Stephen R. C.; Kotloff, Karen L.; Levine, Orin S.; Madhi, Shabir A.; Murdoch, David R.; Scott, J. Anthony G.; Thea, Donald M; Awori, Juliet O.; Chipeta, James; Chuananon, Somchai; DeLuca, Andrea; Driscoll, Amanda J.; Ebruke, Bernard E.; Elhilali, Mounya; Emmanouilidou, Dimitra; Githua, Louis Peter; Higdon, Melissa M.; Hossain, Lokman; Jahan, Yasmin; Karron, Ruth A.; Kyalo, Joshua; Moore, David P.; Mulindwa, Justin; Naorat, Sathapana; Prosperi, Christine; Verwey, Charl; West, James E.; Knoll, Maria Deloria; O’Brien, Katherine L.; Feikin, Daniel R.; Hammitt, Laura L.

doi:10.1002/ppul.25046

Cited by 15 publications

(16 citation statements)

References 21 publications

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“…We have shown that lung sound classifications of digital auscultation recordings generated using a listening panel approach has strong associations with radiographic findings and also mortality outcomes 48 .…”

Section: Discussionmentioning

confidence: 94%

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A study protocol for community use of digital auscultation to improve diagnosis of paediatric pneumonia in Bangladesh

Ahmed

Mitra

Nair

et al. 2021

Preprint

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IntroductionThe World Health Organisation’s Integrated Management of Childhood Illnesses (IMCI) algorithm relies on counting respiratory rate and observing respiratory distress to diagnose childhood pneumonia. IMCI performs with high sensitivity but low specificity, leading to over-diagnosis of child pneumonia and unnecessary antibiotic use. Including lung auscultation in IMCI could improve pneumonia diagnosis. Our objectives are: (i) assess lung sound recording quality by primary health care workers (HCWs) from under-five children with the Feelix Smart Stethoscope; and (ii) determine the reliability and performance of recorded lung sound interpretations by an automated algorithm compared to reference paediatrician interpretations.Methods and analysisIn a cross-sectional design, Community HCWs will record lung sounds of ∼1,000 under-five-year-old children with suspected pneumonia at first-level facilities in Zakiganj sub-district, Sylhet, Bangladesh. Enrolled children will be evaluated for pneumonia, including oxygen saturation, and have their lung sounds recorded by the Feelix Smart stethoscope at four sequential chest locations: two back and two front positions. A novel sound-filtering algorithm will be applied to recordings to address ambient noise and optimize recording quality. Recorded sounds will be assessed against a pre-defined quality threshold. A trained paediatric listening panel will classify recordings into one of the following categories: normal, crackle, wheeze, crackle and wheeze, or uninterpretable. All sound files will be classified into the same categories by the automated algorithm and compared with panel classifications.ConclusionsLung auscultation and reliable interpretation of lung sounds of children are usually not feasible in first-level facilities in Bangladesh and other low- and middle-income countries (LMICs). Incorporating automated lung sound classification within the current IMCI pneumonia diagnostic algorithm may improve childhood pneumonia diagnostic accuracy at LMIC first-level facilities.Ethics and disseminationEthical review has been obtained in Bangladesh (BMRC Registration Number: 09630012018) and in Edinburgh, Scotland, United Kingdom (REC Reference: 18-HV-051). Dissemination will be through conference presentations, peer-reviewed journals and stakeholder engagement meetings in Bangladesh.Trial registration numberNCT03959956Article summayStrengths and limitations of this studyEvaluating the quality of lung sound recordings in a first-level facility where auscultation is usually unavailable and challenging to obtain due to a typically crowded and noisy environment and providers may not get enough time to calm the child due to time pressure from a high-volume patient.This study will assess the feasibility of recording lung sounds by front line community health workers who do not usually use conventional stethoscopes during clinical care.Two standardised paediatricians masked to the child’s clinical status will independently classify the recorded lung sounds, and a third masked and independent paediatrician will arbitrate any discrepancies.A machine-learning algorithm developed by Johns Hopkins and Sonavi Labs will detect abnormal lung sounds and be compared with classifications by human listeners/paediatricians.The study will not have chest radiography findings of enrolled children, which is considered by many a gold standard for pneumonia diagnosis, as chest radiography is not available at this level of the health system in Bangladesh. Instead, this study will measure the peripheral oxyhaemoglobin saturation and evaluate clinical examination findings, including respiratory danger signs data.

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Section: Discussionmentioning

confidence: 94%

“…In this study we will utilize the paediatrician listening panel as our gold standard reference as previously described 40 . We have shown that lung sound classifications of digital auscultation recordings generated using a listening panel approach has strong associations with radiographic findings and also mortality outcomes 48 .…”

Section: Discussionmentioning

confidence: 94%

A study protocol for community use of digital auscultation to improve diagnosis of paediatric pneumonia in Bangladesh

Ahmed

Mitra

Nair

et al. 2021

Preprint

Self Cite

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“…40 We have shown that lung sound classifications of digital auscultation recordings generated using a listening panel approach have strong associations with radiographic findings and also mortality outcomes. 48 Open access work ranges from additionally refining this device, based on lessons learnt from the use of the device at the first-level facility during this study as well as phases 1 and 2 clinical trials that prospectively integrate the digital auscultation into the WHO pneumonia management pathway to evaluate patient outcomes after digital auscultation-based decision-making. Eventually, if these preliminary studies are successful, a large multicountry clinical trial could be designed to evaluate the safety and efficacy of digital auscultation to improve pneumonia management.…”

Section: Discussionmentioning

confidence: 99%

Digital auscultation as a novel childhood pneumonia diagnostic tool for community clinics in Sylhet, Bangladesh: protocol for a cross-sectional study

et al. 2022

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IntroductionThe WHO’s Integrated Management of Childhood Illnesses (IMCI) algorithm for diagnosis of child pneumonia relies on counting respiratory rate and observing respiratory distress to diagnose childhood pneumonia. IMCI case defination for pneumonia performs with high sensitivity but low specificity, leading to overdiagnosis of child pneumonia and unnecessary antibiotic use. Including lung auscultation in IMCI could improve specificity of pneumonia diagnosis. Our objectives are: (1) assess lung sound recording quality by primary healthcare workers (HCWs) from under-5 children with the Feelix Smart Stethoscope and (2) determine the reliability and performance of recorded lung sound interpretations by an automated algorithm compared with reference paediatrician interpretations.Methods and analysisIn a cross-sectional design, community HCWs will record lung sounds of ~1000 under-5-year-old children with suspected pneumonia at first-level facilities in Zakiganj subdistrict, Sylhet, Bangladesh. Enrolled children will be evaluated for pneumonia, including oxygen saturation, and have their lung sounds recorded by the Feelix Smart stethoscope at four sequential chest locations: two back and two front positions. A novel sound-filtering algorithm will be applied to recordings to address ambient noise and optimise recording quality. Recorded sounds will be assessed against a predefined quality threshold. A trained paediatric listening panel will classify recordings into one of the following categories: normal, crackles, wheeze, crackles and wheeze or uninterpretable. All sound files will be classified into the same categories by the automated algorithm and compared with panel classifications. Sensitivity, specificity and predictive values, of the automated algorithm will be assessed considering the panel’s final interpretation as gold standard.Ethics and disseminationThe study protocol was approved by the National Research Ethics Committee of Bangladesh Medical Research Council, Bangladesh (registration number: 09630012018) and Academic and Clinical Central Office for Research and Development Medical Research Ethics Committee, Edinburgh, UK (REC Reference: 18-HV-051). Dissemination will be through conference presentations, peer-reviewed journals and stakeholder engagement meetings in Bangladesh.Trial registration numberNCT03959956.

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“…Ajay C. Kevat MBBS, MMed, MPH, FRACP 1,2 Fazeah Marzbanrad BSc, MSc, PhD 3 Robert Roseby MBBS, FRACP 4,5…”

Section: Conflict Of Interestsmentioning

confidence: 99%

“…The authors of the recently published article “Digital auscultation in PERCH: Associations with chest radiography and pneumonia mortality in children” 1 deserve to be congratulated on their exploration of an innovative approach to the longstanding clinical conundrum of how best to differentiate potentially life‐threatening pneumonia from less severe childhood respiratory illnesses. Utilizing digital stethoscope (DS) technology, they collected breath sound samples from several hundred unwell young children across six different developing nations.…”

mentioning

confidence: 99%

Making digital auscultation accessible and accurate

Kevat

Marzbanrad

Roseby

2020

Pediatric Pulmonology

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The authors of the recently published article "Digital auscultation in PERCH: Associations with chest radiography and pneumonia mortality in children" 1 deserve to be congratulated on their exploration of an innovative approach to the longstanding clinical conundrum of how best to differentiate potentially life-threatening pneumonia from less severe childhood respiratory illnesses. Utilizing digital stethoscope (DS) technology, they collected breath sound samples from several hundred unwell young children across six different developing nations. With a panel of listening pediatricians, these sounds were classified as being normal, or containing wheeze and/or crackles. Breath sounds, in combination with World Health Organi

show abstract

Digital auscultation in PERCH: Associations with chest radiography and pneumonia mortality in children

Cited by 15 publications

References 21 publications

A study protocol for community use of digital auscultation to improve diagnosis of paediatric pneumonia in Bangladesh

A study protocol for community use of digital auscultation to improve diagnosis of paediatric pneumonia in Bangladesh

Digital auscultation as a novel childhood pneumonia diagnostic tool for community clinics in Sylhet, Bangladesh: protocol for a cross-sectional study

Making digital auscultation accessible and accurate

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