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

Salahuddin Ahmed, Dipak Kumar Mitra, Harish Nair, Steven Cunningham, Ahad Mahmud Khan, Asmd Ashraful Islam, Ian Mitra McLane, Nabidul Haque Chowdhury, Nazma Begum, Mohammod Shahidullah, Muhammad Shariful Islam, John Norrie, Harry Campbell, Aziz Sheikh, Abdullah H Baqui, Eric D McCollum, Salahuddin Ahmed, Dipak Kumar Mitra, Harish Nair, Steven Cunningham, Ahad Mahmud Khan, Asmd Ashraful Islam, Ian Mitra McLane, Nabidul Haque Chowdhury, Nazma Begum, Mohammod Shahidullah, Muhammad Shariful Islam, John Norrie, Harry Campbell, Aziz Sheikh, Abdullah H Baqui, Eric D McCollum

Abstract

Introduction: The 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 analysis: In 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 dissemination: The 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 number: NCT03959956.

Keywords: community child health; paediatric infectious disease & immunisation; respiratory infections.

Conflict of interest statement

Competing interests: IMM will be paid for developing Feelix Smart Stethoscope and a machine learning algorithm from Sonavi Labs.

© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY. Published by BMJ.

Figures

Figure 1
Figure 1
Feelix Smartscope.
Figure 2
Figure 2
Study site.
Figure 3
Figure 3
Lung sounds recording positions.
Figure 4
Figure 4
Study flow.

References

    1. Liu L, Oza S, Hogan D, et al. . Global, regional, and national causes of under-5 mortality in 2000-15: an updated systematic analysis with implications for the sustainable development goals. Lancet 2016;388:3027–35. 10.1016/S0140-6736(16)31593-8
    1. UNICEF StC, Every Breath Counts . Every child’s right to survive: a 2020 agenda to end pneumonia deaths (Online), 2020. Available: [Accessed 23 Oct 2021].
    1. McAllister DA, Liu L, Shi T, et al. . Global, regional, and national estimates of pneumonia morbidity and mortality in children younger than 5 years between 2000 and 2015: a systematic analysis. Lancet Glob Health 2019;7:e47–57. 10.1016/S2214-109X(18)30408-X
    1. Cilla G, Oñate E, Perez-Yarza EG, et al. . Viruses in community-acquired pneumonia in children aged less than 3 years old: high rate of viral coinfection. J Med Virol 2008;80:1843–9. 10.1002/jmv.21271
    1. Havers FP, Fry AM, Goswami D. Population-Based incidence of childhood pneumonia associated with viral infections in Bangladesh. Pediatr Infect Dis J 2018.
    1. Naheed A, Saha SK, Breiman RF, et al. . Multihospital surveillance of pneumonia burden among children aged <5 years hospitalized for pneumonia in Bangladesh. Clin Infect Dis 2009;48 Suppl 2:S82–9. 10.1086/596485
    1. Saha S, Hasan M, Kim L, et al. . Epidemiology and risk factors for pneumonia severity and mortality in Bangladeshi children <5 years of age before 10-valent pneumococcal conjugate vaccine introduction. BMC Public Health 2016;16:1233. 10.1186/s12889-016-3897-9
    1. World Health Organization . Technical bases for the who recommendations on the management of pneumonia in children at first-level health facilities 1991.
    1. Tulloch J. Integrated approach to child health in developing countries. Lancet 1999;354 Suppl 2:SII16–20. 10.1016/s0140-6736(99)90252-0
    1. World Health Organization . Who IMCI chart booklet 2014. Geneva: World Health Organisation, 2014.
    1. Theodoratou E, Al-Jilaihawi S, Woodward F, et al. . The effect of case management on childhood pneumonia mortality in developing countries. Int J Epidemiol 2010;39 Suppl 1:i155–71. 10.1093/ije/dyq032
    1. Arifeen SE, Hoque DME, Akter T, et al. . Effect of the integrated management of childhood illness strategy on childhood mortality and nutrition in a rural area in Bangladesh: a cluster randomised trial. The Lancet 2009;374:393–403. 10.1016/S0140-6736(09)60828-X
    1. Chowdhury EK, El Arifeen S, Rahman M, et al. . Care at first-level facilities for children with severe pneumonia in Bangladesh: a cohort study. Lancet 2008;372:822–30. 10.1016/S0140-6736(08)61166-6
    1. Armstrong Schellenberg JRM, Adam T, Mshinda H, et al. . Effectiveness and cost of facility-based integrated management of childhood illness (IMCI) in Tanzania. Lancet 2004;364:1583–94. 10.1016/S0140-6736(04)17311-X
    1. Sazawal S, Black RE, Pneumonia Case Management Trials Group . Effect of pneumonia case management on mortality in neonates, infants, and preschool children: a meta-analysis of community-based trials. Lancet Infect Dis 2003;3:547–56. 10.1016/s1473-3099(03)00737-0
    1. Nantanda R, Tumwine JK, Ndeezi G, et al. . Asthma and pneumonia among children less than five years with acute respiratory symptoms in Mulago Hospital, Uganda: evidence of under-diagnosis of asthma. PLoS One 2013;8:e81562. 10.1371/journal.pone.0081562
    1. Ellington LE, Najjingo I, Rosenfeld M, et al. . Health workers' perspectives of a mobile health tool to improve diagnosis and management of paediatric acute respiratory illnesses in Uganda: a qualitative study. BMJ Open 2021;11:e049708. 10.1136/bmjopen-2021-049708
    1. Margolis PA, Ferkol TW, Marsocci S, et al. . Accuracy of the clinical examination in detecting hypoxemia in infants with respiratory illness. J Pediatr 1994;124:552–60. 10.1016/s0022-3476(05)83133-6
    1. Murphy RLH, Vyshedskiy A, Power-Charnitsky V-A, et al. . Automated lung sound analysis in patients with pneumonia. Respir Care 2004;49:1490–7.
    1. Grenier MC, Gagnon K, Genest J, et al. . Clinical comparison of acoustic and electronic stethoscopes and design of a new electronic stethoscope. Am J Cardiol 1998;81:653–6. 10.1016/s0002-9149(97)00977-6
    1. Brooks D, Thomas J. Interrater reliability of auscultation of breath sounds among physical therapists. Phys Ther 1995;75:1082–8. 10.1093/ptj/75.12.1082
    1. Gjłrup T, Bugge PM, Jensen AM. Interobserver variation in assessment of respiratory signs. physicians' guesses as to interobserver variation. Acta Med Scand 1984;216:61–6. 10.1111/j.0954-6820.1984.tb03772.x
    1. Gurung A, Scrafford CG, Tielsch JM, et al. . Computerized lung sound analysis as diagnostic aid for the detection of abnormal lung sounds: a systematic review and meta-analysis. Respir Med 2011;105:1396–403. 10.1016/j.rmed.2011.05.007
    1. Rennoll V, McLane I, Emmanouilidou D. Electronic stethoscope filtering mimics the perceived sound characteristics of acoustic stethoscope. IEEE journal of biomedical and health informatics 2020;25:1542–9.
    1. Emmanouilidou D, McCollum ED, Park DE, et al. . Computerized lung sound screening for pediatric auscultation in noisy field environments. IEEE Trans Biomed Eng 2018;65:1564–74. 10.1109/TBME.2017.2717280
    1. Palafox M, Guiscafré H, Reyes H, et al. . Diagnostic value of tachypnoea in pneumonia defined radiologically. Arch Dis Child 2000;82:41–5. 10.1136/adc.82.1.41
    1. Leng S, Tan RS, Chai KTC, et al. . The electronic stethoscope. Biomed Eng Online 2015;14:66. 10.1186/s12938-015-0056-y
    1. Pramono RXA, Bowyer S, Rodriguez-Villegas E. Automatic adventitious respiratory sound analysis: a systematic review. PLoS One 2017;12:e0177926. 10.1371/journal.pone.0177926
    1. Andrès E, Gass R, Charloux A, et al. . Respiratory sound analysis in the era of evidence-based medicine and the world of medicine 2.0. J Med Life 2018;11:89.
    1. Elhilali M, West JE. The stethoscope gets smart: engineers from Johns Hopkins are giving the humble stethoscope an AI upgrade. IEEE Spectr 2019;56:36–41. 10.1109/mspec.2019.8635815
    1. West E, McLane I, McLane D. Introducing feelix, a digital stethoscope incorporating active noise control and automatic detection of lung sound abnormalities. J Acoust Soc Am 2019;145:1923–23.
    1. Ramanathan A, Zhou L, Marzbanrad F. Digital stethoscopes in paediatric medicine. Acta Paediatrica 2018.
    1. Kevat AC, Kalirajah A, Roseby R. Digital stethoscopes compared to standard auscultation for detecting abnormal paediatric breath sounds. Eur J Pediatr 2017;176:989–92. 10.1007/s00431-017-2929-5
    1. Møller H, Pedersen CS. Hearing at low and infrasonic frequencies. Noise Health 2004;6:37.
    1. Thinklabs . Thinklabs one digital stethoscope. Centennial, CO, USA: Thinklabs, 2015.
    1. Emmanouilidou D, McCollum ED, Park DE, et al. . Adaptive noise suppression of pediatric lung auscultations with real applications to noisy clinical settings in developing countries. IEEE Trans Biomed Eng 2015;62:2279–88. 10.1109/TBME.2015.2422698
    1. McLane I, Emmanouilidou D, West JE, et al. . Design and comparative performance of a robust lung auscultation system for noisy clinical settings. IEEE J Biomed Health Inform 2021;25:2583-2594. 10.1109/JBHI.2021.3056916
    1. IEEE . Validation of auscultation technologies using objective and clinical comparisons. 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2020.
    1. Riaz BK, Ali L, Ahmad SA, et al. . Community clinics in Bangladesh: a unique example of public-private partnership. Heliyon 2020;6:e03950. 10.1016/j.heliyon.2020.e03950
    1. McCollum ED, Park DE, Watson NL, et al. . 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. BMJ Open Respir Res 2017;4:e000193. 10.1136/bmjresp-2017-000193
    1. Flack VF, Afifi AA, Lachenbruch PA, et al. . Sample size determinations for the two rater kappa statistic. Psychometrika 1988;53:321–5. 10.1007/BF02294215
    1. Ellington LE, Gilman RH, Tielsch JM, et al. . Computerised lung sound analysis to improve the specificity of paediatric pneumonia diagnosis in resource-poor settings: protocol and methods for an observational study. BMJ Open 2012;2:e000506. 10.1136/bmjopen-2011-000506
    1. Scrafford C, Basnet S, Ansari I, et al. . Evaluation of digital auscultation to diagnose pneumonia in children 2 to 35 months of age in a clinical setting in Kathmandu, Nepal: a prospective Case–Control study. J Pediatr Infect Dis 2016;11:028–36. 10.1055/s-0036-1593749
    1. Allwell-Brown G, Hussain-Alkhateeb L, Sewe MO, et al. . Determinants of trends in reported antibiotic use among sick children under five years of age across low-income and middle-income countries in 2005-17: a systematic analysis of user characteristics based on 132 national surveys from 73 countries. Int J Infect Dis 2021;108:473-482. 10.1016/j.ijid.2021.05.058
    1. Assembly UG. Political Declaration of the high-level meeting of the general assembly on antimicrobial resistance. New York, NY: United Nations, 2016.
    1. Lynch T, Bialy L, Kellner JD, et al. . A systematic review on the diagnosis of pediatric bacterial pneumonia: when gold is bronze. PLoS One 2010;5:e11989. 10.1371/journal.pone.0011989
    1. Hazir T, Nisar YB, Qazi SA, et al. . Chest radiography in children aged 2-59 months diagnosed with non-severe pneumonia as defined by World Health organization: descriptive multicentre study in Pakistan. BMJ 2006;333:629. 10.1136/bmj.38915.673322.80
    1. McCollum ED, Park DE, Watson NL, et al. . Digital auscultation in PERCH: associations with chest radiography and pneumonia mortality in children. Pediatr Pulmonol 2020;55:3197–208. 10.1002/ppul.25046

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