Article Text

Computerised lung sound analysis to improve the specificity of paediatric pneumonia diagnosis in resource-poor settings: protocol and methods for an observational study
  1. Laura E Ellington1,
  2. Robert H Gilman2,3,
  3. James M Tielsch2,
  4. Mark Steinhoff2,4,
  5. Dante Figueroa5,
  6. Shalim Rodriguez6,
  7. Brian Caffo7,
  8. Brian Tracey8,
  9. Mounya Elhilali9,
  10. James West9,
  11. William Checkley1
  1. 1Division of Pulmonary and Critical Care, Johns Hopkins University, Baltimore, Maryland, USA
  2. 2Program in Global Disease Epidemiology and Control, Johns Hopkins University, Baltimore, Maryland, USA
  3. 3Asociación Benéfica PRISMA, Lima, Peru
  4. 4Global Health Center, Cincinnati Children's Hospital, Cincinnati, Ohio, USA
  5. 5Instituto Nacional de Salud del Niño, Lima, Peru
  6. 6Unidad de Cuidados Intensivos, Hospital Nacional Rebagliati, Lima, Peru
  7. 7Department of Biostatistics, Johns Hopkins University, Baltimore, Maryland, USA
  8. 8Department of Electrical and Computer Engineering, Tufts University, Medford, Massachusetts, USA
  9. 9Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, Maryland, USA
  1. Correspondence to William Checkley; wcheckl1{at}jhmi.edu

Abstract

Introduction WHO case management algorithm for paediatric pneumonia relies solely on symptoms of shortness of breath or cough and tachypnoea for treatment and has poor diagnostic specificity, tends to increase antibiotic resistance. Alternatives, including oxygen saturation measurement, chest ultrasound and chest auscultation, exist but with potential disadvantages. Electronic auscultation has potential for improved detection of paediatric pneumonia but has yet to be standardised. The authors aim to investigate the use of electronic auscultation to improve the specificity of the current WHO algorithm in developing countries.

Methods This study is designed to test the hypothesis that pulmonary pathology can be differentiated from normal using computerised lung sound analysis (CLSA). The authors will record lung sounds from 600 children aged ≤5 years, 100 each with consolidative pneumonia, diffuse interstitial pneumonia, asthma, bronchiolitis, upper respiratory infections and normal lungs at a children's hospital in Lima, Peru. The authors will compare CLSA with the WHO algorithm and other detection approaches, including physical exam findings, chest ultrasound and microbiologic testing to construct an improved algorithm for pneumonia diagnosis.

Discussion This study will develop standardised methods for electronic auscultation and chest ultrasound and compare their utility for detection of pneumonia to standard approaches. Utilising signal processing techniques, the authors aim to characterise lung sounds and through machine learning, develop a classification system to distinguish pathologic sounds. Data will allow a better understanding of the benefits and limitations of novel diagnostic techniques in paediatric pneumonia.

This is an open-access article distributed under the terms of the Creative Commons Attribution Non-commercial License, which permits use, distribution, and reproduction in any medium, provided the original work is properly cited, the use is non commercial and is otherwise in compliance with the license. See: http://creativecommons.org/licenses/by-nc/2.0/ and http://creativecommons.org/licenses/by-nc/2.0/legalcode.

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Footnotes

  • To cite: 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. doi:10.1136/bmjopen-2011-000506

  • Funding Funding for this study and support for LEE was provided by the Doris Duke Charitable Foundation Clinical Research Fellowship. Additional support came from A.B. PRISMA, Instituto Nacional de Salud del Niño and collaborators at JHU, Tufts University, Cincinnati Children's Hospital and Hospital Nacional Rebagliati. Thinklabs Medical (Centennial, Colorado) generously provided us with an electronic stethoscope, at discount.

  • Competing interests None.

  • Ethics approval Approval was obtained from the Ethics Committees of A.B. PRISMA, Instituto Nacional de Salud del Niño and Johns Hopkins School of Medicine. Dissemination will include publications following the study and the development of a free online library of lung sounds for improvement of CLSA, future research and clinical education.

  • Contributors All authors were involved in the study design and writing of the manuscript, and all reviewed the final manuscript before submission. LEE directly contributed to study design and is responsible for supervision of data gathering at the children's hospital in Lima, electronic auscultation and chest ultrasound recordings, data management, analysis and writing of this manuscript. RHG provided mentorship to LEE and technical support for the study. JMT and MS contributed to the concept and study design. DF will serve as study physician, provide supervision and administrative oversight on site and perform physical testing. SR contributed to study design and was responsible for developing and training the study technician to a standardised chest ultrasound protocol. BC contributed to study design and will contribute to statistical analysis. BT, ME and JW contributed to study design and will contribute significantly to signal processing and data analysis. WC had ultimate oversight over study design and administration and was equally responsible in writing of the manuscript and serves as mentor to LEE throughout the conduct of the study.

  • Provenance and peer review Not commissioned; internally peer reviewed.

  • Data sharing statement Currently, no unpublished data are available. Plans for dissemination include final publication following completion of the study following the STARD guidelines for reporting diagnostic accuracy. We aim to develop a free online library of lung sounds for further enhancement of computerised lung sound analysis and the machine learning algorithm, as well as for future research and clinical education.

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