RT Journal Article
SR Electronic
T1 Computerised lung sound analysis to improve the specificity of paediatric pneumonia diagnosis in resource-poor settings: protocol and methods for an observational study
JF BMJ Open
JO BMJ Open
FD British Medical Journal Publishing Group
SP e000506
DO 10.1136/bmjopen-2011-000506
VO 2
IS 1
A1 Laura E Ellington
A1 Robert H Gilman
A1 James M Tielsch
A1 Mark Steinhoff
A1 Dante Figueroa
A1 Shalim Rodriguez
A1 Brian Caffo
A1 Brian Tracey
A1 Mounya Elhilali
A1 James West
A1 William Checkley
YR 2012
UL http://bmjopen.bmj.com/content/2/1/e000506.abstract
AB 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.