Projahnmo Research Foundation, Dhaka, Bangladesh
Usher Institute, The University of Edinburgh, Edinburgh, UK.
BMJ Open. 2022 Feb 9;12(2):e059630. doi: 10.1136/bmjopen-2021-059630.
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.
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.
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.
NCT03959956.
世界卫生组织(WHO)的儿童疾病综合管理(IMCI)算法用于诊断儿童肺炎,依赖于计算呼吸频率和观察呼吸窘迫来诊断儿童肺炎。IMCI 肺炎病例定义具有较高的敏感性,但特异性较低,导致儿童肺炎过度诊断和不必要的抗生素使用。在 IMCI 中纳入肺部听诊可以提高肺炎诊断的特异性。我们的目标是:(1)评估初级卫生保健工作者(HCWs)使用 Feelix Smart 听诊器对 5 岁以下儿童肺部声音记录的质量;(2)确定自动算法记录的肺部声音解释与参考儿科医生解释的可靠性和性能。
在一项横断面设计中,社区 HCWs 将在孟加拉国锡尔赫特的扎基甘杰分区的一级医疗机构对约 1000 名疑似肺炎的 5 岁以下儿童的肺部声音进行记录。入组的儿童将接受肺炎评估,包括血氧饱和度,并使用 Feelix Smart 听诊器在四个连续的胸部位置(两个背部和两个前位)记录肺部声音。将应用一种新的声音滤波算法来处理录音,以解决环境噪音并优化录音质量。记录的声音将根据预定义的质量标准进行评估。经过培训的儿科听力小组将录音分类为以下类别之一:正常、爆裂声、喘鸣、爆裂声和喘鸣或无法解释。所有声音文件将由自动算法分类为相同类别,并与小组分类进行比较。将自动算法的敏感性、特异性和预测值评估考虑到小组的最终解释作为金标准。
该研究方案已获得孟加拉国医学研究理事会国家研究伦理委员会(注册号:09630012018)和英国爱丁堡学术和临床中央研究与发展医学伦理委员会(REC 参考号:18-HV-051)的批准。传播将通过会议演讲、同行评议期刊和孟加拉国的利益相关者参与会议进行。
NCT03959956。