Department of Global Health, Boston University School of Public Health, Boston, Massachusetts, USA
Medical Research Council Unit, The Gambia at the London School of Hygiene & Tropical Medicine, Fajara, The Gambia.
BMJ Open. 2021 Sep 30;11(9):e046590. doi: 10.1136/bmjopen-2020-046590.
Clinically diagnosed pneumonia in children is a leading cause of paediatric hospitalisation and mortality. The aetiology is usually bacterial or viral, but malaria can cause a syndrome indistinguishable from clinical pneumonia. There is no method with high sensitivity to detect a bacterial infection in these patients and, as result, antibiotics are frequently overprescribed. Conversely, unrecognised concomitant bacterial infection in patients with malarial infections occur with omission of antibiotic therapy from patients with bacterial infections. Previously, we identified two combinations of blood proteins with 96% sensitivity and 86% specificity for detecting bacterial disease. The current project aimed to validate and improve these combinations by evaluating additional biomarkers in paediatric patients with clinical pneumonia. Our goal was to describe combinations of a limited number of proteins with high sensitivity and specificity for bacterial infection to be incorporated in future point-of-care tests. Furthermore, we seek to explore signatures to prognosticate clinical pneumonia.
Patients (n=900) aged 2-59 months presenting with clinical pneumonia at two Gambian hospitals will be enrolled and classified according to criteria for definitive bacterial aetiology (based on microbiological tests and chest radiographs). We will measure proteins at admission using Luminex-based immunoassays in 90 children with definitive and 160 with probable bacterial aetiology, and 160 children classified according to the prognosis of their disease. Previously identified diagnostic signatures will be assessed through accuracy measures. Moreover, we will seek new diagnostic and prognostic signatures through machine learning methods, including support vector machine, penalised regression and classification trees.
Ethics approval has been obtained from the Gambia Government/Medical Research Council Unit The Gambia Joint Ethics Committee (protocol 1616) and the institutional review board of Boston University Medical Centre (STUDY00000958). Study results will be disseminated to the staff of the study hospitals, in scientific seminars and meetings, and in publications.
H-38462.
儿童临床诊断肺炎是导致儿科住院和死亡的主要原因。病因通常为细菌或病毒,但疟疾也可引起与临床肺炎无法区分的综合征。目前尚无高灵敏度的方法可用于检测此类患者的细菌感染,因此抗生素经常被过度开具。相反,在未识别疟疾感染患者的同时合并细菌感染的情况下,会因漏用抗生素治疗而使细菌感染患者的病情恶化。此前,我们发现了两种血液蛋白组合,其对细菌感染的检测具有 96%的灵敏度和 86%的特异性。本项目旨在通过评估儿科临床肺炎患者的其他生物标志物来验证和改进这些组合。我们的目标是描述具有高灵敏度和特异性的细菌感染的少数几种蛋白质组合,以便将来纳入即时检验。此外,我们还寻求探索预测临床肺炎的特征。
本研究将纳入两家冈比亚医院的 900 名年龄在 2-59 个月之间的临床诊断肺炎患儿,根据明确的细菌病因标准(基于微生物学检查和胸部 X 光片)进行分类。我们将在 90 名明确细菌病因和 160 名可能细菌病因的患儿入院时使用 Luminex 免疫分析检测蛋白质,并根据疾病预后将 160 名患儿进行分类。我们将通过准确性指标评估先前确定的诊断特征。此外,我们将通过机器学习方法(包括支持向量机、惩罚回归和分类树)寻找新的诊断和预后特征。
本研究已获得冈比亚政府/医学研究理事会冈比亚联合伦理委员会(协议号 1616)和波士顿大学医学中心机构审查委员会(注册号 STUDY00000958)的伦理批准。研究结果将在研究医院的工作人员、科学研讨会和会议以及出版物中进行传播。
H-38462。