Department of Emergency Medicine, Alpert Medical School, Brown University, Providence, United States.
Department of Pediatrics, and Environmental and Global Health, Emerging Pathogens Institute, University of Florida, Gainesville, United States.
Elife. 2022 Feb 9;11:e72294. doi: 10.7554/eLife.72294.
Diarrheal illness is a leading cause of antibiotic use for children in low- and middle-income countries. Determination of diarrhea etiology at the point-of-care without reliance on laboratory testing has the potential to reduce inappropriate antibiotic use.
This prospective observational study aimed to develop and externally validate the accuracy of a mobile software application ('App') for the prediction of viral-only etiology of acute diarrhea in children 0-59 months in Bangladesh and Mali. The App used a previously derived and internally validated model consisting of patient-specific ('present patient') clinical variables (age, blood in stool, vomiting, breastfeeding status, and mid-upper arm circumference) as well as location-specific viral diarrhea seasonality curves. The performance of additional models using the 'present patient' data combined with other external data sources including location-specific climate, data, recent patient data, and historical population-based prevalence were also evaluated in secondary analysis. Diarrhea etiology was determined with TaqMan Array Card using episode-specific attributable fraction (AFe) >0.5.
Of 302 children with acute diarrhea enrolled, 199 had etiologies above the AFe threshold. Viral-only pathogens were detected in 22% of patients in Mali and 63% in Bangladesh. Rotavirus was the most common pathogen detected (16% Mali; 60% Bangladesh). The present patient+ viral seasonality model had an AUC of 0.754 (0.665-0.843) for the sites combined, with calibration-in-the-large = -0.393 (-0.455--0.331) and calibration slope = 1.287 (1.207-1.367). By site, the present patient+ recent patient model performed best in Mali with an AUC of 0.783 (0.705-0.86); the present patient+ viral seasonality model performed best in Bangladesh with AUC 0.710 (0.595-0.825).
The App accurately identified children with high likelihood of viral-only diarrhea etiology. Further studies to evaluate the App's potential use in diagnostic and antimicrobial stewardship are underway.
Funding for this study was provided through grants from the Bill and Melinda GatesFoundation (OPP1198876) and the National Institute of Allergy and Infectious Diseases (R01AI135114). Several investigators were also partially supported by a grant from the National Institute of Diabetes and Digestive and Kidney Diseases (R01DK116163). This investigation was also supported by the University of Utah Population Health Research (PHR) Foundation, with funding in part from the National Center for Advancing Translational Sciences of the National Institutes of Health under Award Number UL1TR002538. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The funders had no role in the study design, data collection, data analysis, interpretation of data, or in the writing or decision to submit the manuscript for publication.
腹泻病是中低收入国家儿童使用抗生素的主要原因。在不依赖实验室检测的情况下,在床边确定腹泻病因,有可能减少不合理使用抗生素。
本前瞻性观察性研究旨在开发和外部验证一种移动软件应用程序(“应用程序”)用于预测孟加拉国和马里 0-59 个月儿童急性腹泻的单纯病毒病因的准确性。该应用程序使用了先前推导并内部验证的模型,该模型由患者特定的(“当前患者”)临床变量(年龄、粪便中有血、呕吐、母乳喂养状况和中上臂围)以及位置特定的病毒性腹泻季节性曲线组成。还通过使用“当前患者”数据与其他外部数据源(包括位置特定的气候数据、最近患者数据和历史基于人群的流行率)相结合的其他模型的性能在二次分析中进行了评估。使用 TaqMan 微阵列卡检测到特定发作的归因分数(AFe)> 0.5 来确定腹泻病因。
在纳入的 302 名急性腹泻患儿中,有 199 名患儿的病因符合 AFe 阈值。在马里和孟加拉国,分别有 22%和 63%的患儿检测到单纯病毒病原体。轮状病毒是最常见的检测到的病原体(马里 16%;孟加拉国 60%)。本研究中,当前患者+病毒季节性模型在两个地点的 AUC 为 0.754(0.665-0.843),大校准偏差为-0.393(-0.455--0.331),校准斜率为 1.287(1.207-1.367)。按地点划分,当前患者+最近患者模型在马里的表现最佳,AUC 为 0.783(0.705-0.86);当前患者+病毒季节性模型在孟加拉国的表现最佳,AUC 为 0.710(0.595-0.825)。
该应用程序能够准确识别出具有高可能性的单纯病毒性腹泻病因的儿童。正在进行进一步的研究,以评估该应用程序在诊断和抗菌药物管理方面的潜在用途。
本研究的资金来自比尔及梅林达盖茨基金会(OPP1198876)和美国国立过敏和传染病研究所(R01AI135114)的资助。几位研究人员还部分得到了国立糖尿病、消化和肾脏疾病研究所(R01DK116163)的资助。本研究还得到了犹他大学人口健康研究(PHR)基金会的支持,该基金会的部分资金来自美国国立卫生研究院国家推进转化科学中心的拨款,编号为 UL1TR002538。内容仅由作者负责,不一定代表美国国立卫生研究院的官方观点。资助者在研究设计、数据收集、数据分析、数据解释或提交手稿出版方面没有任何作用。