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变革用于精准公共卫生的快速诊断检测:面向制造商和用户的公开指南

Transforming Rapid Diagnostic Tests for Precision Public Health: Open Guidelines for Manufacturers and Users.

作者信息

Lubell-Doughtie Peter, Bhatt Shiven, Wong Roger, Shankar Anuraj H

机构信息

Ona Systems Inc, Burlington, VT, United States.

Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom.

出版信息

JMIR Biomed Eng. 2022 Jul 29;7(2):e26800. doi: 10.2196/26800.

DOI:10.2196/26800
PMID:38875688
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11041428/
Abstract

BACKGROUND

Precision public health (PPH) can maximize impact by targeting surveillance and interventions by temporal, spatial, and epidemiological characteristics. Although rapid diagnostic tests (RDTs) have enabled ubiquitous point-of-care testing in low-resource settings, their impact has been less than anticipated, owing in part to lack of features to streamline data capture and analysis.

OBJECTIVE

We aimed to transform the RDT into a tool for PPH by defining information and data axioms and an information utilization index (IUI); identifying design features to maximize the IUI; and producing open guidelines (OGs) for modular RDT features that enable links with digital health tools to create an RDT-OG system.

METHODS

We reviewed published papers and conducted a survey with experts or users of RDTs in the sectors of technology, manufacturing, and deployment to define features and axioms for information utilization. We developed an IUI, ranging from 0% to 100%, and calculated this index for 33 World Health Organization-prequalified RDTs. RDT-OG specifications were developed to maximize the IUI; the feasibility and specifications were assessed through developing malaria and COVID-19 RDTs based on OGs for use in Kenya and Indonesia.

RESULTS

The survey respondents (n=33) included 16 researchers, 7 technologists, 3 manufacturers, 2 doctors or nurses, and 5 other users. They were most concerned about the proper use of RDTs (30/33, 91%), their interpretation (28/33, 85%), and reliability (26/33, 79%), and were confident that smartphone-based RDT readers could address some reliability concerns (28/33, 85%), and that readers were more important for complex or multiplex RDTs (33/33, 100%). The IUI of prequalified RDTs ranged from 13% to 75% (median 33%). In contrast, the IUI for an RDT-OG prototype was 91%. The RDT open guideline system that was developed was shown to be feasible by (1) creating a reference RDT-OG prototype; (2) implementing its features and capabilities on a smartphone RDT reader, cloud information system, and Fast Healthcare Interoperability Resources; and (3) analyzing the potential public health impact of RDT-OG integration with laboratory, surveillance, and vital statistics systems.

CONCLUSIONS

Policy makers and manufacturers can define, adopt, and synergize with RDT-OGs and digital health initiatives. The RDT-OG approach could enable real-time diagnostic and epidemiological monitoring with adaptive interventions to facilitate control or elimination of current and emerging diseases through PPH.

摘要

背景

精准公共卫生(PPH)可通过根据时间、空间和流行病学特征进行监测和干预,将影响最大化。尽管快速诊断检测(RDT)已在资源匮乏地区实现了广泛的即时检测,但由于缺乏简化数据采集和分析的功能,其影响未达预期。

目的

我们旨在通过定义信息和数据公理以及信息利用指数(IUI),将RDT转变为PPH工具;识别可最大化IUI的设计特征;并为模块化RDT功能制定开放指南(OG),以实现与数字健康工具的链接,从而创建一个RDT-OG系统。

方法

我们查阅了已发表的论文,并对技术、制造和部署领域的RDT专家或用户进行了调查,以定义信息利用的特征和公理。我们开发了一个范围从0%到100%的IUI,并为33种世界卫生组织预认证的RDT计算了该指数。制定RDT-OG规范以最大化IUI;通过基于OG开发用于肯尼亚和印度尼西亚的疟疾和新冠病毒RDT,评估其可行性和规范。

结果

调查对象(n = 33)包括16名研究人员、7名技术专家、3名制造商、2名医生或护士以及5名其他用户。他们最关心RDT的正确使用(30/33,91%)、结果解读(28/33,85%)和可靠性(26/33,79%),并且相信基于智能手机的RDT阅读器可以解决一些可靠性问题(28/33,85%),并且对于复杂或多重RDT来说,阅读器更为重要(33/33,100%)。预认证RDT的IUI范围为13%至75%(中位数为33%)。相比之下,RDT-OG原型的IUI为91%。通过以下方式证明了所开发的RDT开放指南系统是可行的:(1)创建一个参考RDT-OG原型;(2)在智能手机RDT阅读器、云信息系统和快速医疗保健互操作性资源上实现其功能和能力;(3)分析RDT-OG与实验室、监测和人口动态统计系统整合对公共卫生的潜在影响。

结论

政策制定者和制造商可以定义、采用RDT-OG并与数字健康倡议协同合作。RDT-OG方法可实现实时诊断和流行病学监测,并采取适应性干预措施,以通过PPH促进对当前和新出现疾病的控制或消除。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7f51/11041428/b6186d1081e3/biomedeng_v7i2e26800_fig7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7f51/11041428/b7c785cc5471/biomedeng_v7i2e26800_fig1.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7f51/11041428/b6186d1081e3/biomedeng_v7i2e26800_fig7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7f51/11041428/b7c785cc5471/biomedeng_v7i2e26800_fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7f51/11041428/8bb21c1f2ce0/biomedeng_v7i2e26800_fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7f51/11041428/20f85ba1628e/biomedeng_v7i2e26800_fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7f51/11041428/c8bfacfdb8c7/biomedeng_v7i2e26800_fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7f51/11041428/71f9727c340d/biomedeng_v7i2e26800_fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7f51/11041428/1547b83b6054/biomedeng_v7i2e26800_fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7f51/11041428/b6186d1081e3/biomedeng_v7i2e26800_fig7.jpg

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