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首页> 外文期刊>JMIR Research Protocols >Personalized Analytics and a Wearable Biosensor Platform for Early Detection of COVID-19 Decompensation (DeCODe): Protocol for the Development of the COVID-19 Decompensation Index
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Personalized Analytics and a Wearable Biosensor Platform for Early Detection of COVID-19 Decompensation (DeCODe): Protocol for the Development of the COVID-19 Decompensation Index

机译：个人化分析和可穿戴的生物传感器平台，用于早期检测Covid-19失代偿（解码）：用于开发Covid-19代名性指数的协议

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Background During the COVID-19 pandemic, novel digital health technologies have the potential to improve our understanding of SARS-CoV-2 and COVID-19, improve care delivery, and produce better health outcomes. The National Institutes of Health called on digital health leaders to contribute to a high-quality data repository that will support researchers to make discoveries that are otherwise not possible with small, limited data sets. Objective To this end, we seek to develop a COVID-19 digital biomarker for early detection of physiological exacerbation or decompensation. We propose the development and validation of a COVID-19 decompensation Index (CDI) in a 2-phase study that builds on existing wearable biosensor-derived analytics generated by physIQ’s end-to-end cloud platform for continuous physiological monitoring with wearable biosensors. This effort serves to achieve two primary objectives: (1) to collect adequate data to help develop the CDI and (2) to collect rich deidentified clinical data correlating with outcomes and symptoms related to COVID-19 progression. Our secondary objectives include evaluation of the feasibility and usability of pinpointIQ, a digital platform through which data are gathered, analyzed, and displayed. Methods This is a prospective, nonrandomized, open-label, 2-phase study. Phase I will involve data collection for the digital data hub of the National Institutes of Health as well as data to support the preliminary development of the CDI. Phase II will involve data collection for the hub and contribute to continued refinement and validation of the CDI. While this study will focus on the development of a CDI, the digital platform will also be evaluated for feasibility and usability while clinicians deliver care to continuously monitored patients enrolled in the study. Results Our target CDI will be a binary classifier trained to distinguish participants with and those without decompensation. The primary performance metric for CDI will be the area under the receiver operating characteristic curve with a minimum performance criterion of ≥0.75 (α=.05; power [1–β]=0.80). Furthermore, we will determine the sex or gender and race or ethnicity of the participants, which would account for differences in the CDI performance, as well as the lead time—time to predict decompensation—and its relationship with the ultimate disease severity based on the World Health Organization COVID-19 ordinal scale. Conclusions Using machine learning techniques on a large data set of patients with COVID-19 could provide valuable insights into the pathophysiology of COVID-19 and a digital biomarker for COVID-19 decompensation. Through this study, we intend to develop a tool that can uniquely reflect physiological data of a diverse population and contribute to high-quality data that will help researchers better understand COVID-19.
机译：背景技术在Covid-19流行病中，新颖的数字健康技术有可能提高我们对SARS-COV-2和Covid-19的理解，改善护理，并产生更好的健康结果。国家卫生研究院呼吁数字卫生领导人促进高质量的数据存储库，该资料将支持研究人员，以使得小型有限的数据集不可能的发现。目的为此，我们寻求开发一个Covid-19数字生物标志物，用于早期发现生理加剧或失代偿。我们提出了在一个二相研究中的Covid-19失代偿指数（CDI）的开发和验证，该研究构建了Physiq的端到端云平台产生的现有可穿戴的生物传感器衍生的分析，用于使用可穿戴生物传感器的连续生理监测。这项努力有助于实现两个主要目标：（1）收集足够的数据，以帮助开发CDI和（2），收集与与Covid-19进展相关的结果和症状相关的富不同的临床数据。我们的辅助目标包括评估Pinpointiq的可行性和可用性，通过收集数据，分析和显示数据的数字平台。方法这是一个前瞻性，非沉积的开放标签，2相研究。阶段我将涉及国家健康研究院的数字数据中心的数据收集以及支持CDI初步发展的数据。第二阶段将涉及枢纽的数据收集，并有助于继续改进和CDI的验证。虽然本研究将重点关注CDI的开发，但也将评估数字平台以获得可行性和可用性，而临床医生会在不断监测入学患者的情况下进行护理。结果我们的目标CDI将是一个培训的二进制分类器，以区分参与者和那些没有恶性的人。 CDI的主要性能度量将是接收器下的区域，操作特性曲线，最小性能标准≥0.75（α= .05;功率[1-β] = 0.80）。此外，我们将确定参与者的性别或性别和种族或种族，这将考虑CDI绩效的差异，以及预测不起作道的提前时效及其与最终疾病严重程度的关系世界卫生组织Covid-19序数。结论使用机器学习技术在大型Covid-19患者的大型数据集上可以为Covid-19的病理生理学提供有价值的见解，以及用于Covid-19失代偿的数字生物标志物。通过这项研究，我们打算开发一个可以唯一反映各种人口的生理数据的工具，并有助于高质量的数据，帮助研究人员更好地了解Covid-19。

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