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Mounting clinical evidence suggests that viral infections can lead to detectable changes in an individual's normal physiologic and behavioral metrics, including heart and respiration rates, heart rate variability, temperature, activity, and sleep prior to symptom onset, potentially even in asymptomatic individuals. While the ability of wearable devices to detect viral infections in a real-world setting has yet to be proven, multiple recent studies have established that individual, continuous data from a range of biometric monitoring technologies can be easily acquired and that through the use of machine learning techniques, physiological signals and warning signs can be identified. In this review, we highlight the existing knowledge base supporting the potential for widespread implementation of biometric data to address existing gaps in the diagnosis and treatment of viral illnesses, with a particular focus on the many important lessons learned from the coronavirus disease 2019 pandemic. 

Detection and Monitoring of Viral Infections via Wearable Devices and Biometric Data

Quantitative imaging biomarkers are of particular interest in drug development for their potential to accelerate the drug development pipeline. The lack of consensus methods and carefully characterized performance hampers the widespread availability of these quantitative measures. A framework to support collaborative work on quantitative imaging biomarkers would entail advanced statistical techniques, the development of controlled vocabularies, and a service-oriented architecture for processing large image archives. Until now, this framework has not been developed. With the availability of tools for automatic ontology-based annotation of datasets, coupled with image archives, and a means for batch selection and processing of image and clinical data, imaging will go through a similar increase in capability analogous to what advanced genetic profiling techniques have brought to molecular biology. We report on our current progress on developing an informatics infrastructure to store, query, and retrieve imaging biomarker data across a wide range of resources in a semantically meaningful way that facilitates the collaborative development and validation of potential imaging biomarkers by many stakeholders. Specifically, we describe the semantic components of our system, QI-Bench, that are used to specify and support experimental activities for statistical validation in quantitative imaging

/pdf/a-novel-knowledge-representation-framework-for-the-1vvnqmybm6.pdf

A novel knowledge representation framework for the statistical validation of quantitative imaging biomarkers.

The Clinical Data Interchange Standards Consortium (CDISC) has developed standards for data models, study design, and supporting clinical trial documents. CDISC standards have made such gains that the Center for Drug Evaluation and Research strongly encourages their use and implementation for submission of drug applications. However, despite the advances and improvements in these standards over the years and the ever-looming threat that they may someday be a requirement for drug applications, many are skeptical of a single standard and fail to understand the far-reaching advantages of adopting it. In this article we address the concerns and present numerous tangible benefits of CDISC standards.

On the Importance of a Single Data Standard

In May 2006, a CDISC SDTM Device sub-team was formed. This sub-team is made of industry experts, CDISC representatives and FDA representatives. This sub-team has been working on a model to fill-in the gaps in SDTM for medical device and diagnostic companies. The initial focus has been on developing a domain that would contain information (metadata) about devices and not on results from devices. The sub-team has come up with a model that is currently being reviewed by industry experts. The proposed model is based on a custom Finding Observation Class domain. The advantage of this proposed model is that it is simple and extensible.

A CDISC SDTM model for the medical device and diagnostic industry

The Clinical Data Interchange Standards Consortium (CDISC) Study Data Tabulation Model (SDTM) is a standard for submitting data tabulations to the FDA in support of marketing applications. In July 2004, this standard became part of the FDA Study Data Specification referenced in the electronic Common Technical Document (eCTD) Guidance. This article will provide an overview of evolution and status of the SDTM and the associated Implementation Guides, commonly referred to as the Study Data Tabulation Model Implementation Guide for Human Clinical Trials (SDTMIG) and the Standard for Exchange of Nonclinical Data (SEND).

/pdf/evolution-and-implementation-of-the-cdisc-study-data-11r9lnir63.pdf

Evolution and Implementation of the CDISC Study Data Tabulation Model (SDTM)

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Evolution and Implementation of the CDISC Study Data Tabulation Model (SDTM)