Periodic Reporting for period 2 - COVID-RED (COVID-19 infections - Remote Early Detection)
Période du rapport: 2022-01-01 au 2022-12-31
At the time when the call was published in March 2020, there were no specific vaccines or treatments proven effective against this viral disease. Therefore, early and accurate detection of COVID-19 has been critical to containing its spread, improving health outcomes and limits the negative impacts on society.
The COVID-RED consortium was brought together as a result of this call in order to address issues of high public health need that emerged from the pandemic. The COVID-RED project has been working towards developing a reliable tool for easy, at-home use to detect potential COVID-19 infections in symptomatic and asymptomatic individuals. The project employed wearable digital technology (smart armbands) and Artificial Intelligence (AI) algorithms to assess user health status in real-time following known or unknown exposure to the virus. Information is exchanged with the user via a mobile health application. The tool was designed to provide a daily health status check and make recommendations regarding referrals to medical care facilities for confirmatory diagnoses of COVID 19 infections if required.
The overall goal of the COVID-RED public and private partner consortium had been to conduct a study that evaluated the use and performance of a repurposed CE-marked wearable device, which measures breathing rate, pulse rate, skin temperature, and heart rate variability, to detect and monitor changes in the measured bio parameters that are indicative of a potential COVID-19 infection. Participants also reported clinical symptoms via the device’s mobile application. The project has aimed to improve early detection of COVID-19 cases, and thus limit the spread of the disease, optimize triage for medical care, and improve patient prognosis.
Between 22 February 2021 and 07 February 2022, 17 825 subjects in the Netherlands participated in the study. In line with a randomised, single-blind, two-period, two-sequence crossover design, subjects were randomised 1:1 to either Sequence 1 (experimental condition followed by control condition; n=8915) or Sequence 2 (control condition followed by experimental condition; n=8910). The study started with an initial Learning Phase (maximum of 3 months), followed by a 3-month Period 1 and a 3-month Period 2. Based on demographics, medical history, and/or profession, each subject was stratified at baseline within each sequence based on their risk to contract SARS-CoV-2 as well as their risk of developing severe COVID-19 (i.e. a high-risk and a normal-risk group). This design aimed to allocate 10 000 subjects per sequence: approximately 6500 normal-risk individuals and 3500 high-risk individuals per sequences.
Taken together, the primary analyses demonstrated that the algorithm applied in the experimental condition was superior in the overall and early detection of SARS-CoV-2 infections than the control condition. Its higher sensitivity, however, came at the cost of inferior specificity, due to an increased amount of (false) positive signals to get tested received by subjects in the experimental condition.
Secondary analyses revealed the final version of the experimental condition algorithm relied heavily on positive variance (e.g. net increases compared to a baseline mean) in a subject’s breathing rate and heart rate in the 1 and 4 days preceding the alert, respectively. Additional analyses also revealed the pattern of change in physiological parameters across the SARS-CoV-2 disease trajectory and considered the impact of potential confounders on wearable measurements. Finally, we investigated procedural compliance rates across several stratifications, including risk class, study periods, and pre- versus post-COVID-19 vaccination.
Despite a rapidly evolving epidemiological landscape and the unforeseen roll out of vaccines during the course of the investigation, the COVID-RED study achieved its primary objectives. Namely, it developed and tested a novel algorithm ingesting data from a wearable device.