The COVID-19 pandemic highlighted the importance of early detection of illness and the need for health monitoring solutions outside of the hospital setting. We have previously demonstrated a real-time system to identify COVID-19 infection before diagnostic testing, that was powered by commercial-off-the-shelf wearables and machine learning models trained with wearable physiological data from COVID-19 cases outside of hospitals. However, these types of solutions were not readily available at the onset nor during the early outbreak of a new infectious disease when preventing infection transmission was critical, due to a lack of pathogen-specific illness data to train the machine learning models. This study investigated whether a pretrained clinical decision support algorithm for predicting hospital-acquired infection (predating COVID-19) could be readily adapted to detect early signs of COVID-19 infection from wearable physiological signals collected in an unconstrained out-of-hospital setting. A baseline comparison where the pretrained model was applied directly to the wearable physiological data resulted a performance of AUROC = 0.52 in predicting COVID-19 infection. After controlling for contextual effects and applying an unsupervised dataset shift transformation derived from a small set of wearable data from healthy individuals, we found that the model performance improved, achieving an AUROC of 0.74, and it detected COVID-19 infection on average 2 days prior to diagnostic testing. Our results suggest that it is possible to deploy a wearable physiological monitoring system with an infection prediction model pretrained from inpatient data, to readily detect out-of-hospital illness at the emergence of a new infectious disease outbreak.

Authors TF, SM, BC, RD and IS are employees of Philips North America. Author DM was employee of Philips North America. Author DS is employee of Banner Health. All authors declare no other competing interests.

This study is sponsored by the US Department of Defense (DoD), Defense Threat Reduction Agency (DTRA) under contracts: W15QKN-18-9-1002 (CB10560), HDTRA1-20-C-0041, HDTRA121C0006. The funding body did not play a role in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication. The views, opinions and/or findings expressed are those of the authors and should not be interpreted as representing the official views or policies of the Department of Defense or the US Government.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

The MIMIC-III project was approved by the Institutional Review Boards of Beth Israel Deaconess Medical Center and the Massachusetts Institute of Technology. Banner Health data use was a part of a retrospective deterioration detection study approved by the Institutional Review Board of Banner Health and by the Philips Internal Committee for Biomedical Experiments. For both hospital datasets, requirement for individual patient consent was waived because the project did not impact clinical care, was no greater than minimal risk, and all protected health information was removed from the limited dataset used in this study. The collection and use of the wearable dataset was approved by the Institutional Review Boards of the US Department of Defense. Informed consent was obtained from all participants.

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MIMIC-III dataset is available in PhysioNet repository, https://mimic.physionet.org/. The Banner Health dataset is a proprietary dataset that is not publicly shareable. The wearable dataset is from US military personnel and is not publicly shareable.

https://mimic.physionet.org/