Passive Detection of COVID-19 with Wearable Sensors and Explainable Machine Learning Algorithms

Individual smartwatch or fitness band sensor data in the setting of COVID-19 has shown promise to identify symptomatic and pre-symptomatic infection or the need for hospitalization, correlations between peripheral temperature and self-reported fever, and an association between changes in heart-rate-variability and infection. In our study, a total of 38,911 individuals (61% female, 15% over 65) have been enrolled between March 25, 2020 and April 3, 2021, with 1,118 reported testing positive and 7,032 negative for COVID-19 by nasopharyngeal PCR swab test. We propose an explainable gradient boosting prediction model based on decision trees for the detection of COVID-19 infection that can adapt to the absence of self-reported symptoms and to the available sensor data, and that can explain the importance of each feature and the post-test-behavior for the individuals. We tested it in a cohort of symptomatic individuals who exhibited an AUC of 0.83 [0.81-0.85], or AUC=0.78 [0.75-0.80] when considering only data before the test date, outperforming state-of-the-art algorithm in these conditions. The analysis of all individuals (including asymptomatic and pre-symptomatic) when self-reported symptoms were excluded provided an AUC of 0.78 [0.76-0.79], or AUC of 0.70 [0.69-0.72] when considering only data before the test date. Extending the use of predictive algorithms for detection of COVID-19 infection based only on passively monitored data from any device, we showed that it is possible to scale up this platform and apply the algorithm in other settings where self-reported symptoms can not be collected.

The Physiologic Response to COVID-19 Vaccination

Two mRNA vaccines and one adenovirus-based vaccine against SARS CoV-2 are currently being distributed at scale in the United States. Objective evidence of a specific individuals physiologic response to that vaccine are not routinely tracked but may offer insights into the acute immune response and personal and/or vaccine characteristics associated with that. We explored this possibility using a smartphone app-based research platform developed early in the pandemic that enabled volunteers (38,911 individuals between 25 March 2020 and 4 April 2021) to share their smartwatch and activity tracker data, as well as self-report, when appropriate, any symptoms, COVID-19 test results and vaccination dates and type. Of 4,110 individuals who reported at least one mRNA vaccination dose, 3,312 provided adequate resting heart rate data from the peri-vaccine period for analysis. We found changes in resting heart rate with respect to an individual baseline increased the days after vaccination, peaked on day 2, and returned to normal on day 6, with a much stronger effect after second dose with respect to first dose (average changes 1.6 versus 0.5 beats per minute). The changes were more pronounced for individuals who received the Moderna vaccine (on both doses), those who previously tested positive to COVID-19 (on dose 1), and for individuals aged <40 years, after adjusting for possible confounding factors. Taking advantage of continuous passive data from personal sensors could potentially enable the identification of a digital fingerprint of inflammation, which might prove useful as a surrogate for vaccine-induced immune response.

Pre-existing health conditions and severe COVID-19 infection: Analysis of commercial health insurance data from 690,000 infected patients

The development and distribution of new vaccines promises an end to the COVID-19 pandemic. With the elderly and front line workers first in line, the vaccination strategy for the general population remains unclear. In this study we identified 690,000 patients infected with COVID-19 with commercial health insurance coverage across the country between April 1, 2020 and September 30, 2020. From prior health care claims, we determined each persons pre-existing diseases among 26 common chronic diseases. Across age-sex strata we determined the relationships between these conditions and severe COVID-19 infections: ICU admission and extended in-patient stay. We classify disease according to risk, develop multivariable models to predict infection outcomes, and created an online risk assessment tool to estimate risk of ICU admission based on pre-existing health conditions. Our results could be used to help guide risk, vaccination health policy, and personal decision-making as these become available to the general population.

Passive Monitoring of Physiological Data and Self-reported Symptoms to Detect Clusters of People with COVID-19

Traditional screening for COVID-19 typically includes survey questions about symptoms, travel history, and sometimes temperature measurements. We explored whether longitudinal, personal sensor data can help identify subtle changes which may indicate an infection, such as COVID-19. To do this we developed an app that collects smartwatch and activity tracker data, as well as self-reported symptoms and diagnostic testing results from participants living in the US. We assessed whether symptoms and sensor data could differentiate COVID-19 positive versus negative cases in symptomatic individuals. Between March 25 and June 7, 2020, we enrolled 30,529 participants, of whom 3,811 reported symptoms, 54 reported testing positive for COVID-19, and 279 negative. We found that a combination of symptom and sensor data resulted in an AUC=0.80 [0.73 - 0.86] which was significantly better (p < 0.01) than a model which just considered symptoms alone (AUC=0.71 [0.63 - 0.79]) in the discrimination between symptomatic individuals positive or negative for COVID-19. Such orthogonal, continuous, passively captured data may be complementary to virus testing that is generally a one-off, or infrequent, sampling assay.

Local Delivery of Nitric Oxide from an Eluting Stent to Inhibit Neointimal Thickening in a Porcine Coronary Injury Model

To assess the effect of a NO-eluting stent on reducing neointimal thickening in a porcine coronary artery stent injury model, sodium nitroprusside (SNP), a NO donor, was incorporated into polyurethane (PU) polymer and coated onto metallic coil stents, and two types of stents with thin and thick barrier coatings were characterized. In vivo biological activity of the NO-eluting stents was assessed by measurement of coronary arterial cGMP levels in 32 pigs/64 arteries at days 1, 2, 7 and 14. Morphometric analyses were performed in 16 pigs to determine the effect of NO-eluting stents on neointimal hyperplasia 28 days following arterial injury. The SNP-coated stents released NO in a controlled manner for up to 4 weeks in the in vitro experiments and an increase in local tissue cGMP levels was demonstrated for up to 14 days. The neointimal area at 28 days was not diminished, however, by NO eluded from either stents of thin or thick barriers (control bare stent - 0.66 mm2, control PU stent - 0.68 mm2, SNP-PU thin coating stent - 0.78 mm2, SNP-PU thick coating stent - 0.85 mm2; all p=NS). In conclusion, the SNP-coated polymer stent exerted a local biological effect on the arterial wall, with sustained elevation of cGMP level. Although local delivery of NO from this device did not reduce neointimal hyperplasia in this porcine model, this polymer-coated stent might be a promising tool for administration of other agents that may modify the reparative tissue responses leading to restenosis.