Yosi Levi’s research while affiliated with Tel Aviv University and other places

Background Public reluctance to receive COVID-19 vaccination is associated with safety concerns. By contrast, the seasonal influenza vaccine has been administered for decades with a solid safety record and a high level of public acceptance. We compare the safety profile of the BNT162b2 COVID-19 booster vaccine to that of the seasonal influenza vaccine. Methods We study a prospective cohort of 5079 participants in Israel and a retrospective cohort of 250,000 members of MHS selected randomly. We examine reactions to BNT162b2 mRNA COVID-19 booster and to influenza vaccinations. All prospective cohort participants wore a smartwatch and completed a daily digital questionnaire. We compare pre-vaccination and post-vaccination smartwatch heart-rate data, and a stress measure based on heart-rate variability. We also examine adverse events from electronic health records. Results In the prospective cohort, 1905 participants receive the COVID-19 booster vaccine; 899 receive influenza vaccination. Focusing on those who receive both vaccines yields a total of 689 participants in the prospective cohort and 31,297 members in the retrospective cohort. Individuals reporting a more severe reaction after influenza vaccination tend to likewise report a more severe reaction after COVID-19 vaccination. In paired analysis, the increase in both heart rate and stress measure for each participant is higher for COVID-19 than for influenza in the first 2 days after vaccination. No elevated risk of hospitalization due to adverse events is found following either vaccine. Except for Bell’s palsy after influenza vaccination, no elevated risk of adverse events is found. Conclusions The more pronounced side effects after COVID-19 vaccination may explain the greater concern associated with it. Nevertheless, our comprehensive analysis supports the safety profile of both vaccines.

Vaccines stand out as one of the most effective tools in our arsenal for reducing morbidity and mortality. Nonetheless, public hesitancy towards vaccination often stems from concerns about potential side effects, which can vary from person to person. As of now, there are no automated systems available to proactively warn against potential side effects or gauge their severity following vaccination. We have developed machine learning (ML) models designed to predict and detect the severity of post-vaccination side effects. Our study involved 2111 participants who had received at least one dose of either a COVID-19 or influenza vaccine. Each participant was equipped with a Garmin Vivosmart 4 smartwatch and was required to complete a daily self-reported questionnaire regarding local and systemic reactions through a dedicated mobile application. Our XGBoost models yielded an area under the receiver operating characteristic curve (AUROC) of 0.69 and 0.74 in predicting and detecting moderate to severe side effects, respectively. These predictions were primarily based on variables such as vaccine type (influenza vs. COVID-19), the individual's history of side effects from previous vaccines, and specific data collected from the smartwatches prior to vaccine administration, including resting heart rate, heart rate, and heart rate variability. In conclusion, our findings suggest that wearable devices can provide an objective and continuous method for predicting and monitoring moderate to severe vaccine side effects. This technology has the potential to improve clinical trials by automating the classification of vaccine severity.

Background: Public reluctance to receive COVID-19 vaccination is due in large part to safety concerns. We compare the safety profile of the BNT162b2 COVID-19 booster vaccine to that of the seasonal influenza vaccine, which has been administered for decades with a solid safety record and a high level of public acceptance. Methods: We study a prospective cohort of 5,079 participants in Israel (the PerMed study) and a retrospective cohort of 250,000 members of Maccabi Healthcare Services. We examine reactions to BNT162b2 (Pfizer-BioNTech) mRNA COVID-19 booster vaccinations and to influenza vaccination. All prospective cohort participants wore a Garmin Vivosmart 4 smartwatch and completed a daily questionnaire via smartphone. For the prospective cohort, we compare pre-vaccination (baseline) and post-vaccination smartwatch heart rate data and a stress measure based on heart rate variability, and we examine symptom severity from patient self-reports. For the retrospective cohort, we examine electronic health records (EHRs) for the existence of 28 potential adverse events during the 28-day period before and after each vaccination. Findings: In the prospective cohort, 1,905 participants received COVID-19 vaccination; 899 received influenza vaccination. Focusing on those who received both vaccines yielded a total of 689 participants in the prospective cohort and 31,297 members in the retrospective cohort. Questionnaire analysis: For the COVID-19 vaccine, 39.7% [95% CI 36.4% to 42.9%] of individuals reported no systemic reaction vs. 66.9% [95% CI 63.4% to 70.3%] for the influenza vaccine. Individuals reporting a more severe reaction after influenza vaccination tended to likewise report a more severe reaction after COVID-19 vaccination (r=0.185, p<0.001). Smartwatch analysis: A statistically significant increase in heart rate and stress measure occurred during the first 3 days after COVID-19 vaccination, peaking 22 hours after vaccination with a mean increase of 4.48 (95% CI 3.94 to 5.01) beats per minute and 9.34 (95% CI 8.31 to 10.37) units in the stress measure compared to baseline. For influenza vaccination, we observed no changes in heart rate or stress measures. In paired analysis, the increase in both heart rate and stress measure for each participant was higher (p-value < 0.001) for COVID-19 vaccination than for influenza vaccination in the first 2 days after vaccination. On the second day after vaccination, participants had 1.5 (95% CI 0.68 to 2.20) more heartbeats per minute and 3.8 (95% CI 2.27 to 5.22) units higher stress measure, compared to their baseline. These differences disappeared by the third day after vaccination. EHR analysis: We found no elevated risk of non-COVID-19 or -influenza hospitalization following either vaccine. COVID-19 vaccination was not associated with an increased risk of any of the adverse events examined. Influenza vaccination was associated with an increased risk of Bells palsy (1.3 [95% CI 0.3 to 2.6] additional events per 10,000 people). Interpretation: The more pronounced side effects after COVID-19 vaccination compared to influenza vaccination may explain the greater concern regarding COVID-19 vaccines. Nevertheless, our findings support the safety profile of both vaccines, as the reported side effects and physiological reactions measured by the smartwatches faded shortly after inoculation, and no substantial increase in adverse events was detected in the retrospective cohort. Funding: This work was supported by the European Research Council, project #949850, and a Koret Foundation gift for Smart Cities and Digital Living.

Halting the rapid clinical deterioration, marked by arterial hypoxemia, is among the greatest challenges clinicians face when treating COVID-19 patients in hospitals. While it is clear that oxygen measures and treatment procedures describe a patient’s clinical condition at a given time point, the potential predictive strength of the duration and extent of oxygen supplementation methods over the entire course of hospitalization for a patient death from COVID-19 has yet to be assessed. In this study, we aim to develop a prediction model for COVID-19 mortality in hospitals by utilizing data on oxygen supplementation modalities of patients. We analyzed the data of 545 patients hospitalized with COVID-19 complications admitted to Assuta Ashdod Medical Center, Israel, between 7 March 2020, and 16 March 2021. By solely analyzing the daily data on oxygen supplementation modalities in 182 random patients, we could identify that 75% (9 out of 12) of individuals supported by reservoir oxygen masks during the first two days died 3–30 days following hospital admission. By contrast, the mortality rate was 4% (4 out of 98) among those who did not require any oxygenation supplementation. Then, we combined this data with daily blood test results and clinical information of 545 patients to predict COVID-19 mortality. Our Random Forest model yielded an area under the receiver operating characteristic curve (AUC) score on the test set of 82.5%, 81.3%, and 83.0% at admission, two days post-admission, and seven days post-admission, respectively. Overall, our results could essentially assist clinical decision-making and optimized treatment and management for COVID-19 hospitalized patients with an elevated risk of mortality.