McMurry et al. assess the real-world safety of the BNT162b2 and mRNA-1273 COVID-19 vaccines. Using natural language processing, they compare the rates of specified adverse effects between 68,266 vaccinated individuals and 68,266 matched unvaccinated individuals. They find that both vaccines are safe and tolerated in clinical practice.

Background: Consecutive negative SARS-CoV-2 PCR test results are being considered to estimate viral clearance in COVID-19 patients. However, there are anecdotal reports of hospitalization from protracted COVID-19 complications despite such confirmed viral clearance, presenting a clinical conundrum. Methods: We conducted a retrospective analysis of 222 hospitalized COVID-19 patients to compare those that were readmitted post-viral clearance (hospitalized post-clearance cohort, n = 49) with those that were not readmitted post-viral clearance (non-hospitalized post-clearance cohort, n = 173) between February and October 2020. In order to differentiate these two cohorts, we used neural network models for the 'augmented curation' of comorbidities and complications with positive sentiment in the Electronic Hosptial Records physician notes. Findings: In the year preceding COVID-19 onset, anemia (n = 13 [26.5%], p-value: 0.007), cardiac arrhythmias (n = 14 [28.6%], p-value: 0.015), and acute kidney injury (n = 7 [14.3%], p-value: 0.030) were significantly enriched in the physician notes of the hospitalized post-clearance cohort. Interpretation: Overall, this retrospective study highlights specific pre-existing conditions that are associated with higher hospitalization rates in COVID-19 patients despite viral clearance and motivates follow-up prospective research into the associated risk factors.

The current diagnostic gold-standard for SARS-CoV-2 clearance from infected patients is two consecutive negative PCR test results. However, there are anecdotal reports of hospitalization from protracted COVID complications (long-COVID) despite such confirmed viral clearance, presenting a clinical conundrum. We conducted a retrospective analysis of 266 COVID patients to compare those that were admitted/re-admitted post-viral clearance (hospitalized post-clearance cohort, n=93) with those that were hospitalized pre-clearance but were not re-admitted post-viral clearance (non-hospitalized post-clearance cohort, n=173). In order to differentiate these two cohorts, we used neural network models for the augmented curation of comorbidities and complications with positive sentiment in the EHR physician notes. In the year preceding COVID onset, acute kidney injury (n=15 (16.1%), p-value: 0.03), anemia (n=20 (21.5%), p-value: 0.02), and cardiac arrhythmia (n=21 (22.6%), p-value: 0.05) were significantly enriched in the physician notes of the hospitalized post-clearance cohort. This study highlights that these specific pre-existing conditions are associated with amplified hospitalization risk in long-COVID patients, despite their successful SARS-CoV-2 viral clearance. Our findings motivate follow-up prospective research into specific risk factors that predispose some patients towards the long-COVID syndrome.

As the COVID-19 vaccination campaign unfolds as one of the most rapid and widespread in history, it is important to continuously assess the real world safety of the FDA-authorized vaccines. Curation from large-scale electronic health records (EHRs) allows for near real-time safety evaluations that were not previously possible. Here, we advance context- and sentiment-aware deep neural networks over the multi-state Mayo Clinic enterprise (Minnesota, Arizona, Florida, Wisconsin) for automatically curating the adverse effects mentioned by physicians in over 108,000 EHR clinical notes between December 1st 2020 to February 8th 2021. We retrospectively compared the clinical notes of 31,069 individuals who received at least one dose of the Pfizer/BioNTech or Moderna vaccine to those of 31,069 unvaccinated individuals who were propensity matched by demographics, residential location, and history of prior SARS-CoV-2 testing. We find that vaccinated and unvaccinated individuals were seen in the the clinic at similar rates within 21 days of the first or second actual or assigned vaccination dose (first dose Odds Ratio = 1.13, 95% CI: 1.09-1.16; second dose Odds Ratio = 0.89, 95% CI: 0.84-0.93). Further, the incidence rates of all surveyed adverse effects were similar or lower in vaccinated individuals compared to unvaccinated individuals after either vaccine dose. Finally, the most frequently documented adverse effects within 7 days of each vaccine dose were fatigue (Dose 1: 1.77%, Dose 2: 1.2%),nausea (Dose 1: 1.05%, Dose 2: 0.84%), myalgia (Dose 1: 0.67%; Dose 2: 0.66%), diarrhea (Dose 1: 0.67%; Dose 2: 0.46%), arthralgia (Dose 1: 0.64%; Dose 2: 0.57%), erythema (Dose 1: 0.59%; Dose 2: 0.46%), vomiting (Dose 1: 0.45%, Dose 2: 0.29%) and fever (Dose 1: 0.29%; Dose 2: 0.23%). These remarkably low frequencies of adverse effects recorded in EHRs versus those derived from active solicitation during clinical trials (arthralgia: 24-46%; erythema: 9.5-14.7%; myalgia: 38-62%; fever: 14.2-15.5%) emphasize the rarity of vaccine-associated adverse effects requiring clinical attention. This rapid and timely analysis of vaccine-related adverse effects from contextually rich EHR notes of 62,138 individuals, which was enabled through a large scale Artificial Intelligence (AI)-powered platform, reaffirms the safety and tolerability of the FDA-authorized COVID-19 vaccines in practice.

Patients rehospitalized after SARS-CoV-2 clearance have distinct laboratory test profiles Rehospitalized patients have lower hemoglobin before and during SARS-CoV-2 infection Rehospitalized patients are more likely to experience anemia during active infection