Real-world effectiveness of Ad26.COV2.S adenoviral vector vaccine for COVID-19

In light of the massive and rapid vaccination campaign against COVID-19, continuous real-world effectiveness and safety assessment of the FDA-authorized vaccines is critical to amplify transparency, build public trust, and ultimately improve overall health outcomes. In this study, we leveraged large-scale longitudinal curation of electronic health records (EHRs) from the multi-state Mayo Clinic health system (MN, AZ, FL, WN, IA). We compared the infection rate of 2,195 individuals who received a single dose of the Ad26.COV2.S vaccine from Johnson & Johnson (J&J) to the infection rate of 21,950 unvaccinated, propensity-matched individuals between February 27th and April 14th 2021. Of the 1,779 vaccinated individuals with at least two weeks of follow-up, only 3 (0.17%) tested positive for SARS-CoV-2 15 days or more after vaccination compared to 128 of 17,744 (0.72%) unvaccinated individuals (4.34 fold reduction rate). This corresponds to a vaccine effectiveness of 76.7% (95% CI: 30.3-95.3%) in preventing SARS-CoV-2 infection with onset at least two weeks after vaccination. This data is consistent with the clinical trial-reported efficacy of Ad26.COV2.S in preventing moderate to severe COVID-19 with onset at least 14 days after vaccine administration (66.9%; 95% CI: 59.0-73.4%). Due to the recent authorization of the Ad26.COV2.S vaccine, there are not yet enough hospitalizations, ICU admissions, or deaths within this cohort to robustly assess the effect of vaccination on COVID-19 severity, but these outcomes will be continually assessed in near-real-time with our platform. Collectively, this study provides further evidence that a single dose of Ad26.COV2.S is highly effective in preventing SARS-CoV-2 infection and reaffirms the urgent need to continue mass vaccination efforts globally.

Real-time analysis of a mass vaccination effort via an Artificial Intelligence platform confirms the safety of FDA-authorized COVID-19 vaccines

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 arthralgia (Dose 1: 0.59%; Dose 2: 0.39%), diarrhea (Dose 1: 0.58%; Dose 2: 0.33%), erythema (Dose 1: 0.51%; Dose 2: 0.31%), myalgia (Dose 1: 0.40%; Dose 2: 0.34%), and fever (Dose 1: 0.27%; Dose 2: 0.31%). 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.

Plasma IL-6 Levels following Corticosteroid Therapy as an Indicator of ICU Length of Stay in Critically ill COVID-19 Patients

Intensive Care Unit (ICU) admissions and mortality in severe COVID-19 patients are driven by "cytokine storms" and acute respiratory distress syndrome (ARDS). Interim clinical trial results suggest that the corticosteroid dexamethasone displays superior 28-day survival in severe COVID-19 patients requiring ventilation or oxygen. Among 16 patients with plasma IL-6 measurement post-corticosteroid administration, a higher proportion of patients with an IL-6 value over 10 pg/mL have worse outcomes (i.e. ICU Length of Stay > 15 days or death) when compared to 41 patients treated with non-corticosteroid drugs including antivirals, tocilizumab, azithromycin, and hydroxychloroquine (p-value = 0.0024). Given this unexpected clinical association between post-corticosteroid IL-6 levels and COVID-19 severity, we hypothesized that the Glucocorticoid Receptor (GR or NR3C1) may be coupled to IL-6 expression in specific cell types that govern cytokine release syndrome (CRS). Examining single cell RNA-seq data from bronchoalveolar lavage fluid of severe COVID-19 patients and nearly 2 million human cells from a pan-tissue scan shows that alveolar macrophages, smooth muscle cells, and endothelial cells co-express both NR3C1 and IL-6. The mechanism of Glucocorticoid Receptor (GR) agonists mitigating pulmonary and multi-organ inflammation in some COVID-19 patients with respiratory failure, may be in part due to their successful antagonism of IL-6 production within lung macrophages and vasculature.

Longitudinal laboratory testing tied to PCR diagnostics in COVID-19 patients reveals temporal evolution of distinctive coagulopathy signatures

Temporal inference from laboratory testing results and their triangulation with clinical outcomes as described in the associated unstructured text from the providers notes in the Electronic Health Record (EHR) is integral to advancing precision medicine. Here, we studied 181 COVIDpos and 7,775 COVIDneg patients subjected to 1.3 million laboratory tests across 194 assays during a two-month observation period centered around their SARS-CoV-2 PCR testing dates. We found that compared to COVIDneg at the time of clinical presentation and diagnostic testing, COVIDpos patients tended to have higher plasma fibrinogen levels and similarly low platelet counts, with approximately 25% of patients in both cohorts showing outright thrombocytopenia. However, these measures show opposite longitudinal trends as the infection evolves, with declining fibrinogen and increasing platelet counts to levels that are lower and higher compared to the COVIDneg cohort, respectively. Our EHR augmented curation efforts suggest a minority of patients develop thromboembolic events after the PCR testing date, including rare cases with disseminated intravascular coagulopathy (DIC), with most patients lacking the platelet reductions typically observed in consumptive coagulopathies. These temporal trends present, for the first time, fine-grained resolution of COVID-19 associated coagulopathy (CAC), via a digital framework that synthesizes longitudinal lab measurements with structured medication data and neural network-powered extraction of outcomes from the unstructured EHR. This study demonstrates how a precision medicine platform can help contextualize each patients specific coagulation profile over time, towards the goal of informing better personalization of thromboprophylaxis regimen.

Augmented Curation of Unstructured Clinical Notes from a Massive EHR System Reveals Specific Phenotypic Signature of Impending COVID-19 Diagnosis

Understanding temporal dynamics of COVID-19 patient symptoms could provide fine-grained resolution to guide clinical decision-making. Here, we use deep neural networks over an institution-wide platform for the augmented curation of clinical notes from 77,167 patients subjected to COVID-19 PCR testing. By contrasting Electronic Health Record (EHR)-derived symptoms of COVID-19-positive (COVIDpos; n=2,317) versus COVID-19-negative (COVIDneg; n=74,850) patients for the week preceding the PCR testing date, we identify anosmia/dysgeusia (27.1-fold), fever/chills (2.6-fold), respiratory difficulty (2.2-fold), cough (2.2-fold), myalgia/arthralgia (2-fold), and diarrhea (1.4-fold) as significantly amplified in COVIDpos over COVIDneg patients. The combination of cough and fever/chills has 4.2-fold amplification in COVIDpos patients during the week prior to PCR testing, and along with anosmia/dysgeusia, constitutes the earliest EHR-derived signature of COVID-19. This study introduces an Augmented Intelligence platform for the real-time synthesis of institutional biomedical knowledge. The platform holds tremendous potential for scaling up curation throughput, thus enabling EHR-powered early disease diagnosis.

Knowledge synthesis from 100 million biomedical documents augments the deep expression profiling of coronavirus receptors

The COVID-19 pandemic demands assimilation of all available biomedical knowledge to decode its mechanisms of pathogenicity and transmission. Despite the recent renaissance in unsupervised neural networks for decoding unstructured natural languages, a platform for the real-time synthesis of the exponentially growing biomedical literature and its comprehensive triangulation with deep omic insights is not available. Here, we present the nferX platform for dynamic inference from over 45 quadrillion possible conceptual associations extracted from unstructured biomedical text, and their triangulation with Single Cell RNA-sequencing based insights from over 25 tissues. Using this platform, we identify intersections between the pathologic manifestations of COVID-19 and the comprehensive expression profile of the SARS-CoV-2 receptor ACE2. We find that tongue keratinocytes, airway club cells, and ciliated cells are likely underappreciated targets of SARS-CoV-2 infection, in addition to type II pneumocytes and olfactory epithelial cells. We further identify mature small intestinal enterocytes as a possible hotspot of COVID-19 fecal-oral transmission, where an intriguing maturation-correlated transcriptional signature is shared between ACE2 and the other coronavirus receptors DPP4 (MERS-CoV) and ANPEP (-coronavirus). This study demonstrates how a holistic data science platform can leverage unprecedented quantities of structured and unstructured publicly available data to accelerate the generation of impactful biological insights and hypotheses. The nferX Platform Single-cell resource - https://academia.nferx.com/