COVID-19 severity prediction based on patient risk factors and number of vaccines received

BackgroundVaccines are highly effective in preventing severe disease and death from COVID-19, and new medications that can reduce severity of disease have been approved. However, many countries are facing limited supply of vaccine doses and medications. A model estimating the probabilities for hospitalization and mortality according to individual risk factors and vaccine doses received could help prioritize vaccination and yet scarce medications to maximize lives saved and reduce the burden on hospitalization facilities. MethodsElectronic health records from 101,039 individuals infected with SARS-CoV-2, since the beginning of the pandemic and until November 30, 2021 were extracted from a national healthcare organization in Israel. Logistic regression models were built to estimate the risk for subsequent hospitalization and death based on the number of BNT162b2 mRNA vaccine doses received and few major risk factors (age, sex, body mass index, hemoglobin A1C, kidney function, and presence of hypertension, pulmonary disease and malignancy). ResultsThe models built predict the outcome of newly infected individuals with remarkable accuracy: area under the curve was 0.889 for predicting hospitalization, and 0.967 for predicting mortality. Even when a breakthrough infection occurs, having received three vaccination doses significantly reduces the risk of hospitalization by 66% (OR=0.339) and of death by 78% (OR=0.223). ConclusionsThe models enable rapid identification of individuals at high risk for hospitalization and death when infected. These patients can be prioritized to receive booster vaccination and the yet scarce medications. A calculator based on these models is made publicly available on http://covidest.web.app

Synthetic lethality-based prediction of anti-SARS-CoV-2 targets

Novel strategies are needed to identify drug targets and treatments for the COVID-19 pandemic. The altered gene expression of virus-infected host cells provides an opportunity to specifically inhibit viral propagation via targeting the synthetic lethal (SL) partners of such altered host genes. Pursuing this antiviral strategy, here we comprehensively analyzed multiple in vitro and in vivo bulk and single-cell RNA-sequencing datasets of SARS-CoV-2 infection to predict clinically relevant candidate antiviral targets that are SL with altered host genes. The predicted SL-based targets are highly enriched for infected cell inhibiting genes reported in four SARS-CoV-2 CRISPR-Cas9 genome-wide genetic screens. Integrating our predictions with the results of these screens, we further selected a focused subset of 26 genes that we experimentally tested in a targeted siRNA screen using human Caco-2 cells. Notably, as predicted, knocking down these targets reduced viral replication and cell viability only under the infected condition without harming non-infected cells. Our results are made publicly available, to facilitate their in vivo testing and further validation.

Large-scale study of antibody titer decay following BNT162b2 mRNA vaccine or SARS-CoV-2 infection

BackgroundImmune protection following either vaccination or infection with SARS-CoV-2 decreases over time. ObjectiveTo determine the kinetics of SARS-CoV-2 IgG antibodies following administration of two doses of BNT162b2 vaccine, or SARS-CoV-2 infection in unvaccinated individuals. MethodsAntibody titers were measured between January 31, 2021, and July 31, 2021 in two mutually exclusive groups: i) vaccinated individuals who received two doses of BNT162b2 vaccine and had no history of previous infection with COVID-19 and ii) SARS-CoV-2 convalescents who had not received the vaccine. ResultsA total of 2,653 individuals fully vaccinated by two doses of vaccine during the study period and 4,361 convalescent patients were included. Higher SARS-CoV-2 IgG antibody titers were observed in vaccinated individuals (median 1581 AU/mL IQR [533.8-5644.6]) after the second vaccination, than in convalescent individuals (median 355.3 AU/mL IQR [141.2-998.7]; p<0.001). In vaccinated subjects, antibody titers decreased by up to 40% each subsequent month while in convalescents they decreased by less than 5% per month. Six months after BNT162b2 vaccination 16.1% subjects had antibody levels below the seropositivity threshold of <50 AU/mL, while only 10.8% of convalescent patients were below <50 AU/mL threshold after 9 months from SARS-CoV-2 infection. ConclusionsThis study demonstrates individuals who received the Pfizer-BioNTech mRNA vaccine have different kinetics of antibody levels compared to patients who had been infected with the SARS-CoV-2 virus, with higher initial levels but a much faster exponential decrease in the first group. FundingThis research was internally funded by Leumit Health Services (LHS) and was supported in part by the Intramural Research Program, National Institutes of Health, National Cancer Institute, Center for Cancer Research. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government. Impact statementLarge scale study display the kinetics of SARS-CoV-2 IgG antibodies present in individuals vaccinated with two doses of mRNA vaccine vs. unvaccinated patients who had recovered from the disease: initial levels of antibody are much higher in vaccinated patients, but decrease faster.

Elapsed time since BNT162b2 vaccine and risk of SARS-CoV-2 infection in a large cohort

ImportanceIsrael was among the first countries to launch a large-scale COVID-19 vaccination campaign, and quickly vaccinated its population, achieving early control over the spread of the virus. However, the number of COVID-19 cases is now rapidly increasing, which may indicate that vaccine protection decreases over time. ObjectiveTo determine whether time elapsed since the second BNT162b2 messenger RNA (mRNA) vaccine (Pfizer-BioNTech) injection is significantly associated with the risk of post-vaccination COVID-19 infection. DesignThis is a retrospective cohort study performed in a large state-mandated health care organization in Israel. ParticipantsAll fully vaccinated adults who have received a RT-PCR test between May 15, 2021 and July 26, 2021, at least two weeks after their second vaccine injection were included. Patients with a history of past COVID-19 infection were excluded. Main Outcome and MeasurePositive result for the RT-PCR test. ResultsThe cohort included 33,993 fully vaccinated adults, 49% women, with a mean age of 47 years (SD, 17 years), who received an RT-PCR test for SARS-CoV-2 during the study period. The median time between the second dose of the vaccine and the RT-PCR test was 146 days, interquartile range [121-167] days. 608 (1.8%) patients had positive test results. There was a significantly higher rate of positive results among patients who received their second vaccine dose at least 146 days before the RT-PCR test compared to patients who have received their vaccine less than 146 days before: odds ratio for infection was 3.00 for patients aged over 60 (95% CI 1.86-5.11); 2.29 for patients aged between 40 and 59 (95% CI 1.67-3.17); and 1.74 for patients aged between 18 and 39 (95% CI 1.27-2.37); P<0.001 in each age group. Conclusions and RelevanceIn this large population study of patients tested for SARS-CoV-2 by RT-PCR following two doses of mRNA BNT162b2 vaccine, we observe a significant increase of the risk of infection in individuals who received their last vaccine dose since at least 146 days ago, particularly among patients older than 60.

Genome-scale metabolic modeling reveals SARS-CoV-2-induced host metabolic reprogramming and identifies metabolic antiviral targets

Tremendous progress has been made to control the COVID-19 pandemic caused by the SARS-CoV-2 virus. However, effective therapeutic options are still rare. Drug repurposing and combination represent practical strategies to address this urgent unmet medical need. Viruses, including coronaviruses, are known to hijack host metabolism to facilitate viral proliferation, making targeting host metabolism a promising antiviral approach. Here, we describe an integrated analysis of 12 published in vitro and human patient gene expression datasets on SARS-CoV-2 infection using genome-scale metabolic modeling (GEM), revealing complicated host metabolism reprogramming during SARS-CoV-2 infection. We next applied the GEM-based metabolic transformation algorithm to predict anti-SARS-CoV-2 targets that counteract the virus-induced metabolic changes. We successfully validated these targets using published drug and genetic screen data and by performing an siRNA assay in Caco-2 cells. Further generating and analyzing RNA-sequencing data of remdesivir-treated Vero E6 cell samples, we predicted metabolic targets acting in combination with remdesivir, an approved anti-SARS-CoV-2 drug. Our study provides clinical data-supported candidate anti-SARS-CoV-2 targets for future evaluation, demonstrating host metabolism-targeting as a promising antiviral strategy.

Systematic analysis of electronic health records identifies drugs reducing risk of COVID-19 hospitalization and severity

BackgroundUntil COVID-19 drugs specifically developed to treat COVID-19 become more widely accessible, it is crucial to identify whether existing medications have a protective effect against severe disease. Towards this objective, we conducted a large population study in Clalit Health Services (CHS), the largest healthcare provider in Israel, insuring over 4.7 million members. MethodsTwo case-control matched cohorts were assembled to assess which medications, acquired in the last month, decreased the risk of COVID-19 hospitalization. Case patients were adults aged 18-95 hospitalized for COVID-19. In the first cohort, five control patients, from the general population, were matched to each case (n=6202); in the second cohort, two non-hospitalized SARS-CoV-2 positive control patients were matched to each case (n=6919). The outcome measures for a medication were: odds ratio (OR) for hospitalization, 95% confidence interval (CI), and the p-value, using Fishers exact test. False discovery rate was used to adjust for multiple testing. ResultsMedications associated with most significantly reduced odds for COVID-19 hospitalization include: ubiquinone (OR=0.185, 95% CI (0.058 to 0.458), p<0.001), ezetimibe (OR=0.488, 95% CI ((0.377 to 0.622)), p<0.001), rosuvastatin (OR=0.673, 95% CI (0.596 to 0.758), p<0.001), flecainide (OR=0.301, 95% CI (0.118 to 0.641), p<0.001), and vitamin D (OR=0.869, 95% CI (0.792 to 0.954), p<0.003). Remarkably, acquisition of artificial tears, eye care wipes, and several ophthalmological products were also associated with decreased risk for hospitalization. ConclusionsUbiquinone, ezetimibe and rosuvastatin, all related to the cholesterol synthesis pathway were associated with reduced hospitalization risk. These findings point to a promising protective effect which should be further investigated in controlled, prospective studies. FundingThis research was supported in part by the Intramural Research Program of the National Institutes of Health, NCI.