Assessment of the Performance of Expired and Unexpired BinaxNOW COVID Rapid Antigen Test Kits Using Positive and Negative Controls (preprint)

Rapid antigen tests are widely used to diagnose infection with SARS-CoV-2, and millions of kits have been distributed for free by government agencies. However, unused and expired kits beyond their final expiration dates remain prevalent in people's homes. This study aimed to determine the accuracy of expired BinaxNOW COVID rapid antigen test kits. 100 expired and 100 unexpired test kits were checked for sensitivity and specificity using positive and negative controls, respectively. The results showed that there was no change in the sensitivity and specificity of BinaxNOW COVID rapid antigen test kits four months beyond the manufacturer-extended expiration date when using manufacturer-provided positive controls. The findings provide confidence in the accuracy of expired test kits, which could potentially reduce waste and strengthen supply chain resilience for pandemic preparedness. Further research utilizing actual human specimens can help determine the true accuracy of expired rapid antigen test kits in clinical use.

Feasibility of and Experience with Telehealth Based Patient Self-referral for COVID-19 Monoclonal Antibody Therapy (preprint)

BackgroundMonoclonal antibody (mAb) treatment for COVID-19 has been underutilized due to logistical challenges, lack of access and variable treatment awareness among patients and providers. The use of telehealth during the pandemic provides an opportunity to increase access to COVID care. MethodsThis is a single-center descriptive study of telehealth-based patient self-referral for mAb therapy between March 1, 2021 to October 31, 2021 at Baltimore Convention Center Field Hospital (BCCFH). ResultsAmong the 1001 self-referral patients, the mean age was 47, and most were female (57%) white (66%), and had a primary care provider (62%). During the study period, self-referrals increased from 14 per month in March to 427 in October resulting in a 30-fold increase. About 57% of self-referred patients received a telehealth visit, and of those 82% of patients received mAb infusion therapy, either onsite or at other infusion sites. The median time from self-referral to onsite infusion was 2 days (1-3 IQR). DiscussionOur study shows the integration of telehealth with a self-referral process improved access to mAb infusion. A high proportion of self-referrals were appropriate and led to timely treatment. Incorporation of self-referral and telehealth for monoclonal antibody therapy led to successful timely infusions. This approach helped those without traditional avenues for care and avoided potential delay for patients seeking referral from their medical providers.

Racial Disparities in Hesitancy and Utilization of Monoclonal Antibody Infusion Treatment of COVID-19 (preprint)

Background and MethodsWe conducted a single center cross-sectional study to investigate racial disparities in the hesitancy and utilization of monoclonal antibody (mAb) treatment of COVID-19 among treatment eligible patients who were referred to the infusion center between January 4, 2021 and May 14, 2021. ResultsAmong the 2,406 eligible participants, African Americans were significantly more likely to underutilize mAb treatment (OR 1.8; 95% CI 1.5-2.1) and miss treatment opportunities due to monoclonal hesitancy (OR 1.7, 95% CI 1.3-2.1). ConclusionAddressing racial disparities in mAb delivery is an opportunity to bridge the racial inequities in COVID-19 care.

High Sensitivity and NPV for BinaxNOW Rapid Antigen Test in Children at a Mass Testing Site During Prevalent Delta Variant (preprint)

SARS-CoV-2 continues to develop new, increasingly infectious variants, such as delta and omicron. Here, we evaluate the efficacy of the Abbott BinaxNOW Rapid Antigen Test against the gold standard of Reverse Transcription Polymerase Chain Reaction (RT-PCR) in 1054 pediatric participants presenting to a state-owned high-volume Coronavirus Disease 2019 (COVID-19) testing site. During the testing period, the delta variant was predominant. Prior to sample collection, symptomatic and exposure status was collected for all participants based on Centers for Disease Control (CDC) criteria. RT-PCR results demonstrated an overall prevalence rate of 5.2%. For all participants, the sensitivity of the rapid antigen tests was 92.7% (95% CI 82.4% - 98.0%) and specificity was 98.0% (95% CI 97.0%-98.8%). For symptomatic participants, the sensitivity was 92.3% (95% CI 74.9% - 99.1%), specificity was 96.6% (95% CI 93.6%- 98.4%), positive predictive value (PPV) was 72.7% (95% CI 54.5% - 86.7%) and negative predictive value (NPV) was 99.2% (95% CI 98.2% - 100%). Among asymptomatic participants, the sensitivity was 92.6% (95% CI 75.7% - 99.1%), specificity was 98.6% (95% CI 97.5% - 99.3%) the PPV was 71.4% (95% CI 53.7% - 85.4%) and the NPV was 99.7% (95% CI 99.0% - 100%). Our reported sensitivity and NPV are higher than other pediatric studies, but specificity and PPV are lower. Importance Children are especially impacted by the disease and its ability to disrupt educational opportunities. Although vaccinations have been approved for children 5 years and older, many children remain unvaccinated. Widespread testing may improve the ability for children to remain in in-person activities, minimizing absences from school and extracurriculars. Highly accurate rapid antigen tests may be vital to containing future COVID-19 waves while mitigating detrimental effects.

Molecular signature of postmortem lung tissue from COVID-19 patients suggests distinct trajectories driving mortality (preprint)

The precise molecular mechanisms behind severe life-threatening lung abnormalities during severe SARS-CoV-2 infections are still unclear. To address this challenge, we performed whole transcriptome sequencing of lung autopsies from 31 patients suffering from severe COVID-19 related complications and 10 uninfected controls. Using a metatranscriptome analysis of lung tissue samples we identified the existence of two distinct molecular signatures of lethal COVID-19. The dominant "classical" signature (n=23) showed upregulation of unfolded protein response, steroid biosynthesis and complement activation supported by massive metabolic reprogramming leading to characteristic lung damage. The rarer signature (n=8) potentially representing "Cytokine Release Syndrome" (CRS) showed upregulation of IL1 cytokines such CCL19 but absence of complement activation and muted inflammation. Further, dissecting expression of individual genes within enriched pathways for patient signature suggests heterogeneity in host response to the primary infection. We found that the majority of patients cleared the SARS-CoV-2 infection, but all suffered from acute dysbiosis with characteristic enrichment of opportunistic pathogens such as Gordonia bronchialis in "classical" patients and Staphylococcus warneri in CRS patients. Our results suggest two distinct models of lung pathology in severe COVID-19 patients that can be identified through the status of the complement activation, presence of specific cytokines and characteristic microbiome. This information can be used to design personalized therapy to treat COVID-19 related complications corresponding to patient signature such as using the identified drug molecules or mitigating specific secondary infections.

Severity and Mortality Prediction Models to Triage Indian COVID-19 Patients (preprint)

As the second wave in India mitigates, COVID-19 has now infected about 29 million patients countrywide, leading to more than 350 thousand people dead. As the infections surged, the strain on the medical infrastructure in the country became apparent. While the country vaccinates its population, opening up the economy may lead to an increase in infection rates. In this scenario, it is essential to effectively utilize the limited hospital resources by an informed patient triaging system based on clinical parameters. Here, we present two interpretable machine learning models predicting the clinical outcomes, severity, and mortality, of the patients based on routine non-invasive surveillance of blood parameters from one of the largest cohorts of Indian patients at the day of admission. Patient severity and mortality prediction models achieved 86.3% and 88.06% accuracy, respectively, with an AUC-ROC of 0.91 and 0.92. We have integrated both the models in a user-friendly web app calculator, https://triage-COVID-19.herokuapp.com/, to showcase the potential deployment of such efforts at scale.

Genetic risk prediction of COVID-19 susceptibility and severity in the Indian population (preprint)

Host genetic variants can determine the susceptibility to COVID-19 infection and severity as noted in a recent Genome-wide Association Study (GWAS) by Pairo-Castineira et al.1. Given the prominent genetic differences in Indian sub-populations as well as differential prevalence of COVID-19, here, we deploy the previous study and compute genetic risk scores in different Indian sub-populations that may predict the severity of COVID-19 outcomes in them. We computed polygenic risk scores (PRSs) in different Indian sub-populations with the top 100 single-nucleotide polymorphisms (SNPs) with a p-value cutoff of 10-6 derived from the previous GWAS summary statistics1. We selected SNPs overlapping with the Indian Genome Variation Consortium (IGVC) and with similar frequencies in the Indian population. For each population, median PRS was calculated, and a correlation analysis was performed to test the association of these genetic risk scores with COVID-19 mortality. We found a varying distribution of PRS in Indian sub-populations. Correlation analysis indicates a positive linear association between PRS and COVID-19 deaths. This was not observed with non-risk alleles in Indian sub-populations. Our analyses suggest that Indian sub-populations differ with respect to the genetic risk for developing COVID-19 mediated critical illness. Combining PRSs with other observed risk-factors in a Bayesian framework can provide a better prediction model for ascertaining high COVID-19 risk groups. This has a potential utility in the design of more effective vaccine disbursal schemes.

Challenges in the application of a mortality prediction model for COVID-19 patients on an Indian cohort (preprint)

Many countries are now experiencing the third wave of the COVID-19 pandemic straining the healthcare resources with an acute shortage of hospital beds and ventilators for the critically ill patients. This situation is especially worse in India with the second largest load of COVID-19 cases and a relatively resource-scarce medical infrastructure. Therefore, it becomes essential to triage the patients based on the severity of their disease and devote resources towards critically ill patients. Yan et al. 1 have published a very pertinent research that uses Machine learning (ML) methods to predict the outcome of COVID-19 patients based on their clinical parameters at the day of admission. They used the XGBoost algorithm, a type of ensemble model, to build the mortality prediction model. The final classifier is built through the sequential addition of multiple weak classifiers. The clinically operable decision rule was obtained from a 'single-tree XGBoost' and used lactic dehydrogenase (LDH), lymphocyte and high-sensitivity C-reactive protein (hs-CRP) values. This decision tree achieved a 100% survival prediction and 81% mortality prediction. However, these models have several technical challenges and do not provide an out of the box solution that can be deployed for other populations as has been reported in the "Matters Arising" section of Yan et al. Here, we show the limitations of this model by deploying it on one of the largest datasets of COVID-19 patients containing detailed clinical parameters collected from India.

Integrated analysis of bulk multi omic and single-cell sequencing data confirms the molecular origin of hemodynamic changes in Covid-19 infection explaining coagulopathy and higher geriatric mortality (preprint)

Besides severe respiratory distress, recent reports in Covid-19 patients have found a strong association between platelet counts and patient survival. Along with hemodynamic changes such as prolonged clotting time, high fibrin degradation products and D-dimers, increased levels of monocytes with disturbed morphology have also been identified. In this study, through an integrated analysis of bulk RNA-sequencing data from Covid-19 patients with data from single-cell sequencing studies on lung tissues, we found that most of the cell-types that contributed to the altered gene expression were of hematopoietic origin. We also found that differentially expressed genes in Covid-19 patients formed a significant pool of the expressing genes in phagocytic cells such as Monocytes and Platelets. Interestingly, while we observed a general enrichment for Monocytes in Covid-19 patients, we found that the signal for FCGRA3+ Monocytes was depleted. Further, we found evidence that age-associated gene expression changes in Monocytes and Platelets, associated with inflammation, mirror gene expression changes in Covid-19 patients suggesting that pro-inflammatory signalling during aging may worsen the infection in older patients. We identified more than 20 genes that change in the same direction between Covid-19 infection and aging cells that may act as potential therapeutic targets. Of particular interest were IL2RG, GNLY and GMZA expressed in Platelets, which facilitates cytokine signalling in Monocytes through an interaction with Platelets. To understand whether infection can directly manipulate the biology of Monocytes and Platelets, we hypothesize that these non-ACE2 expressing cells may be infected by the virus through the phagocytic route. We observed that phagocytic cells such as Monocytes, T-cells, and Platelets have a significantly higher expression of genes that are a part of the Covid-19 viral interactome. Hence these cell-types may have an active rather than a reactive role in viral pathogenesis to manifest clinical symptoms such as coagulopathy. Therefore, our results present molecular evidence for pursuing both anti-inflammatory and anticoagulation therapy for better patient management especially in older patients.