Genome-wide CRISPR Screens Reveal Host Factors Critical for SARS-CoV-2 Infection.

Identification of host genes essential for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection may reveal novel therapeutic targets and inform our understanding of coronavirus disease 2019 (COVID-19) pathogenesis. Here we performed genome-wide CRISPR screens in Vero-E6 cells with SARS-CoV-2, Middle East respiratory syndrome CoV (MERS-CoV), bat CoV HKU5 expressing the SARS-CoV-1 spike, and vesicular stomatitis virus (VSV) expressing the SARS-CoV-2 spike. We identified known SARS-CoV-2 host factors, including the receptor ACE2 and protease Cathepsin L. We additionally discovered pro-viral genes and pathways, including HMGB1 and the SWI/SNF chromatin remodeling complex, that are SARS lineage and pan-coronavirus specific, respectively. We show that HMGB1 regulates ACE2 expression and is critical for entry of SARS-CoV-2, SARS-CoV-1, and NL63. We also show that small-molecule antagonists of identified gene products inhibited SARS-CoV-2 infection in monkey and human cells, demonstrating the conserved role of these genetic hits across species. This identifies potential therapeutic targets for SARS-CoV-2 and reveals SARS lineage-specific and pan-CoV host factors that regulate susceptibility to highly pathogenic CoVs.

Immune dysregulation and autoreactivity correlate with disease severity in SARS-CoV-2-associated multisystem inflammatory syndrome in children.

Multisystem inflammatory syndrome in children (MIS-C) is a life-threatening post-infectious complication occurring unpredictably weeks after mild or asymptomatic SARS-CoV-2 infection. We profiled MIS-C, adult COVID-19, and healthy pediatric and adult individuals using single-cell RNA sequencing, flow cytometry, antigen receptor repertoire analysis, and unbiased serum proteomics, which collectively identified a signature in MIS-C patients that correlated with disease severity. Despite having no evidence of active infection, MIS-C patients had elevated S100A-family alarmins and decreased antigen presentation signatures, indicative of myeloid dysfunction. MIS-C patients showed elevated expression of cytotoxicity genes in NK and CD8+ T cells and expansion of specific IgG-expressing plasmablasts. Clinically severe MIS-C patients displayed skewed memory T cell TCR repertoires and autoimmunity characterized by endothelium-reactive IgG. The alarmin, cytotoxicity, TCR repertoire, and plasmablast signatures we defined have potential for application in the clinic to better diagnose and potentially predict disease severity early in the course of MIS-C.

Single-cell longitudinal analysis of SARS-CoV-2 infection in human airway epithelium identifies target cells, alterations in gene expression, and cell state changes.

There are currently limited Food and Drug Administration (FDA)-approved drugs and vaccines for the treatment or prevention of Coronavirus Disease 2019 (COVID-19). Enhanced understanding of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection and pathogenesis is critical for the development of therapeutics. To provide insight into viral replication, cell tropism, and host-viral interactions of SARS-CoV-2, we performed single-cell (sc) RNA sequencing (RNA-seq) of experimentally infected human bronchial epithelial cells (HBECs) in air-liquid interface (ALI) cultures over a time course. This revealed novel polyadenylated viral transcripts and highlighted ciliated cells as a major target at the onset of infection, which we confirmed by electron and immunofluorescence microscopy. Over the course of infection, the cell tropism of SARS-CoV-2 expands to other epithelial cell types including basal and club cells. Infection induces cell-intrinsic expression of type I and type III interferons (IFNs) and interleukin (IL)-6 but not IL-1. This results in expression of interferon-stimulated genes (ISGs) in both infected and bystander cells. This provides a detailed characterization of genes, cell types, and cell state changes associated with SARS-CoV-2 infection in the human airway.

Prediction of neuropathologic lesions from clinical data.

INTRODUCTION: Post-mortem analysis provides definitive diagnoses of neurodegenerative diseases; however, only a few can be diagnosed during life. METHODS: This study employed statistical tools and machine learning to predict 17 neuropathologic lesions from a cohort of 6518 individuals using 381 clinical features (Table S1). The multisite data allowed validation of the model's robustness by splitting train/test sets by clinical sites. A similar study was performed for predicting Alzheimer's disease (AD) neuropathologic change without specific comorbidities. RESULTS: Prediction results show high performance for certain lesions that match or exceed that of research annotation. Neurodegenerative comorbidities in addition to AD neuropathologic change resulted in compounded, but disproportionate, effects across cognitive domains as the comorbidity number increased. DISCUSSION: Certain clinical features could be strongly associated with multiple neurodegenerative diseases, others were lesion-specific, and some were divergent between lesions. Our approach could benefit clinical research, and genetic and biomarker research by enriching cohorts for desired lesions.

Development and Validation of the Quick COVID-19 Severity Index: A Prognostic Tool for Early Clinical Decompensation.

STUDY OBJECTIVE: The goal of this study is to create a predictive, interpretable model of early hospital respiratory failure among emergency department (ED) patients admitted with coronavirus disease 2019 (COVID-19). METHODS: This was an observational, retrospective, cohort study from a 9-ED health system of admitted adult patients with severe acute respiratory syndrome coronavirus 2 (COVID-19) and an oxygen requirement less than or equal to 6 L/min. We sought to predict respiratory failure within 24 hours of admission as defined by oxygen requirement of greater than 10 L/min by low-flow device, high-flow device, noninvasive or invasive ventilation, or death. Predictive models were compared with the Elixhauser Comorbidity Index, quick Sequential [Sepsis-related] Organ Failure Assessment, and the CURB-65 pneumonia severity score. RESULTS: During the study period, from March 1 to April 27, 2020, 1,792 patients were admitted with COVID-19, 620 (35%) of whom had respiratory failure in the ED. Of the remaining 1,172 admitted patients, 144 (12.3%) met the composite endpoint within the first 24 hours of hospitalization. On the independent test cohort, both a novel bedside scoring system, the quick COVID-19 Severity Index (area under receiver operating characteristic curve mean 0.81 [95% confidence interval {CI} 0.73 to 0.89]), and a machine-learning model, the COVID-19 Severity Index (mean 0.76 [95% CI 0.65 to 0.86]), outperformed the Elixhauser mortality index (mean 0.61 [95% CI 0.51 to 0.70]), CURB-65 (0.50 [95% CI 0.40 to 0.60]), and quick Sequential [Sepsis-related] Organ Failure Assessment (0.59 [95% CI 0.50 to 0.68]). A low quick COVID-19 Severity Index score was associated with a less than 5% risk of respiratory decompensation in the validation cohort. CONCLUSION: A significant proportion of admitted COVID-19 patients progress to respiratory failure within 24 hours of admission. These events are accurately predicted with bedside respiratory examination findings within a simple scoring system.

Longitudinal single-cell transcriptional dynamics throughout neurodegeneration in SCA1.

Neurodegeneration is a protracted process involving progressive changes in myriad cell types that ultimately results in the death of vulnerable neuronal populations. To dissect how individual cell types within a heterogeneous tissue contribute to the pathogenesis and progression of a neurodegenerative disorder, we performed longitudinal single-nucleus RNA sequencing of mouse and human spinocerebellar ataxia type 1 (SCA1) cerebellar tissue, establishing continuous dynamic trajectories of each cell population. Importantly, we defined the precise transcriptional changes that precede loss of Purkinje cells and, for the first time, identified robust early transcriptional dysregulation in unipolar brush cells and oligodendroglia. Finally, we applied a deep learning method to predict disease state accurately and identified specific features that enable accurate distinction of wild-type and SCA1 cells. Together, this work reveals new roles for diverse cerebellar cell types in SCA1 and provides a generalizable analysis framework for studying neurodegeneration.

Whole genome deconvolution unveils Alzheimer's resilient epigenetic signature.

Assay for Transposase Accessible Chromatin by sequencing (ATAC-seq) accurately depicts the chromatin regulatory state and altered mechanisms guiding gene expression in disease. However, bulk sequencing entangles information from different cell types and obscures cellular heterogeneity. To address this, we developed Cellformer, a deep learning method that deconvolutes bulk ATAC-seq into cell type-specific expression across the whole genome. Cellformer enables cost-effective cell type-specific open chromatin profiling in large cohorts. Applied to 191 bulk samples from 3 brain regions, Cellformer identifies cell type-specific gene regulatory mechanisms involved in resilience to Alzheimer's disease, an uncommon group of cognitively healthy individuals that harbor a high pathological load of Alzheimer's disease. Cell type-resolved chromatin profiling unveils cell type-specific pathways and nominates potential epigenetic mediators underlying resilience that may illuminate therapeutic opportunities to limit the cognitive impact of the disease. Cellformer is freely available to facilitate future investigations using high-throughput bulk ATAC-seq data.

Cross-species comparative analysis of single presynapses.

Comparing brain structure across species and regions enables key functional insights. Leveraging publicly available data from a novel mass cytometry-based method, synaptometry by time of flight (SynTOF), we applied an unsupervised machine learning approach to conduct a comparative study of presynapse molecular abundance across three species and three brain regions. We used neural networks and their attractive properties to model complex relationships among high dimensional data to develop a unified, unsupervised framework for comparing the profile of more than 4.5 million single presynapses among normal human, macaque, and mouse samples. An extensive validation showed the feasibility of performing cross-species comparison using SynTOF profiling. Integrative analysis of the abundance of 20 presynaptic proteins revealed near-complete separation between primates and mice involving synaptic pruning, cellular energy, lipid metabolism, and neurotransmission. In addition, our analysis revealed a strong overlap between the presynaptic composition of human and macaque in the cerebral cortex and neostriatum. Our unique approach illuminates species- and region-specific variation in presynapse molecular composition.

Deep representation learning identifies associations between physical activity and sleep patterns during pregnancy and prematurity.

Preterm birth (PTB) is the leading cause of infant mortality globally. Research has focused on developing predictive models for PTB without prioritizing cost-effective interventions. Physical activity and sleep present unique opportunities for interventions in low- and middle-income populations (LMICs). However, objective measurement of physical activity and sleep remains challenging and self-reported metrics suffer from low-resolution and accuracy. In this study, we use physical activity data collected using a wearable device comprising over 181,944 h of data across N = 1083 patients. Using a new state-of-the art deep learning time-series classification architecture, we develop a 'clock' of healthy dynamics during pregnancy by using gestational age (GA) as a surrogate for progression of pregnancy. We also develop novel interpretability algorithms that integrate unsupervised clustering, model error analysis, feature attribution, and automated actigraphy analysis, allowing for model interpretation with respect to sleep, activity, and clinical variables. Our model performs significantly better than 7 other machine learning and AI methods for modeling the progression of pregnancy. We found that deviations from a normal 'clock' of physical activity and sleep changes during pregnancy are strongly associated with pregnancy outcomes. When our model underestimates GA, there are 0.52 fewer preterm births than expected (P = 1.01e - 67, permutation test) and when our model overestimates GA, there are 1.44 times (P = 2.82e - 39, permutation test) more preterm births than expected. Model error is negatively correlated with interdaily stability (P = 0.043, Spearman's), indicating that our model assigns a more advanced GA when an individual's daily rhythms are less precise. Supporting this, our model attributes higher importance to sleep periods in predicting higher-than-actual GA, relative to lower-than-actual GA (P = 1.01e - 21, Mann-Whitney U). Combining prediction and interpretability allows us to signal when activity behaviors alter the likelihood of preterm birth and advocates for the development of clinical decision support through passive monitoring and exercise habit and sleep recommendations, which can be easily implemented in LMICs.

Data-driven longitudinal characterization of neonatal health and morbidity.

Although prematurity is the single largest cause of death in children under 5 years of age, the current definition of prematurity, based on gestational age, lacks the precision needed for guiding care decisions. Here, we propose a longitudinal risk assessment for adverse neonatal outcomes in newborns based on a deep learning model that uses electronic health records (EHRs) to predict a wide range of outcomes over a period starting shortly before conception and ending months after birth. By linking the EHRs of the Lucile Packard Children's Hospital and the Stanford Healthcare Adult Hospital, we developed a cohort of 22,104 mother-newborn dyads delivered between 2014 and 2018. Maternal and newborn EHRs were extracted and used to train a multi-input multitask deep learning model, featuring a long short-term memory neural network, to predict 24 different neonatal outcomes. An additional cohort of 10,250 mother-newborn dyads delivered at the same Stanford Hospitals from 2019 to September 2020 was used to validate the model. Areas under the receiver operating characteristic curve at delivery exceeded 0.9 for 10 of the 24 neonatal outcomes considered and were between 0.8 and 0.9 for 7 additional outcomes. Moreover, comprehensive association analysis identified multiple known associations between various maternal and neonatal features and specific neonatal outcomes. This study used linked EHRs from more than 30,000 mother-newborn dyads and would serve as a resource for the investigation and prediction of neonatal outcomes. An interactive website is available for independent investigators to leverage this unique dataset: https://maternal-child-health-associations.shinyapps.io/shiny_app/.

Large-scale correlation network construction for unraveling the coordination of complex biological systems.

Advanced measurement and data storage technologies have enabled high-dimensional profiling of complex biological systems. For this, modern multiomics studies regularly produce datasets with hundreds of thousands of measurements per sample, enabling a new era of precision medicine. Correlation analysis is an important first step to gain deeper insights into the coordination and underlying processes of such complex systems. However, the construction of large correlation networks in modern high-dimensional datasets remains a major computational challenge owing to rapidly growing runtime and memory requirements. Here we address this challenge by introducing CorALS (Correlation Analysis of Large-scale (biological) Systems), an open-source framework for the construction and analysis of large-scale parametric as well as non-parametric correlation networks for high-dimensional biological data. It features off-the-shelf algorithms suitable for both personal and high-performance computers, enabling workflows and downstream analysis approaches. We illustrate the broad scope and potential of CorALS by exploring perspectives on complex biological processes in large-scale multiomics and single-cell studies.

Revealing the impact of lifestyle stressors on the risk of adverse pregnancy outcomes with multitask machine learning.

Psychosocial and stress-related factors (PSFs), defined as internal or external stimuli that induce biological changes, are potentially modifiable factors and accessible targets for interventions that are associated with adverse pregnancy outcomes (APOs). Although individual APOs have been shown to be connected to PSFs, they are biologically interconnected, relatively infrequent, and therefore challenging to model. In this context, multi-task machine learning (MML) is an ideal tool for exploring the interconnectedness of APOs on the one hand and building on joint combinatorial outcomes to increase predictive power on the other hand. Additionally, by integrating single cell immunological profiling of underlying biological processes, the effects of stress-based therapeutics may be measurable, facilitating the development of precision medicine approaches. Objectives: The primary objectives were to jointly model multiple APOs and their connection to stress early in pregnancy, and to explore the underlying biology to guide development of accessible and measurable interventions. Materials and Methods: In a prospective cohort study, PSFs were assessed during the first trimester with an extensive self-filled questionnaire for 200 women. We used MML to simultaneously model, and predict APOs (severe preeclampsia, superimposed preeclampsia, gestational diabetes and early gestational age) as well as several risk factors (BMI, diabetes, hypertension) for these patients based on PSFs. Strongly interrelated stressors were categorized to identify potential therapeutic targets. Furthermore, for a subset of 14 women, we modeled the connection of PSFs to the maternal immune system to APOs by building corresponding ML models based on an extensive single cell immune dataset generated by mass cytometry time of flight (CyTOF). Results: Jointly modeling APOs in a MML setting significantly increased modeling capabilities and yielded a highly predictive integrated model of APOs underscoring their interconnectedness. Most APOs were associated with mental health, life stress, and perceived health risks. Biologically, stressors were associated with specific immune characteristics revolving around CD4/CD8 T cells. Immune characteristics predicted based on stress were in turn found to be associated with APOs. Conclusions: Elucidating connections among stress, multiple APOs simultaneously, and immune characteristics has the potential to facilitate the implementation of ML-based, individualized, integrative models of pregnancy in clinical decision making. The modifiable nature of stressors may enable the development of accessible interventions, with success tracked through immune characteristics.

Single-cell multi-omics reveals dyssynchrony of the innate and adaptive immune system in progressive COVID-19.

Dysregulated immune responses against the SARS-CoV-2 virus are instrumental in severe COVID-19. However, the immune signatures associated with immunopathology are poorly understood. Here we use multi-omics single-cell analysis to probe the dynamic immune responses in hospitalized patients with stable or progressive course of COVID-19, explore V(D)J repertoires, and assess the cellular effects of tocilizumab. Coordinated profiling of gene expression and cell lineage protein markers shows that S100Ahi/HLA-DRlo classical monocytes and activated LAG-3hi T cells are hallmarks of progressive disease and highlights the abnormal MHC-II/LAG-3 interaction on myeloid and T cells, respectively. We also find skewed T cell receptor repertories in expanded effector CD8+ clones, unmutated IGHG+ B cell clones, and mutated B cell clones with stable somatic hypermutation frequency over time. In conclusion, our in-depth immune profiling reveals dyssynchrony of the innate and adaptive immune interaction in progressive COVID-19.

Clinical phenogroups are more effective than left ventricular ejection fraction categories in stratifying heart failure outcomes.

AIMS: Heart failure (HF) guidelines place patients into 3 discrete groups according to left ventricular ejection fraction (LVEF): reduced (<40%), mid-range (40-49%), and preserved LVEF (≥50%). We assessed whether clinical phenogroups offer better prognostication than LVEF. METHODS AND RESULTS: This was a sub-study of the Patient-Centered Care Transitions in HF trial. We analysed baseline characteristics of hospitalized patients in whom LVEF was recorded. We used unsupervised machine learning to identify clinical phenogroups and, thereafter, determined associations between phenogroups and outcomes. Primary outcome was the composite of all-cause death or rehospitalization at 6 and 12 months. Secondary outcome was the composite cardiovascular death or HF rehospitalization at 6 and 12 months. Cluster analysis of 1693 patients revealed six discrete phenogroups, each characterized by a predominant comorbidity: coronary heart disease, valvular heart disease, atrial fibrillation (AF), sleep apnoea, chronic obstructive pulmonary disease (COPD), or few comorbidities. Phenogroups were LVEF independent, with each phenogroup encompassing a wide range of LVEFs. For the primary composite outcome at 6 months, the hazard ratios (HRs) for phenogroups ranged from 1.25 [95% confidence interval (CI) 1.00-1.58 for AF] to 2.04 (95% CI 1.62-2.57 for COPD) (log-rank P < 0.001); and at 12 months, the HRs for phenogroups ranged from 1.15 (95% CI 0.94-1.41 for AF) to 1.87 (95% 1.52-3.20 for COPD) (P < 0.002). LVEF-based classifications did not separate patients into different risk categories for the primary outcomes at 6 months (P = 0.69) and 12 months (P = 0.30). Phenogroups also stratified risk of the secondary composite outcome at 6 and 12 months more effectively than LVEF. CONCLUSION: Among patients hospitalized for HF, clinical phenotypes generated by unsupervised machine learning provided greater prognostic information for a composite of clinical endpoints at 6 and 12 months compared with LVEF-based categories. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02112227.

A method for the rational selection of drug repurposing candidates from multimodal knowledge harmonization.

The SARS-CoV-2 pandemic has challenged researchers at a global scale. The scientific community's massive response has resulted in a flood of experiments, analyses, hypotheses, and publications, especially in the field of drug repurposing. However, many of the proposed therapeutic compounds obtained from SARS-CoV-2 specific assays are not in agreement and thus demonstrate the need for a singular source of COVID-19 related information from which a rational selection of drug repurposing candidates can be made. In this paper, we present the COVID-19 PHARMACOME, a comprehensive drug-target-mechanism graph generated from a compilation of 10 separate disease maps and sources of experimental data focused on SARS-CoV-2/COVID-19 pathophysiology. By applying our systematic approach, we were able to predict the synergistic effect of specific drug pairs, such as Remdesivir and Thioguanosine or Nelfinavir and Raloxifene, on SARS-CoV-2 infection. Experimental validation of our results demonstrate that our graph can be used to not only explore the involved mechanistic pathways, but also to identify novel combinations of drug repurposing candidates.

Post-infectious inflammatory disease in MIS-C features elevated cytotoxicity signatures and autoreactivity that correlates with severity.

Multisystem inflammatory syndrome in children (MIS-C) is a life-threatening post-infectious complication occurring unpredictably weeks after mild or asymptomatic SARS-CoV2 infection in otherwise healthy children. Here, we define immune abnormalities in MIS-C compared to adult COVID-19 and pediatric/adult healthy controls using single-cell RNA sequencing, antigen receptor repertoire analysis, unbiased serum proteomics, and in vitro assays. Despite no evidence of active infection, we uncover elevated S100A-family alarmins in myeloid cells and marked enrichment of serum proteins that map to myeloid cells and pathways including cytokines, complement/coagulation, and fluid shear stress in MIS-C patients. Moreover, NK and CD8 T cell cytotoxicity genes are elevated, and plasmablasts harboring IgG1 and IgG3 are expanded. Consistently, we detect elevated binding of serum IgG from severe MIS-C patients to activated human cardiac microvascular endothelial cells in culture. Thus, we define immunopathology features of MIS-C with implications for predicting and managing this SARS-CoV2-induced critical illness in children.

Neuroinvasion of SARS-CoV-2 in human and mouse brain.

Although COVID-19 is considered to be primarily a respiratory disease, SARS-CoV-2 affects multiple organ systems including the central nervous system (CNS). Yet, there is no consensus on the consequences of CNS infections. Here, we used three independent approaches to probe the capacity of SARS-CoV-2 to infect the brain. First, using human brain organoids, we observed clear evidence of infection with accompanying metabolic changes in infected and neighboring neurons. However, no evidence for type I interferon responses was detected. We demonstrate that neuronal infection can be prevented by blocking ACE2 with antibodies or by administering cerebrospinal fluid from a COVID-19 patient. Second, using mice overexpressing human ACE2, we demonstrate SARS-CoV-2 neuroinvasion in vivo. Finally, in autopsies from patients who died of COVID-19, we detect SARS-CoV-2 in cortical neurons and note pathological features associated with infection with minimal immune cell infiltrates. These results provide evidence for the neuroinvasive capacity of SARS-CoV-2 and an unexpected consequence of direct infection of neurons by SARS-CoV-2.

Clinical impact of concomitant tricuspid valve procedures during left ventricular assist device implantation.

BACKGROUND: Tricuspid regurgitation (TR) is common in patients with end-stage heart failure receiving left ventricular assist devices (LVADs), but the benefit of concomitant tricuspid valve procedures (TVPs) remains uncertain. This study examined the impact of TVP at the time of LVAD implantation on clinical outcomes and quality of life (QOL) metrics. METHODS: We included adult patients in the Interagency Registry for Mechanical Circulatory Support database with various degrees of TR who received continuous-flow LVADs from 2008 to 2017. Patients undergoing concomitant TVP were compared with those without the intervention in a stratified analysis. Descriptive analyses, survival analyses, and Andersen‒Gill hazard models were used as appropriate to examine associations with clinical and patient-centered QOL outcomes. RESULTS: Our analysis included 8,263 (53.1%) mild, 4,252 (33.3%) moderate, and 2,100 (13.5%) severe TR cases. TVP rate increased with severity: 8.6% of mild, 18.0% of moderate, and 43.9% of severe cases. TVP was not associated with survival benefit in cases of mild (adjusted hazard ratio [aHR]: 0.97, 95% CI: 0.79-1.19, p = 0.75), moderate (aHR: 1.03, 95% CI: 0.88-1.20, p = 0.72), or severe (aHR: 1.20, 95% CI: 0.98-1.48, p = 0.08) TR. For patients with combined moderate or severe TR, TVP was associated with increased mortality (log-rank p < 0.01, aHR: 1.13, 95% CI: 1.00-1.27, p = 0.04). After adjusting for TR severity, TVP was associated with increased risk of bleeding, arrhythmia, and stroke (p < 0.01 each) and no improvements in QOL (p > 0.05). CONCLUSIONS: TVP at the time of LVAD implantation was not associated with either improved survival or QOL, and there were associations with increased risk of adverse events among patients with moderate and severe TR.

Patient factors associated with SARS-CoV-2 in an admitted emergency department population.

Background: The SARS-CoV-2 (COVID-19) virus has wide community spread. The aim of this study was to describe patient characteristics and to identify factors associated with COVID-19 among emergency department (ED) patients under investigation for COVID-19 who were admitted to the hospital. Methods: This was a retrospective observational study from 8 EDs within a 9-hospital health system. Patients with COVID-19 testing around the time of hospital admission were included. The primary outcome measure was COVID-19 test result. Patient characteristics were described and a multivariable logistic regression model was used to identify factors associated with a positive COVID-19 test. Results: During the study period from March 1, 2020 to April 8, 2020, 2182 admitted patients had a test resulted for COVID-19. Of these patients, 786 (36%) had a positive test result. For COVID-19-positive patients, 63 (8.1%) died during hospitalization. COVID-19-positive patients had lower pulse oximetry (0.91 [95% confidence interval, CI], [0.88-0.94]), higher temperatures (1.36 [1.26-1.47]), and lower leukocyte counts than negative patients (0.78 [0.75-0.82]). Chronic lung disease (odds ratio [OR] 0.68, [0.52-0.90]) and histories of alcohol (0.64 [0.42-0.99]) or substance abuse (0.39 [0.25-0.62]) were less likely to be associated with a positive COVID-19 result. Conclusion: We observed a high percentage of positive results among an admitted ED cohort under investigation for COVID-19. Patient factors may be useful in early differentiation of patients with COVID-19 from similarly presenting respiratory illnesses although no single factor will serve this purpose.