Search

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/ .

Objectives The aim of this study was to examine patterns of care and clinical outcomes among patients with heart failure with reduced ejection fraction (HFrEF) in the United States and Canada. Background In the GUIDE-IT (Guiding Evidence Based Therapy Using Biomarker Intensified Treatment) trial, the use of N-terminal pro–B-type natriuretic peptide–guided titration of guideline-directed medical therapy (GDMT) was compared with usual care alone for patients with HFrEF in the United States and Canada. It remains unknown whether the country of enrollment had an impact on outcomes or GDMT use. Methods A total of 894 patients at 45 sites across the United States and Canada with HFrEF (ejection fraction ≤40%) were enrolled in the trial. Kaplan-Meier survival estimates stratified by country of enrollment were developed for the trial outcomes, and log-rank testing was compared between the groups. GDMT use and titration were also compared. Results U.S. patients were more likely to be younger, to be Black, to have higher body mass index, and to have histories of defibrillator placement or sleep apnea. Use of β-blockers was significantly higher in Canada at baseline (99.3% vs. 94.0%; p = 0.01) and 6 months (99.0% vs. 94.1%; p = 0.04), and use of mineralocorticoid receptor antagonists was higher in Canada at 6 months (68.3% vs. 55.1%; p = 0.01). Canadian patients were less likely to experience the primary study endpoint (hazard ratio [HR]: 0.65; 95% confidence interval [CI]: 0.45 to 0.93; p = 0.01) due to decreased rates of HF hospitalization (HR: 0.57; 95% CI: 0.38 to 0.86; p = 0.003). The differences in outcomes were driven by increased heart failure hospitalization among U.S. Black patients. Conclusions In GUIDE-IT, patients with HFrEF in Canada were significantly less likely to be hospitalized for heart failure. Differences in GDMT use, along with differences in sociodemographics and care delivery structures, may contribute to these differences, highlighting the importance of increasing diversity in clinical trials. (Guiding Evidence Based Therapy Using Biomarker Intensified Treatment [GUIDE-IT]; NCT01685840 )

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.

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.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.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 p0.01, 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 (p0.01 each) and no improvements in QOL (p0.05).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.

This study sought to use INTERMACS (Interagency Registry for Mechanically Assisted Circulatory Support) results to evaluate sex differences in the use and clinical outcomes of left ventricular assist devices (LVAD).Despite a similar incidence of heart failure in men and women, prior studies have highlighted potential underuse of LVADs in women, and studies of clinical outcomes have yielded conflicting results.Patients were enrolled from the INTERMACS study who underwent implantation of their first continuous-flow LVAD between 2008 and 2017, and survival analyses stratified by sex were conducted.Among the 18,868 patients, 3,984 (21.1%) were women. At 1 year, women were less likely to undergo heart transplantation than men (17.9% vs. 20.0%, respectively; p = 0.003). After multivariable adjustments, women had a higher risk of death (hazard ratio [HR]: 1.15; 95% confidence interval [CI]: 1.07 to 1.23; p 0.001) and were more likely to incur post-implantation adverse events, including rehospitalization, bleeding, stroke, and pump thrombosis or device malfunction. Although women younger than 50 years of age had an increased risk of death compared to men of the same age (HR: 1.34; 95% CI: 1.12 to 1.6), men and women 65 years of age and older had a similar risk of death (HR: 1.09; 95% CI: 0.95 to 1.24).This study found that women had a higher risk of mortality and adverse events after LVAD. Only 1 in 5 LVADs were implanted in women, and women were less likely to receive a heart transplant than men. Further investigation is needed to understand the causes of adverse events and potential underuse of advanced treatment options in women.

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 patients under investigation for COVID-19 who were admitted to the hospital. Methods: This was a retrospective observational study from eight emergency departments within a nine-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, 2,182 admitted patients had a test resulted for COVID-19. Of these patients, 786 (36%) had a positive test result. For COVID positive patients, 63 (8.1%) died during hospitalization. COVID-19 positive patients had lower pulse oximetry (0.91 [95%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 (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 emergency department 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. This article is protected by copyright. All rights reserved.

Psychiatric comorbidities play a key role in patient selection for left ventricular assist devices (LVADs), but their impact on clinical outcomes is unknown.The goal of this study was to examine the clinical impact of psychiatric illness on outcomes in patients receiving LVADs for end-stage heart failure (HF).The study identified adults in the Interagency Registry for Mechanically Assisted Circulatory Support with psychiatric comorbidities (history of alcohol abuse, drug use, narcotic dependence, depression, and other major psychiatric diagnoses) receiving continuous-flow LVADs from 2008 to 2017. Demographic characteristics, survival, adverse events, and quality of life scores were compared for patients with and without each psychiatric comorbidity.Over the study period, the prevalence of psychiatric comorbidities was low: alcohol abuse, n = 1,093 (5.5%); drug use, n = 1,077 (5.4%); narcotic dependence, n = 96 (0.5%); depression, n = 401 (2.0%); and other major psychiatric illnesses, n = 265 (1.4%). Narcotic dependence (adjusted hazard ratio: 1.9; 95% confidence interval: 1.2 to 3.0; p = 0.004) and other major psychiatric illnesses (adjusted hazard ratio: 1.4; 95% confidence interval: 1.0 to 1.9; p = 0.02) were associated with increased risk of mortality, whereas alcohol abuse, drug use, and depression were not. All comorbidities except narcotic dependence were associated with increased risk of rehospitalization and device-related infection (both p 0.05). Kansas City Cardiomyopathy Questionnaire scores were lower from 6 to 24 months' post-implantation among patients with psychiatric comorbidities (p 0.05).Despite a low prevalence of psychiatric comorbidities among LVAD recipients, these conditions were associated with mortality risk, adverse events, and poorer quality of life. Further study is needed to understand how specific psychiatric conditions affect outcomes and how to best manage this vulnerable patient population.

Objectives The purpose of this study was to examine outcomes after left ventricular assist device (LVAD) implantation in older adults (>75 years of age). Background An aging heart failure population together with improvements in mechanical circulatory support (MCS) technology have led to increasing LVAD implantations in older adults. However, data presenting age-specific outcomes are limited. Methods Adult patients in the Interagency Registry for Mechanically Assisted Circulatory Support (INTERMACS) who required durable MCS between 2008 and 2017 were included. Patients were stratified by 4 age groups: 75 years of age. Kaplan-Meier survival estimates were used to assess post-LVAD outcomes, with log-rank testing used to compare groups. Univariate and multivariate cox proportional hazard regression models were used to determine predictors of survival and complications. Results A total of 20,939 individuals received an LVAD during the study period: 7,743 (37.0%) were Conclusions Despite careful selection of older adults for LVAD implantation, age remains a significant predictor of mortality. Higher bleeding and lower clotting risk in elderly patients with LVADs support the use of a less intense antithrombotic regimen in this unique population.

SUMMARYNeurodegeneration is a protracted process involving progressive changes in myriad cell types that ultimately result in neuronal death. Changes in vulnerable neuronal populations are highly influenced by concomitant changes in surrounding cells, complicating experimental approaches to interrogate the simultaneous events that underlie neurodegeneration. 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 the mouse and human spinocerebellar ataxia type 1 (SCA1) cerebellum, establishing continuous dynamic trajectories of each population. Furthermore, we defined the precise transcriptional changes that precede loss of Purkinje cells and identified early oligodendroglial impairments that can profoundly impact cerebellar function. Finally, we applied a deep learning method to accurately predict disease state and identify drivers of disease. Together, this work uncovers new roles for diverse cerebellar cell types in SCA1 and provides a generalizable analysis framework for studying neurodegeneration.

The corticospinal tract (CST) is refractory to repair after CNS trauma, resulting in chronic debilitating functional motor deficits after spinal cord injury. While novel pro-axon growth activators have stimulated plasticity and regeneration of corticospinal neurons (CSNs) after injury, robust functional recovery remains elusive. These repair strategies are sub-optimal in part due to underexplored molecular heterogeneity within the developing and adult CST. In this study, we combine retrograde CST tracing with single-cell RNA sequencing to build a comprehensive atlas of CSN subtypes. By comparing CSNs to non-spinally projecting neurons in layer Vb, we identify pan-CSN markers including Wnt7b. By leveraging retrograde tracing, we are able to compare forelimb and hindlimb projecting CSNs, identifying subtype-specific markers, including Cacng7 and Slc16a2 respectively. These markers are expressed in embryonic and neonatal CSNs and can be used to study early postnatal patterning of the CST. Our results provide molecular insight into the differences between anatomically distinct CSN subtypes and provide a resource for future screening and exploitation of these subtypes to repair the damaged CST after injury and disease.

This study assessed the performance of an automated speech analysis technology in detecting pulmonary fluid overload in patients with acute decompensated heart failure (ADHF).Pulmonary edema is the main cause of heart failure (HF)-related hospitalizations and a key predictor of poor postdischarge prognosis. Frequent monitoring is often recommended, but signs of decompensation are often missed. Voice and sound analysis technologies have been shown to successfully identify clinical conditions that affect vocal cord vibration mechanics.Adult patients with ADHF (n = 40) recorded 5 sentences, in 1 of 3 languages, using HearO, a proprietary speech processing and analysis application, upon admission (wet) to and discharge (dry) from the hospital. Recordings were analyzed for 5 distinct speech measures (SMs), each a distinct time, frequency resolution, and linear versus perceptual (ear) model; mean change from baseline SMs was calculated.In total, 1,484 recordings were analyzed. Discharge recordings were successfully tagged as distinctly different from baseline (wet) in 94% of cases, with distinct differences shown for all 5 SMs in 87.5% of cases. The largest change from baseline was documented for SM2 (218%). Unsupervised, blinded clustering of untagged admission and discharge recordings of 9 patients was further demonstrated for all 5 SMs.Automated speech analysis technology can identify voice alterations reflective of HF status. This platform is expected to provide a valuable contribution to in-person and remote follow-up of patients with HF, by alerting to imminent deterioration, thereby reducing hospitalization rates. (Clinical Evaluation of Cordio App in Adult Patients With CHF; NCT03266029).

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., Graphical abstract, Multisystem inflammatory syndrome in children (MIS-C) is a life-threatening and unpredictable condition of unknown etiology. Ramaswamy et al. use peripheral blood single-cell transcriptomic profiling along with other techniques to define key innate and adaptive signatures that characterize MIS-C.

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., Single-cell analysis of human airway epithelial cells infected with SARS-CoV-2 provides novel insights into viral replication, cell tropism, and host-viral interactions. This study reveals novel polyadenylated viral transcripts, preferential tropism for ciliated cells that later extends to other epithelial cell types, and cell-intrinsic expression of type I and type III IFNs and IL6 induced by infection, resulting in expression of interferon-stimulated genes in both infected and bystander cells.

Despite antiretroviral therapy (ART), HIV-1 persists in proliferating T cell clones that increase over time. To understand whether early ART affects HIV-1 persistence in vivo, we performed single-cell ECCITE-seq and profiled 89,279 CD4+ T cells in paired samples during viremia and after immediate versus delayed ART in six people in the randomized interventional Sabes study. We found that immediate ART partially reverted TNF responses while delayed ART did not. Antigen and TNF responses persisted despite immediate ART and shaped the transcriptional landscape of CD4+ T cells, HIV-1 RNA+ cells, and T cell clones harboring them (cloneHIV-1). Some HIV-1 RNA+ cells reside in the most clonally expanded cytotoxic T cell populations (GZMB and GZMK Th1 cells). CloneHIV-1+ were larger in clone size, persisted despite ART, and exhibited transcriptional signatures of antigen, cytotoxic effector, and cytokine responses. Using machine-learning algorithms, we identified markers for HIV-1 RNA+ cells and cloneHIV-1+ as potential therapeutic targets. Overall, by combining single-cell immune profiling and T cell expansion dynamics tracking, we identified drivers of HIV-1 persistence in vivo.

Neurological symptoms are frequently observed in COVID-19. Here, we examine the neuroinvasive potential of SARS-CoV-2 and demonstrate infection of neurons in three separate approaches: mouse model, human brain organoid, and autopsy of COVID-19 patients., 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., Graphical Abstract

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.

SUMMARYMultisystem 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, andin vitroassays. 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.

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.

A dysregulated immune response against the SARS-CoV-2 virus plays a critical role in severe COVID-19. However, the molecular and cellular mechanisms by which the virus causes lethal immunopathology are poorly understood. Here, we utilize multiomics single-cell analysis to probe dynamic immune responses in patients with stable or progressive manifestations of COVID-19, and assess the effects of tocilizumab, an anti-IL-6 receptor monoclonal antibody. Coordinated profiling of gene expression and cell lineage protein markers reveals a prominent type-1 interferon response across all immune cells, especially in progressive patients. An anti-inflammatory innate immune response and a pre-exhaustion phenotype in activated T cells are hallmarks of progressive disease. Skewed T cell receptor repertoires in CD8+ T cells and uniquely enriched V(D)J sequences are also identified in COVID-19 patients. B cell repertoire and somatic hypermutation analysis are consistent with a primary immune response, with possible contribution from memory B cells. Our in-depth immune profiling reveals dyssynchrony of the innate and adaptive immune interaction in progressive COVID-19, which may contribute to delayed virus clearance and has implications for therapeutic intervention.

Importance Patient outcomes in heart failure clinical trials are generally better than those observed in real-world settings. This may be related to stricter inclusion and exclusion criteria in clinical trials. Objective We study sought to characterize the clinical implications of differences between patients in clinical trials and those in a real-world registry of patients receiving left ventricular assist devices (LVADs). Design, setting, and participants This retrospective cohort study included all patients in INTERMACS (the Interagency Registry for Mechanically Assisted Circulatory Support) who were implanted with an axial flow LVAD from 2010 to 2015 to allow for equivalent comparisons. Main outcomes and measures Differences in patient characteristics and 2-year rates of adverse outcomes with those reported in the ENDURANCE and MOMENTUM 3 clinical trials. Survival analyses were used to assess the relationships between prespecified patient factors and clinical outcomes. Results Of the 10,937 LVAD recipients identified in INTERMACS between 2010–2015, 44% met at least 1 clinical trial exclusion criterion. The 2-year incidence of stroke and death amongst LVAD recipients in INTERMACS and the landmark clinical trials differed significantly (P Conclusions and relevance Most exclusion criteria used in LVAD clinical trials did not afford a substantially greater risk to patients in the real-world setting. In the relatively infrequent cases of end stage renal disease, thrombocytopenia, respiratory failure, and need for ECMO, the risks and benefits of LVAD therapy need careful weighting and further study.

ObjectiveThe goal of this study was to create a predictive model of early hospital respiratory decompensation among patients with COVID-19.DesignObservational, retrospective cohort study.SettingNine-hospital health system within the Northeastern United States.PopulationsAdult patients (≥ 18 years) admitted from the emergency department who tested positive for SARS-CoV-2 (COVID-19) up to 24 hours after initial presentation. Patients meeting criteria for respiratory critical illness within 4 hours of arrival were excluded.Main outcome and performance measuresWe used a composite endpoint of critical illness as defined by oxygen requirement (greater than 10 L/min by low-flow device, high-flow device, non-invasive, or invasive ventilation) or death within the first 24 hours of hospitalization. We developed models predicting our composite endpoint using patient demographic and clinical data available within the first four hours of arrival. Eight hospitals (n= 932) were used for model development and one hospital (n= 240) was held out for external validation. Area under receiver operating characteristic (AU-ROC), precision-recall curves (AU-PRC), and calibration metrics were used to compare predictive models to three illness scoring systems: Elixhauser comorbidity index, qSOFA, and CURB-65.ResultsDuring the study period from March 1, 2020 to April 27,2020, 1,792 patients were admitted with COVID-19. Six-hundred and twenty patients were excluded based on age or critical illness within the first 4 hours, yielding 1,172 patients in the final cohort. Of these patients, 144 (12.3%) met the composite endpoint within the first 24 hours. We first developed a bedside quick COVID-19 severity index (qCSI), a twelve-point scale using nasal cannula flow rate, respiratory rate, and minimum documented pulse oximetry. We then created a machine-learning gradient boosting model, the COVID-19 severity index (CSI), using twelve additional variables including inflammatory markers and liver chemistries. Both the qCSI (AU-ROC mean [95% CI]: 0.90 [0.85-0.96]) and CSI (AU-ROC: 0.91 [0.86-0.97]) outperformed the comparator models (qSOFA: 0.76 [0.69-0.85]; Elixhauser: 0.70 [0.62-0.80]; CURB-65: AU-ROC 0.66 [0.58-0.77]) on cross-validation and performed well on external validation (qCSI: 0.82, CSI: 0.76, CURB-65: 0.50, qSOFA: 0.59, Elixhauser: 0.61). We find that a qCSI score of 0-3 is associated with a less than 5% risk of critical respiratory illness, while a score of 9-12 is associated with a 57% risk of progression to critical illness.ConclusionsA significant proportion of admitted COVID-19 patients decompensate within 24 hours of hospital presentation and these events are accurately predicted using bedside respiratory exam findings within a simple scoring system.

SARS-CoV-2, the causative agent of COVID-19, has tragically burdened individuals and institutions around the world. There are currently no approved drugs or vaccines for the treatment or prevention of COVID-19. Enhanced understanding of SARS-CoV-2 infection and pathogenesis is critical for the development of therapeutics. To reveal insight into viral replication, cell tropism, and host-viral interactions of SARS-CoV-2 we performed single-cell RNA sequencing of experimentally infected human bronchial epithelial cells (HBECs) in air-liquid interface cultures over a time-course. This revealed novel polyadenylated viral transcripts and highlighted ciliated cells as a major target of infection, which we confirmed by electron microscopy. Over the course of infection, 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 IFNs and IL6 but not IL1. This results in expression of interferon-stimulated genes in both infected and bystander cells. We observe similar gene expression changes from a COVID-19 patient ex vivo. In addition, we developed a new computational method termed CONditional DENSity Embedding (CONDENSE) to characterize and compare temporal gene dynamics in response to infection, which revealed genes relating to endothelin, angiogenesis, interferon, and inflammation-causing signaling pathways. In this study, we conducted an in-depth analysis of SARS-CoV-2 infection in HBECs and a COVID-19 patient and revealed genes, cell types, and cell state changes associated with infection.

Single-cell RNA sequencing (scRNA-seq) has revolutionized biological discovery, providing an unbiased picture of cellular heterogeneity in tissues. While scRNA-seq has been used extensively to provide insight into both healthy systems and diseases, it has not been used for disease prediction or diagnostics. Graph Attention Networks (GAT) have proven to be versatile for a wide range of tasks by learning from both original features and graph structures. Here we present a graph attention model for predicting disease state from single-cell data on a large dataset of Multiple Sclerosis (MS) patients. MS is a disease of the central nervous system that can be difficult to diagnose. We train our model on single-cell data obtained from blood and cerebrospinal fluid (CSF) for a cohort of seven MS patients and six healthy adults (HA), resulting in 66,667 individual cells. We achieve 92 % accuracy in predicting MS, outperforming other state-of-the-art methods such as a graph convolutional network and a random forest classifier. Further, we use the learned graph attention model to get insight into the features (cell types and genes) that are important for this prediction. The graph attention model also allow us to infer a new feature space for the cells that emphasizes the differences between the two conditions. Finally we use the attention weights to learn a new low-dimensional embedding that can be visualized. To the best of our knowledge, this is the first effort to use graph attention, and deep learning in general, to predict disease state from single-cell data. We envision applying this method to single-cell data for other diseases., Incorporated suggestions from anonymous reviewers, Accepted at ACM CHIL 2020, comments welcome

BackgroundPatients with comorbid conditions have a higher risk of mortality with SARS-CoV-2 (COVID-19) infection, but the impact on heart failure patients living near a disease hotspot is unknown. Therefore, we sought to characterize the prevalence and outcomes of COVID-19 in a live registry of heart failure patients across an integrated health care system in Connecticut.MethodsIn this retrospective analysis, the Yale Heart Failure Registry (NCT04237701) that includes 26,703 patients with heart failure across a 6-hospital integrated health care system in Connecticut was queried on April 16th, 2020 for all patients tested for COVID-19. Sociodemographic and geospatial data as well as, clinical management, respiratory failure, and patient mortality were obtained via the real-time registry. Data on COVID-19 specific care was extracted by retrospective chart review.ResultsCOVID-19 testing was performed on 900 symptomatic patients, comprising 3.4% of the Yale Heart Failure Registry (N=26,703). Overall, 206 (23%) were COVID-19+. As compared to COVID-19-, these patients were more likely to be older, black, have hypertension, coronary artery disease, and were less likely to be on renin angiotensin blockers (P- patients tended to be more diffusely spread across the state whereas COVID-19+ were largely clustered around urban centers. 20% of COVID-19+ patients died, and age was associated with increased risk of death [OR 1.92 95% CI (1.33–2.78); P+ patients who were ≥85 years of age rates of hospitalization were 87%, rates of death 36%, and continuing hospitalization 62% at time of manuscript preparation.ConclusionsIn this real-world snapshot of COVID-19 infection among a large cohort of heart failure patients, we found that a small proportion had undergone testing. Patients found to be COVID-19+ tended to be black with multiple comorbidities and clustered around lower socioeconomic status communities. Elderly COVID-19+ patients were very likely to be admitted to the hospital and experience high rates of mortality.

Background: The impact of respiratory failure on patients undergoing left ventricular assist device (LVAD) implantation is not well understood, especially since these patients were excluded from landmark clinical trials. We sought to evaluate the associations between immediate preimplant and postimplant respiratory failure on outcomes in advanced heart failure patients undergoing LVAD implantation. Methods and Results: We included all patients in the Interagency Registry for Mechanically Assisted Circulatory Support who were implanted with continuous-flow LVADs from 2008 to 2016. Of the 16 362 patients who underwent continuous-flow LVAD placement, 906 (5.5%) required preimplant intubation within 48 hours before implantation, and 1001 (6.1%) patients developed respiratory failure within 1 week after implantation. A higher proportion of patients requiring preimplant intubation were Interagency Registry for Mechanically Assisted Circulatory Support profile 1, required mechanical circulatory support, and presented with cardiac arrest or myocardial infarction ( P P P =0.001). After multivariable analysis, both preimplant intubation (hazard ratio, 1.20 [95% CI, 1.03–1.41]; P =0.021) and respiratory failure within 1 week (hazard ratio, 2.54 [95% CI, 2.26–2.85]; P Conclusions: Respiratory failure both before and after LVAD implantation identifies an advanced heart failure population with significantly worse 1-year mortality. This data might be helpful in counseling patients and their families about expectations about life with an LVAD.

Purpose The clinical impact of mitral valve procedures (MVP) at time of left ventricular assist device (LVAD) is not well understood. We examined the association of MVP, according to severity of MR, with long term survival and quality of life metrics. Methods Continuous-flow LVAD recipients with MR from the 2008-2017 INTERMACS registry were included in the analysis. Patients with concomitant MVP were compared to those without intervention, stratified by severity of MR. Descriptive analyses, hazard modeling, and Kaplan-Meier analyses were used to examine associations between intervention and clinical outcomes. Results 6360 (40.0%) mild, 5592 (35.2%) moderate, and 3936 (24.8%) severe MR cases were included. MVP was not associated with improved survival for mild (median: 80.7 months for MVP vs. 55.0 months for non-MVP, P=0.49, HR 0.65 [95%:0.34-1.23]), moderate (58.8 vs. 49.3 months, P=0.31, HR 0.90 [0.57-1.42]), or severe MR (76.8 vs 49.7 months, P=0.15, HR 1.08 [0.73-1.59]). MVP was not associated with differential risk for device failure, bleeding, arrhythmia, thrombotic, right heart failure, or renal failure. There were no differences in Kansas City Cardiomyopathy Questionnaire with or without MVP for mild, moderate, or severe MR at 1- or 2-years after implantation. Conclusion Concomitant MVP with LVAD implantation was not associated with mortality, adverse events, or objective quality of life metrics. The role of concomitant MVP with LVAD implantation must be carefully considered.

The purpose of this study was to examine outcomes after left ventricular assist device (LVAD) implantation in older adults (75 years of age).An aging heart failure population together with improvements in mechanical circulatory support (MCS) technology have led to increasing LVAD implantations in older adults. However, data presenting age-specific outcomes are limited.Adult patients in the Interagency Registry for Mechanically Assisted Circulatory Support (INTERMACS) who required durable MCS between 2008 and 2017 were included. Patients were stratified by 4 age groups: 55 years of age, 55 to 64 years of age, and75 years of age. Kaplan-Meier survival estimates were used to assess post-LVAD outcomes, with log-rank testing used to compare groups. Univariate and multivariate cox proportional hazard regression models were used to determine predictors of survival and complications.A total of 20,939 individuals received an LVAD during the study period: 7,743 (37.0%) were 55 years of age, 6,755 (32.3%) were 55 to 64 years of age, 5,418 (25.9%) were 65 to 74 years of age, and 1,023 (4.9%) were ≥75 years of age or older. After multivariate adjustment, adults ≥75 years of age had increased mortality post-LVAD implantation. Elderly patients with LVADs had a higher incidence of gastrointestinal bleeding but lower rates of device thrombosis. Compared to 84.5% of patients 55 years of age who were discharged home, only 46.8% of adults ≥75 years of age were discharged home following implantation (p 0.001). Use of a RVAD, serum albumin level, and 6-min walk test distances were identified as predictors of outcomes in the oldest cohort.Despite careful selection of older adults for LVAD implantation, age remains a significant predictor of mortality. Higher bleeding and lower clotting risk in elderly patients with LVADs support the use of a less intense antithrombotic regimen in this unique population.

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.

Purpose Tricuspid regurgitation (TR) is common in end stage heart failure patients receiving left ventricular assist devices (LVADs), but the value of intervention is uncertain. This study examined the impact of tricuspid valve procedures (TVP) at time of LVAD implantation on clinical outcomes and quality of life metrics. Methods We included patients in the INTERMACS database with TR who received continuous-flow LVADs from the 2008 to 2017. TR was stratified by severity. Patients receiving concomitant TVP were compared to those without intervention in a stratified analysis. Survival analyses and Andersen-Gill hazard models were used to examine associations with clinical outcomes. Results Our analysis included 8263 (53.1%) mild, 4252 (33.3%) moderate, and 2100 (13.5%) severe TR cases. TVP rate increased with severity: 13.0% of mild cases underwent TVP versus 55.2% of severe cases. TVP was not associated with survival difference for mild (median 57.8 months for TVP vs. 55.6 months for non-TVP, P=0.69, HR 1.10 [95%: 0.87-1.50]), moderate (47.5 vs. 51.1 months, P=0.23, HR 1.20 [0.99-1.50]), or severe TR (43.3 vs 46.3 months, P=0.09, HR 1.30 [0.99-1.60]). TVP was associated with increased risk of bleeding, arrhythmia, stroke, renal failure, and respiratory failure (P Conclusion TVP at time of LVAD implantation was not associated with differences in survival or KCCQ scores. However, there were associations with increased risk of adverse events. Further studies are needed to understand appropriateness criteria for TVP with LVAD implantation.

Outcomes in heart failure clinical trials are generally better than those in the real-world. This may be due to stricter inclusion criteria. We compared patient characteristics and clinical outcomes in clinical trial patients and those in a real-world registry who received an axial flow left

Social support is considered a key factor that determines candidacy for LVADs. Few studies have examined its impact on clinical outcomes in a real-world population of patients receiving LVADs. We included INTERMACS patients who received an LVAD from 2008-2017 and excluded patients with missing data