Prior metabolic surgery reduced COVID-19 severity: Systematic analysis from year one of the COVID-19 pandemic

Background Obesity is a risk factor for COVID-19 severity. Recent studies suggest that prior metabolic surgery (MS) modifies the risk of COVID-19 severity. Methods COVID-19 outcomes were compared between patients with MS (n = 287) and a matched cohort of unoperated patients (n = 861). Multiple logistic regression was used to identify predictors of hospitalization. A systematic literature review and pooled analysis was conducted to provide overall evidence of the influence of prior metabolic surgery on COVID-19 outcomes. Results COVID-19 patients with MS had less hospitalization (9.8% versus 14.3%, p = 0.049). Age 70+, higher BMI, and low weight regain after MS were associated with more hospitalization after COVID-19. A systematic review of 7 studies confirmed that MS reduced the risk of post-COVID-19 hospitalization (OR = 0.71, 95%CI = [0.61–0.83], p < 0.0001) and death (OR = 0.44, 95%CI = [0.30–0.65], p < 0.0001). Conclusion MS favorably modifies the risks of severe COVID-19 infection. Older age and higher BMI are major risk factors for severity of COVID-19 infection.

Student Anxiety and Its Impact: A Recent American History

This article traces the rise of anxiety among American high school and college students since the late 1950s, with particular focus on the decades before 2000. Evidence for rates of change comes from anxiety tests administered during the period, as well as a variety of psychological studies. The article also takes up the issue of causation, highlighting the extension of counseling services and psychological vocabulary that affected evaluations of nervousness;the impact of negative developments like crime rates and growing family instability;and the results both of changes in educational patterns—such as more frequent examinations—and significant shifts in student goals and expectations. Finally, the article touches on efforts to mitigate anxiety, such as expanding student services, and also their limited impact.

Prevalence and Causes of Diagnostic Errors in Hospitalized Patients Under Investigation for COVID-19.

BACKGROUND: The COVID-19 pandemic required clinicians to care for a disease with evolving characteristics while also adhering to care changes (e.g., physical distancing practices) that might lead to diagnostic errors (DEs). OBJECTIVE: To determine the frequency of DEs and their causes among patients hospitalized under investigation (PUI) for COVID-19. DESIGN: Retrospective cohort. SETTING: Eight medical centers affiliated with the Hospital Medicine ReEngineering Network (HOMERuN). TARGET POPULATION: Adults hospitalized under investigation (PUI) for COVID-19 infection between February and July 2020. MEASUREMENTS: We randomly selected up to 8 cases per site per month for review, with each case reviewed by two clinicians to determine whether a DE (defined as a missed or delayed diagnosis) occurred, and whether any diagnostic process faults took place. We used bivariable statistics to compare patients with and without DE and multivariable models to determine which process faults or patient factors were associated with DEs. RESULTS: Two hundred and fifty-seven patient charts underwent review, of which 36 (14%) had a diagnostic error. Patients with and without DE were statistically similar in terms of socioeconomic factors, comorbidities, risk factors for COVID-19, and COVID-19 test turnaround time and eventual positivity. Most common diagnostic process faults contributing to DE were problems with clinical assessment, testing choices, history taking, and physical examination (all p < 0.01). Diagnostic process faults associated with policies and procedures related to COVID-19 were not associated with DE risk. Fourteen patients (35.9% of patients with errors and 5.4% overall) suffered harm or death due to diagnostic error. LIMITATIONS: Results are limited by available documentation and do not capture communication between providers and patients. CONCLUSION: Among PUI patients, DEs were common and not associated with pandemic-related care changes, suggesting the importance of more general diagnostic process gaps in error propagation.

Acute testicular infarction in the setting of SARS-CoV-2 infection and diabetic vasculopathy

Acute testicular pain with no arterial flow on Doppler ultrasonography is highly consistent with testicular torsion. In adults, there are rare etiologies of testicular infarction other than torsion, including infection, vasculitis, and trauma. We describe a 41-year-old man with type 2 diabetes complicated by severe vasculopathy and positive SARS-CoV-2 status presenting with acute right testicular pain. Surgical exploration and pathology were concerning for arteriosclerosis and vasculitis. These observations suggest that medically complex patients presenting with acute testicular pain in the setting of COVID-19 infection could be at risk for ischemia;causes of testicular pain beyond torsion should be considered.

Acute testicular infarction in the setting of SARS-CoV-2 infection and diabetic vasculopathy.

Acute testicular pain with no arterial flow on Doppler ultrasonography is highly consistent with testicular torsion. In adults, there are rare etiologies of testicular infarction other than torsion, including infection, vasculitis, and trauma. We describe a 41-year-old man with type 2 diabetes complicated by severe vasculopathy and positive SARS-CoV-2 status presenting with acute right testicular pain. Surgical exploration and pathology were concerning for arteriosclerosis and vasculitis. These observations suggest that medically complex patients presenting with acute testicular pain in the setting of COVID-19 infection could be at risk for ischemia; causes of testicular pain beyond torsion should be considered.

SARS-CoV-2 NSP5 Antagonizes the MHC II Antigen Presentation Pathway by Hijacking Histone Deacetylase 2 (preprint)

The clearance of SARS-CoV-2 requires a multi-faceted immune response that is initiated by innate immune cells, with infection ultimately resolved by adaptive immune mechanisms. Induction of adaptive immunity to SARS-CoV-2 is dependent on the presentation of viral antigens on MHC II by professional antigen presenting cells such as dendritic cells and macrophages, to induce robust activation of CD4+ T cells. SARS-CoV-2 interferes with antigen presentation by downregulating MHC II on the antigen presenting cells of COVID-19 patients, but the molecular mechanism mediating this process is unelucidated. In this study, analysis of protein and gene expression in human antigen presenting cells reveals that the expression of MHC II is inhibited by the SARS-CoV-2 main protease, NSP5. Suppression of MHC II expression occurs via downregulation of the transcription factor CIITA, which is required for MHC II expression. This downregulation of CIITA is independent of NSP5's proteolytic activity, and rather, NSP5 delivers HDAC2 to the CIITA promoter via interactions with IRF3, Here, HDAC2 deacetylates and inactivates the CIITA promoter. This loss of CIITA expression prevents further expression of MHC II, with this suppression alleviated by ectopic expression of CIITA or knockdown of HDAC2. These results identify a novel mechanism by which SARS-CoV-2 can limit antigen presentation on MHC II, thereby delaying or weakening the subsequent adaptive immune response.

A method for comparing multiple imputation techniques: A case study on the U.S. national COVID cohort collaborative.

Healthcare datasets obtained from Electronic Health Records have proven to be extremely useful for assessing associations between patients' predictors and outcomes of interest. However, these datasets often suffer from missing values in a high proportion of cases, whose removal may introduce severe bias. Several multiple imputation algorithms have been proposed to attempt to recover the missing information under an assumed missingness mechanism. Each algorithm presents strengths and weaknesses, and there is currently no consensus on which multiple imputation algorithm works best in a given scenario. Furthermore, the selection of each algorithm's parameters and data-related modeling choices are also both crucial and challenging. In this paper we propose a novel framework to numerically evaluate strategies for handling missing data in the context of statistical analysis, with a particular focus on multiple imputation techniques. We demonstrate the feasibility of our approach on a large cohort of type-2 diabetes patients provided by the National COVID Cohort Collaborative (N3C) Enclave, where we explored the influence of various patient characteristics on outcomes related to COVID-19. Our analysis included classic multiple imputation techniques as well as simple complete-case Inverse Probability Weighted models. Extensive experiments show that our approach can effectively highlight the most promising and performant missing-data handling strategy for our case study. Moreover, our methodology allowed a better understanding of the behavior of the different models and of how it changed as we modified their parameters. Our method is general and can be applied to different research fields and on datasets containing heterogeneous types.

KG-Hub -- Building and Exchanging Biological Knowledge Graphs (preprint)

Knowledge graphs (KGs) are a powerful approach for integrating heterogeneous data and making inferences in biology and many other domains, but a coherent solution for constructing, exchanging, and facilitating the downstream use of knowledge graphs is lacking. Here we present KG-Hub, a platform that enables standardized construction, exchange, and reuse of knowledge graphs. Features include a simple, modular extract-transform-load (ETL) pattern for producing graphs compliant with Biolink Model (a high-level data model for standardizing biological data), easy integration of any OBO (Open Biological and Biomedical Ontologies) ontology, cached downloads of upstream data sources, versioned and automatically updated builds with stable URLs, web-browsable storage of KG artifacts on cloud infrastructure, and easy reuse of transformed subgraphs across projects. Current KG-Hub projects span use cases including COVID-19 research, drug repurposing, microbial-environmental interactions, and rare disease research. KG-Hub is equipped with tooling to easily analyze and manipulate knowledge graphs. KG-Hub is also tightly integrated with graph machine learning (ML) tools which allow automated graph machine learning, including node embeddings and training of models for link prediction and node classification.

Generalisable long COVID subtypes: findings from the NIH N3C and RECOVER programmes.

BACKGROUND: Stratification of patients with post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) would allow precision clinical management strategies. However, long COVID is incompletely understood and characterised by a wide range of manifestations that are difficult to analyse computationally. Additionally, the generalisability of machine learning classification of COVID-19 clinical outcomes has rarely been tested. METHODS: We present a method for computationally modelling PASC phenotype data based on electronic healthcare records (EHRs) and for assessing pairwise phenotypic similarity between patients using semantic similarity. Our approach defines a nonlinear similarity function that maps from a feature space of phenotypic abnormalities to a matrix of pairwise patient similarity that can be clustered using unsupervised machine learning. FINDINGS: We found six clusters of PASC patients, each with distinct profiles of phenotypic abnormalities, including clusters with distinct pulmonary, neuropsychiatric, and cardiovascular abnormalities, and a cluster associated with broad, severe manifestations and increased mortality. There was significant association of cluster membership with a range of pre-existing conditions and measures of severity during acute COVID-19. We assigned new patients from other healthcare centres to clusters by maximum semantic similarity to the original patients, and showed that the clusters were generalisable across different hospital systems. The increased mortality rate originally identified in one cluster was consistently observed in patients assigned to that cluster in other hospital systems. INTERPRETATION: Semantic phenotypic clustering provides a foundation for assigning patients to stratified subgroups for natural history or therapy studies on PASC. FUNDING: NIH (TR002306/OT2HL161847-01/OD011883/HG010860), U.S.D.O.E. (DE-AC02-05CH11231), Donald A. Roux Family Fund at Jackson Laboratory, Marsico Family at CU Anschutz.

Metformin is associated with reduced COVID-19 severity in patients with prediabetes.

AIMS: Studies suggest that metformin is associated with reduced COVID-19 severity in individuals with diabetes compared to other antihyperglycemics. We assessed if metformin is associated with reduced incidence of severe COVID-19 for patients with prediabetes or polycystic ovary syndrome (PCOS), common diseases that increase the risk of severe COVID-19. METHODS: This observational, retrospective study utilized EHR data from 52 hospitals for COVID-19 patients with PCOS or prediabetes treated with metformin or levothyroxine/ondansetron (controls). After balancing via inverse probability score weighting, associations with COVID-19 severity were assessed by logistic regression. RESULTS: In the prediabetes cohort, when compared to levothyroxine, metformin was associated with a significantly lower incidence of COVID-19 with "mild-ED" or worse (OR [95% CI]: 0.636, [0.455-0.888]) and "moderate" or worse severity (0.493 [0.339-0.718]). Compared to ondansetron, metformin was associated with lower incidence of "mild-ED" or worse severity (0.039 [0.026-0.057]), "moderate" or worse (0.045 [0.03-0.069]), "severe" or worse (0.183 [0.077-0.431]), and "mortality/hospice" (0.223 [0.071-0.694]). For PCOS, metformin showed no significant differences in severity compared to levothyroxine, but was associated with a significantly lower incidence of "mild-ED" or worse (0.101 [0.061-0.166]), and "moderate" or worse (0.094 [0.049-0.18]) COVID-19 outcome compared to ondansetron. CONCLUSIONS: Metformin use is associated with less severe COVID-19 in patients with prediabetes or PCOS.

The impact of the COVID-19 pandemic on 30-day survival in pediatric cancer patients in low, middle, and high-income countries: an international, observational cohort study

Objectives: To conduct the first international cohort study to ascertain the short-term outcome for pediatric oncology patients who underwent treatment across 16 high-income countries (HICs) and 23 low-and-middle-income countries (LMICs) during the COVID-19 pandemic. The hypotheses being tested was that the COVID-19 pandemic had affected paediatric cancer care, and that the outcomes of children were worse in LMICs. Design: A multicenter, international, collaborative cohort study. Setting: 91 hospitals and cancer centers in 39 countries providing cancer treatment to pediatric patients between March and December 2020. Participants: Patients were included if they were under the age of 18 years, and newly diagnosed with or undergoing active cancer treatment for Acute lymphoblastic leukemia, Non-Hodgkin lymphoma, Hodgkin lymphoma, Wilms Tumor, Sarcoma, Retinoblastoma, Gliomas, Medulloblastomas or Neuroblastomas, in keeping with the World Health Organization Global Initiative for Childhood Cancer. Main outcome measure: All-cause mortality at 30 days and 90 days Results: 1660 patients were recruited. Over 30 days, 45 LMIC patients (4.3%;95% CI: 3.1 to 5.5) and 2 HIC patients (0.4%;95% CI: -0.1 to 0.9) died. 219 children had their treatments delayed, interrupted, or modified. LMIC patients had 11.7 (95% CI: 10.3 to 13.1) and 7.4 (95% CI: 6.5 to 8.3) times the risk of death at 30 days and 90 days respectively (p < 0.001). After adjusting for confounders, pediatric cancer patients in LMICs had 35.7 times the odds of death at 30 days (p < 0.001). Conclusions: The COVID-19 pandemic has affected pediatric oncology service provision. It has disproportionately affected patients in LMICs, highlighting and compounding existing disparities in healthcare systems globally that need addressing urgently. However, most pediatric cancer patients continued to receive their normal standard of care. This speaks to the adaptability and resilience of health-care systems and healthcare workers globally.

Data-driven commentary on SARS-CoV-2 infection, vaccination, and fertility.

A recent study by Wesselink et al. (Am J Epidemiol. 2022 Jan 20;kwac011. doi: 10.1093/aje/kwac011. Online ahead of print) adds to the growing body of research finding that vaccination for coronavirus disease 2019 (COVID-19) is safe for individuals either seeking pregnancy or who are pregnant. The study's authors found no effect of COVID-19 vaccination on fecundity in a population of individuals with no known infertility who were attempting conception. The finding reinforces the messaging of the American Society for Reproductive Medicine COVID-19 Task Force, the aim of which is to provide data-driven recommendations to individuals contemplating pregnancy in the face of the COVID-19 pandemic. As safe and effective COVID-19 vaccines became available, and with an increasing number of studies showing a heightened risk of severe disease during pregnancy, an important role of the Task Force is to encourage vaccination during the preconceptual window and in early pregnancy. The Task Force supports ongoing research to address gaps in knowledge about safe and effective therapies and preventive measures for individuals contemplating pregnancy and during pregnancy. Such research will help optimize care for reproductive-age individuals in the face of current and future health crises.

Manifestations Associated with Post Acute Sequelae of SARS-CoV2 Infection (PASC) Predict Diagnosis of New-Onset Psychiatric Disease: Findings from the NIH N3C and RECOVER Studies (preprint)

Acute COVID-19 infection can be followed by persistent or newly diagnosed manifestations in many different organ systems, referred to as Post Acute Sequelae of SARS-CoV2 Infection (PASC). Numerous studies have shown an increased risk of being diagnosed with new-onset psychiatric disease in the first 21-120 days following a diagnosis of acute COVID-19. However, it was unclear whether non-psychiatric PASC-associated manifestations (PASC-AMs) are associated with an increased risk of receiving a diagnosis of new-onset psychiatric disease following COVID-19. Here, we perform a retrospective electronic health record (EHR) cohort study to evaluate whether non-psychiatric PASC-AMs can predict whether patients will receive a diagnosis of new-onset psychiatric disease. Data were obtained from the National COVID Cohort Collaborative (N3C), which has EHR data from 65 clinical organizations which are harmonized using the Observational Medical Outcomes Partnership (OMOP) data model. Non-psychiatric PASC-AMs were recorded 21-120 days following SARS-CoV-2 diagnosis and before diagnosis of new-onset psychiatric disease. OMOP codes were mapped to 178 Human Phenotype Ontology (HPO) terms that represent PASC-AMs. Logistic regression was applied to predict newly diagnosed psychiatric disease occurrence based on age, sex, race, pre-existing comorbidities, and PASC-AMs in eleven categories. The cohort of 1,135,973 individuals with acute COVID-19 had a mean age of 40.5 years and included 56.0% females. We found a significant association for seven of the HPO categories with newly diagnosed psychiatric disease, with odds ratios highest for neurological (2.30, 2.24-2.36) and cardiovascular (1.77, 1.69-1.85) PASC-AMs. Secondary analysis revealed that the proportions of 95 of 154 individual phenotypic features differed significantly among patients diagnosed with different psychiatric diseases (anxiety, mood disorders, dementia, and psychosis). Neurological, pulmonary, gastrointestinal, endocrine, cardiovascular, constitutional, and ENT PASC-AMs are each associated with an increased risk of newly diagnosed psychiatric disease. This suggests that the total burden of PASC-AMs influences the risk of receiving a diagnosis of a new-onset psychiatric disease. This finding may be used to inform psychiatric screening following acute COVID-19 by identifying high-risk patients.

A method for comparing multiple imputation techniques: a case study on the U.S. National COVID Cohort Collaborative (preprint)

Healthcare datasets obtained from Electronic Health Records have proven to be extremely useful to assess associations between patients' predictors and outcomes of interest. However, these datasets often suffer from missing values in a high proportion of cases and the simple removal of these cases may introduce severe bias. For these reasons, several multiple imputation algorithms have been proposed to attempt to recover the missing information. Each algorithm presents strengths and weaknesses, and there is currently no consensus on which multiple imputation algorithms works best in a given scenario. Furthermore, the selection of each algorithm parameters and data-related modelling choices are also both crucial and challenging. In this paper, we propose a novel framework to numerically evaluate strategies for handling missing data in the context of statistical analysis, with a particular focus on multiple imputation techniques. We demonstrate the feasibility of our approach on a large cohort of type-2 diabetes patients provided by the National COVID Cohort Collaborative (N3C) Enclave, where we explored the influence of various patient characteristics on outcomes related to COVID-19. Our analysis included classic multiple imputation techniques as well as simple complete-case Inverse Probability Weighted models. The experiments presented here show that our approach could effectively highlight the most valid and performant missing-data handling strategy for our case study. Moreover, our methodology allowed us to gain an understanding of the behavior of the different models and of how it changed as we modified their parameters. Our method is general and can be applied to different research fields and on datasets containing heterogeneous types.

NSAID use and clinical outcomes in COVID-19 patients: a 38-center retrospective cohort study.

BACKGROUND: Non-steroidal anti-inflammatory drugs (NSAIDs) are commonly used to reduce pain, fever, and inflammation but have been associated with complications in community-acquired pneumonia. Observations shortly after the start of the COVID-19 pandemic in 2020 suggested that ibuprofen was associated with an increased risk of adverse events in COVID-19 patients, but subsequent observational studies failed to demonstrate increased risk and in one case showed reduced risk associated with NSAID use. METHODS: A 38-center retrospective cohort study was performed that leveraged the harmonized, high-granularity electronic health record data of the National COVID Cohort Collaborative. A propensity-matched cohort of 19,746 COVID-19 inpatients was constructed by matching cases (treated with NSAIDs at the time of admission) and 19,746 controls (not treated) from 857,061 patients with COVID-19 available for analysis. The primary outcome of interest was COVID-19 severity in hospitalized patients, which was classified as: moderate, severe, or mortality/hospice. Secondary outcomes were acute kidney injury (AKI), extracorporeal membrane oxygenation (ECMO), invasive ventilation, and all-cause mortality at any time following COVID-19 diagnosis. RESULTS: Logistic regression showed that NSAID use was not associated with increased COVID-19 severity (OR: 0.57 95% CI: 0.53-0.61). Analysis of secondary outcomes using logistic regression showed that NSAID use was not associated with increased risk of all-cause mortality (OR 0.51 95% CI: 0.47-0.56), invasive ventilation (OR: 0.59 95% CI: 0.55-0.64), AKI (OR: 0.67 95% CI: 0.63-0.72), or ECMO (OR: 0.51 95% CI: 0.36-0.7). In contrast, the odds ratios indicate reduced risk of these outcomes, but our quantitative bias analysis showed E-values of between 1.9 and 3.3 for these associations, indicating that comparatively weak or moderate confounder associations could explain away the observed associations. CONCLUSIONS: Study interpretation is limited by the observational design. Recording of NSAID use may have been incomplete. Our study demonstrates that NSAID use is not associated with increased COVID-19 severity, all-cause mortality, invasive ventilation, AKI, or ECMO in COVID-19 inpatients. A conservative interpretation in light of the quantitative bias analysis is that there is no evidence that NSAID use is associated with risk of increased severity or the other measured outcomes. Our results confirm and extend analogous findings in previous observational studies using a large cohort of patients drawn from 38 centers in a nationally representative multicenter database.

Generalizable Long COVID Subtypes: Findings from the NIH N3C and RECOVER Programs (preprint)

Accurate stratification of patients with Post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) would allow precision clinical management strategies and could enable more focussed investigation of the molecular pathogenetic mechanisms of this disease. However, the natural history of long COVID is incompletely understood and characterized by an extremely wide range of manifestations that are difficult to analyze computationally. In addition, the generalizability of machine learning classification of COVID-19 clinical outcomes has rarely been tested. We present a method for computationally modeling long COVID phenotype data based on electronic healthcare records (EHRs) and for assessing pairwise phenotypic similarity between patients using semantic similarity. Using unsupervised machine learning (k-means clustering), we found six distinct clusters of long COVID patients, each with distinct profiles of phenotypic abnormalities with enrichments in pulmonary, cardiovascular, neuropsychiatric, and constitutional symptoms such as fatigue and fever. There was a highly significant association of cluster membership with a range of pre-existing conditions and with measures of severity during acute COVID-19. We show that the clusters we identified in one hospital system were generalizable across different hospital systems. Semantic phenotypic clustering can provide a foundation for assigning patients to stratified subgroups for natural history or therapy studies on long COVID.

Anti-COVID-19 potential of Azadirachta indica (Neem) leaf extract.

COVID-19 is caused by infection with the "severe acute respiratory syndrome coronavirus-2″ (i.e., SARS-CoV-2). This is an enveloped virus having a positive sense, single-stranded RNA genome; like the two earlier viruses SARS-CoV and the Middle East respiratory syndrome (MERS) virus. COVID-19 is unique in that, in the severe case, it has the propensity to affect multiple organs, leading to multiple organ distress syndrome (MODS), and causing high morbidity and mortality in the extreme case. In addition, comorbidities like age, cardiovascular disease, diabetes and its complications, obesity, are risk factors for severe COVID-19. It turns out that a most plausible, simple, single explanation for this propensity for MODS is the pivotal involvement of the vascular endothelium (VE). This is a consequence of the fact that the VE seamlessly connects all the entire vascular bed in the body, thus linking all the target organs (heart, lungs, kidney, liver, brain) and systems. Infection with SARS-CoV-2 leads to hyper-inflammation yielding uncontrolled production of a mixture of cytokines, chemokines, reactive oxygen species, nitric oxide, oxidative stress, acute phase proteins (e.g., C-reactive protein), and other pro-inflammatory substances. In the extreme case, a cytokine storm is created. Displacement of the virus bound to the VE, and/or inhibition of binding of the virus, would constitute an effective strategy for preventing COVID-19. In this regard, the acetone-water extract of the leaf of the Neem (Azadirachta indica) plant has been known to prevent the adherence of malaria parasitized red blood cells (pRBCs) to VE; prevent cytoadherence of cancer cells in metastasis; and prevent HIV from invading target T lymphocytes. We therefore hypothesize that this Neem leaf acetone-water extract will prevent the binding of SARS-CoV-2 to the VE, and therefore be an effective therapeutic formulation against COVID-19. It is therefore advocated herein that this extract be investigated through rigorous clinical trials for this purpose. It has the advantages of being (i) readily available, and renewable in favor of the populations positioned to benefit from it; (ii) simple to prepare; and (iii) devoid of any detectable toxicity.