No genetic causal association between systemic lupus erythematosus and COVID-19.

Objective: Emerging evidence suggests an increased prevalence of coronavirus disease 2019 (COVID-19) in patients with systemic lupus erythematosus (SLE), the prototype of autoimmune disease, compared to the general population. However, the conclusions were inconsistent, and the causal relationship between COVID-19 and SLE remains unknown. Methods: In this study, we aimed to evaluate the bidirectional causal relationship between COVID-19 and SLE using bidirectional Mendelian randomization (MR) analysis, including MR-Egger, weighted median, weighted mode, and the inverse variance weighting (IVW) method. Results: The results of IVW showed a negative effect of SLE on severe COVID-19 (OR = 0.962, p = 0.040) and COVID-19 infection (OR = 0.988, p = 0.025), which disappeared after Bonferroni correction. No causal effect of SLE on hospitalized COVID-19 was observed (OR = 0.983, p = 0.148). In the reverse analysis, no causal effects of severe COVID-19 infection (OR = 1.045, p = 0.664), hospitalized COVID-19 (OR = 0.872, p = 0.109), and COVID-19 infection (OR = 0.943, p = 0.811) on SLE were found. Conclusion: The findings of our bidirectional causal inference analysis did not support a genetically predicted causal relationship between SLE and COVID-19; thus, their association observed in previous observational studies may have been caused by confounding factors.

Validating a membership disclosure metric for synthetic health data.

Background: One of the increasingly accepted methods to evaluate the privacy of synthetic data is by measuring the risk of membership disclosure. This is a measure of the F1 accuracy that an adversary would correctly ascertain that a target individual from the same population as the real data is in the dataset used to train the generative model, and is commonly estimated using a data partitioning methodology with a 0.5 partitioning parameter. Objective: Validate the membership disclosure F1 score, evaluate and improve the parametrization of the partitioning method, and provide a benchmark for its interpretation. Materials and methods: We performed a simulated membership disclosure attack on 4 population datasets: an Ontario COVID-19 dataset, a state hospital discharge dataset, a national health survey, and an international COVID-19 behavioral survey. Two generative methods were evaluated: sequential synthesis and a generative adversarial network. A theoretical analysis and a simulation were used to determine the correct partitioning parameter that would give the same F1 score as a ground truth simulated membership disclosure attack. Results: The default 0.5 parameter can give quite inaccurate membership disclosure values. The proportion of records from the training dataset in the attack dataset must be equal to the sampling fraction of the real dataset from the population. The approach is demonstrated on 7 clinical trial datasets. Conclusions: Our proposed parameterization, as well as interpretation and generative model training guidance provide a theoretically and empirically grounded basis for evaluating and managing membership disclosure risk for synthetic data.

Pearson's patterns correlational of clinical risks at admissions with hospitalization outcomes during initial COVID-19 outbreak.

COVID-19 outbreaks have crushed our healthcare systems, which requires clinical guidance for the healthcare following the outbreaks. We conducted retrospective cohort studies with Pearson's pattern-based analysis of clinical parameters of 248 hospitalized patients with COVID-19. We found that dysregulated neutrophil densities were correlated with hospitalization duration before death (p = 0.000066, r = -0.45 for % neutrophil; p = 0.0001, r = -0.47 for neutrophil count). As such, high neutrophil densities were associated with mortality (p = 4.23 × 10-31 for % neutrophil; p = 4.14 × 10-27 for neutrophil count). These findings were further illustrated by a representative "second week crash" pattern and validated by an independent cohort (p = 5.98 × 10-11 for % neutrophil; p = 1.65 × 10-7 for neutrophil count). By contrast, low aspartate aminotransferase (AST) or lactate dehydrogenase (LDH) levels were correlated with quick recovery (p ≤ 0.00005). Collectively, these correlational at-admission findings may provide healthcare guidance for patients with COVID-19 in the absence of targeted therapy.

Seroprevalence of SARS-CoV-2-specific antibodies and vaccination-related adverse events in systemic lupus erythematosus and rheumatoid arthritis.

BACKGROUND: This study aimed to investigate the seroreactivity of Coronavirus disease 2019 (COVID-19) vaccination and its adverse events among systemic lupus erythematosus (SLE) patients, rheumatoid arthritis (RA) patients, and healthy controls (HCs). METHODS: A total of 60 SLE patients, 70 RA patients and 35 HCs, who received a complete inactivated COVID-19 vaccine (Vero cells) regimen, were recruited in the current study. Serum IgG and IgM antibodies against Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) were determined by using chemiluminescent microparticle immunoassay (CMIA). RESULTS: There were no significant differences regarding the seroprevalences of IgG and IgM antibodies against SARS-CoV-2, and the self-reported vaccination-related adverse events among SLE patients, RA patients and HCs. The inactivated COVID-19 vaccines appeared to be well-tolerated and moderately immunogenic. In addition, case-only analysis indicated that in SLE patients, the disease manifestation of rash and anti-SSA autoantibody were associated with seroprevalence of IgG antibody against SARS-CoV-2, whereas the uses of ciclosporin and leflunomide had influence on the seroprevalence of IgM antibody against SARS-CoV-2. In RA patients, rheumatoid factor (RF) appeared to be associated with the seroprevalence of IgG antibody against SARS-CoV-2. CONCLUSION: Our study reveals that the seroprevalences of IgG and IgM antibodies against SARS-CoV-2 and vaccination-related adverse effects are similar among SLE, RA and HCs, suggesting that COVID-19 vaccine is safe and effective for SLE and RA patients to prevent from the pandemic of COVID-19.

Prevention and Control of COVID-19 after Resuming General Hospital Functions.

During the COVID-19 pandemic, many general hospitals have been transformed into designated infectious disease care facilities, where a large number of patients with COVID-19 infections have been treated and discharged. With declines in the number of hospitalizations, a major question for our healthcare systems, especially for these designated facilities, is how to safely resume hospital function after these patients have been discharged. Here, we take a designated COVID-19-care facility in Wuhan, China, as an example to share our experience in resuming hospital function while ensuring the safety of patients and medical workers. After more than 1200 patients with COVID-19 infections were discharged in late March, 2020, our hospital resumed function by setting up a three-level hospital infection management system with four grades of risk of exposure. Moreover, we also took measures to ensure the safety of medical personnel in different departments including clinics, wards, and operation rooms. After all patients with COVID-19 infections were discharged, during the five months of regular function from April to September in 2020, no positive cases have been found among more than 40,000 people in our hospital, including hospital staff and patients.

Attitudes to Being Vaccinated Against COVID-19: A Survey of People With Epilepsy in China.

Objective: We conducted a survey to assess vaccination coverage, vaccination willingness, and variables associated with vaccination hesitancy to provide evidence on coronavirus disease (COVID-19) vaccination strategies. Methods: This anonymous questionnaire study conducted a multicenter, cross-sectional survey of outpatients and inpatients with epilepsy (PWE) registered in epilepsy clinics, in 2021, in 10 hospitals in seven cities of Shandong Province. Results: A total of 600 questionnaires were distributed, and 557 valid questionnaires were returned. A total of 130 people were vaccinated against COVID-19. Among 427 unvaccinated participants, 69.32% (296/427) were willing to receive the COVID-19 vaccine in the future, and the remaining 30.68% (131/427) were unwilling to receive vaccination. Most (89.9%) of the participants believed that the role of vaccination was crucial in response to the spread of COVID-19. A significant association was found between willingness to receive the COVID-19 vaccine and the following variables: age, marital status, level of education, occupation, residence, seizure type, and seizure control after antiepileptic drug therapy. It is noteworthy that education level, living in urban areas, and seizure freedom were significantly related to willingness to receive COVID-19 vaccination. Conclusions: Vaccination is a key measure for the prevention and control of COVID-19, and most PWE are willing to be vaccinated. Vaccine safety, effectiveness, and accessibility are essential in combatting vaccine hesitation and increasing vaccination rates.

Emerging Evidence for Neuropsycho-Consequences of COVID-19.

The pandemic novel coronavirus disease (COVID-19) has become a global concern in which the respiratory system is not the only one involved. Previous researches have presented the common clinical manifestations including respiratory symptoms (i.e., fever and cough), fatigue and myalgia. However, there is limited evidence for neurological and psychological influences of SARS-CoV-2. In this review, we discuss the common neurological manifestations of COVID-19 including acute cerebrovascular disease (i.e., cerebral hemorrhage) and muscle ache. Possible viral transmission to the nervous system may occur via circulation, an upper nasal transcribrial route and/or conjunctival route. Moreover, we cannot ignore the psychological influence on the public, medical staff and confirmed patients. Dealing with public psychological barriers and performing psychological crisis intervention are an important part of public health interventions.

Association of AI quantified COVID-19 chest CT and patient outcome.

PURPOSE: Severity scoring is a key step in managing patients with COVID-19 pneumonia. However, manual quantitative analysis by radiologists is a time-consuming task, while qualitative evaluation may be fast but highly subjective. This study aims to develop artificial intelligence (AI)-based methods to quantify disease severity and predict COVID-19 patient outcome. METHODS: We develop an AI-based framework that employs deep neural networks to efficiently segment lung lobes and pulmonary opacities. The volume ratio of pulmonary opacities inside each lung lobe gives the severity scores of the lobes, which are then used to predict ICU admission and mortality with three different machine learning methods. The developed methods were evaluated on datasets from two hospitals (site A: Firoozgar Hospital, Iran, 105 patients; site B: Massachusetts General Hospital, USA, 88 patients). RESULTS: AI-based severity scores are strongly associated with those evaluated by radiologists (Spearman's rank correlation 0.837, [Formula: see text]). Using AI-based scores produced significantly higher ([Formula: see text]) area under the ROC curve (AUC) values. The developed AI method achieved the best performance of AUC = 0.813 (95% CI [0.729, 0.886]) in predicting ICU admission and AUC = 0.741 (95% CI [0.640, 0.837]) in mortality estimation on the two datasets. CONCLUSIONS: Accurate severity scores can be obtained using the developed AI methods over chest CT images. The computed severity scores achieved better performance than radiologists in predicting COVID-19 patient outcome by consistently quantifying image features. Such developed techniques of severity assessment may be extended to other lung diseases beyond the current pandemic.

Mild manifestations of COVID-19 in healthcare workers.

Medical staff treating Coronavirus Disease 2019 (COVID-19) patients are at high risk for exposure to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), and many have been infected, which may cause panic among medical workers, their relatives, health professionals, and government leaders. We report the epidemiologic and clinical characteristics of healthcare workers and that the majority of infected medical staff had milder symptoms/conditions with a better prognosis than admitted patients. Timely improvement to medical staff's working conditions such as allowing adequate rest and providing sufficient medical protection is extremely important.

Integrative analysis for COVID-19 patient outcome prediction.

While image analysis of chest computed tomography (CT) for COVID-19 diagnosis has been intensively studied, little work has been performed for image-based patient outcome prediction. Management of high-risk patients with early intervention is a key to lower the fatality rate of COVID-19 pneumonia, as a majority of patients recover naturally. Therefore, an accurate prediction of disease progression with baseline imaging at the time of the initial presentation can help in patient management. In lieu of only size and volume information of pulmonary abnormalities and features through deep learning based image segmentation, here we combine radiomics of lung opacities and non-imaging features from demographic data, vital signs, and laboratory findings to predict need for intensive care unit (ICU) admission. To our knowledge, this is the first study that uses holistic information of a patient including both imaging and non-imaging data for outcome prediction. The proposed methods were thoroughly evaluated on datasets separately collected from three hospitals, one in the United States, one in Iran, and another in Italy, with a total 295 patients with reverse transcription polymerase chain reaction (RT-PCR) assay positive COVID-19 pneumonia. Our experimental results demonstrate that adding non-imaging features can significantly improve the performance of prediction to achieve AUC up to 0.884 and sensitivity as high as 96.1%, which can be valuable to provide clinical decision support in managing COVID-19 patients. Our methods may also be applied to other lung diseases including but not limited to community acquired pneumonia. The source code of our work is available at https://github.com/DIAL-RPI/COVID19-ICUPrediction.

Integrative Analysis for COVID-19 Patient Outcome Prediction.

While image analysis of chest computed tomography (CT) for COVID-19 diagnosis has been intensively studied, little work has been performed for image-based patient outcome prediction. Management of high-risk patients with early intervention is a key to lower the fatality rate of COVID-19 pneumonia, as a majority of patients recover naturally. Therefore, an accurate prediction of disease progression with baseline imaging at the time of the initial presentation can help in patient management. In lieu of only size and volume information of pulmonary abnormalities and features through deep learning based image segmentation, here we combine radiomics of lung opacities and non-imaging features from demographic data, vital signs, and laboratory findings to predict need for intensive care unit (ICU) admission. To our knowledge, this is the first study that uses holistic information of a patient including both imaging and non-imaging data for outcome prediction. The proposed methods were thoroughly evaluated on datasets separately collected from three hospitals, one in the United States, one in Iran, and another in Italy, with a total 295 patients with reverse transcription polymerase chain reaction (RT-PCR) assay positive COVID-19 pneumonia. Our experimental results demonstrate that adding non-imaging features can significantly improve the performance of prediction to achieve AUC up to 0.884 and sensitivity as high as 96.1%, which can be valuable to provide clinical decision support in managing COVID-19 patients. Our methods may also be applied to other lung diseases including but not limited to community acquired pneumonia.