Bayesian Functional Analysis for Untargeted Metabolomics Data with Matching Uncertainty and Small Sample Sizes (preprint)

Untargeted metabolomics based on liquid chromatography-mass spectrometry technology is quickly gaining widespread application given its ability to depict the global metabolic pattern in biological samples. However, the data is noisy and plagued by the lack of clear identity of data features measured from samples. Multiple potential matchings exist between data features and known metabolites, while the truth can only be one-to-one matches. Some existing methods attempt to reduce the matching uncertainty, but are far from being able to remove the uncertainty for most features. The existence of the uncertainty causes major difficulty in downstream functional analysis. To address these issues, we develop a novel approach for Bayesian Analysis of Untargeted Metabolomics data (BAUM) to integrate previously separate tasks into a single framework, including matching uncertainty inference, metabolite selection, and functional analysis. By incorporating the knowledge graph between variables and using relatively simple assumptions, BAUM can analyze datasets with small sample sizes. By allowing different confidence levels of feature-metabolite matching, the method is applicable to datasets in which feature identities are partially known. Simulation studies demonstrate that, compared with other existing methods, BAUM achieves better accuracy in selecting important metabolites that tend to be functionally consistent and assigning confidence scores to feature-metabolite matches. We analyze a COVID-19 metabolomics dataset and a mouse brain metabolomics dataset using BAUM. Even with a very small sample size of 16 mice per group, BAUM is robust and stable. It finds pathways that conform to existing knowledge, as well as novel pathways that are biologically plausible.

Machine Learning to Assess the Prognostic Utility of Radiomic Features for In-hospital COVID-19 Mortality (preprint)

As portable chest X-rays are an efficient means of triaging emergent cases, their increased use has raised the question as to whether imaging carries additional prognostic utility for survival among patients with COVID-19. This study assessed the importance of known risk factors on in-hospital mortality and to investigate the predictive utility of radiomic texture features using various machine learning approaches. We detected incremental improvements in survival prognostication utilizing texture features derived from emergent chest X-rays, particularly among older patients or those with higher comorbidity burden. Important features included age, oxygen saturation, blood pressure, and certain comorbid conditions, as well as image features related to the intensity and variability of the pixel distribution. Thus, widely available chest X-rays, in conjunction with clinical information, may be predictive of survival outcomes of patients with COVID-19, especially older, sicker patients, and can aid in disease management by providing additional information.

Understanding the dynamic impact of COVID-19 through competing risk modeling with bivariate varying coefficients (preprint)

The coronavirus disease 2019 (COVID-19) pandemic has exerted a profound impact on patients with end-stage renal disease relying on kidney dialysis to sustain their lives. Motivated by a request by the U.S. Centers for Medicare & Medicaid Services, our analysis of their postdischarge hospital readmissions and deaths in 2020 revealed that the COVID-19 effect has varied significantly with postdischarge time and time since the onset of the pandemic. However, the complex dynamics of the COVID-19 effect trajectories cannot be characterized by existing varying coefficient models. To address this issue, we propose a bivariate varying coefficient model for competing risks within a cause-specific hazard framework, where tensor-product B-splines are used to estimate the surface of the COVID-19 effect. An efficient proximal Newton algorithm is developed to facilitate the fitting of the new model to the massive Medicare data for dialysis patients. Difference-based anisotropic penalization is introduced to mitigate model overfitting and the wiggliness of the estimated trajectories; various cross-validation methods are considered in the determination of optimal tuning parameters. Hypothesis testing procedures are designed to examine whether the COVID-19 effect varies significantly with postdischarge time and the time since pandemic onset, either jointly or separately. Simulation experiments are conducted to evaluate the estimation accuracy, type I error rate, statistical power, and model selection procedures. Applications to Medicare dialysis patients demonstrate the real-world performance of the proposed methods.

Bayesian learning of COVID-19 Vaccine safety while incorporating Adverse Events ontology (preprint)

While vaccines are crucial to end the COVID-19 pandemic, public confidence in vaccine safety has always been vulnerable. Many statistical methods have been applied to VAERS (Vaccine Adverse Event Reporting System) database to study the safety of COVID-19 vaccines. However, all these methods ignored the adverse event (AE) ontology. AEs are naturally related; for example, events of retching, dysphagia, and reflux are all related to an abnormal digestive system. Explicitly bringing AE relationships into the model can aid in the detection of true AE signals amid the noise while reducing false positives. We propose a Bayesian graphical model to estimate all AEs while incorporating the AE ontology simultaneously. We proposed strategies to construct conjugate forms leading to an efficient Gibbs sampler. Built upon the posterior distributions, we proposed a negative control approach to mitigate reporting bias and an enrichment approach to detect AE groups of concern. The proposed methods were evaluated using simulation studies and were further illustrated on studying the safety of COVID-19 vaccines. The proposed methods were implemented in R package \textit{BGrass} and source code are available at https://github.com/BangyaoZhao/BGrass.

Comprehensive Evaluation of COVID-19 Patient Short- and Long-term Outcomes: Disparities in Healthcare Utilization and Post-Hospitalization Outcomes (preprint)

BackgroundUnderstanding risk factors for short- and long-term COVID-19 outcomes have implications for current guidelines and practice. We study whether early identified risk factors for COVID-19 persist one year later and through varying disease progression trajectories. MethodsThis was a retrospective study of 6,731 COVID-19 patients presenting to Michigan Medicine between March 10, 2020 and March 10, 2021. We describe disease progression trajectories from diagnosis to potential hospital admission, discharge, readmission, or death. Outcomes pertained to all patients: rate of medical encounters, hospitalization-free survival, and overall survival, and hospitalized patients: discharge versus in-hospital death and readmission. Risk factors included patient age, sex, race, body mass index, and 29 comorbidity conditions. ResultsYounger, non-Black patients utilized healthcare resources at higher rates, while older, male, and Black patients had higher rates of hospitalization and mortality. Diabetes with complications, coagulopathy, fluid and electrolyte disorders, and blood loss anemia were risk factors for these outcomes. Diabetes with complications, coagulopathy, fluid and electrolyte disorders, and blood loss were associated with lower discharge and higher inpatient mortality rates. ConclusionsThis study found differences in healthcare utilization and adverse COVID-19 outcomes, as well as differing risk factors for short- and long-term outcomes throughout disease progression. These findings may inform providers in emergency departments or critical care settings of treatment priorities, empower healthcare stakeholders with effective disease management strategies, and aid health policy makers in optimizing allocations of medical resources.

The Spatial-Temporal Distribution and Control Measures Evaluation of Droplets and Aerosol Cloud in Dental Procedures (preprint)

Background: Droplets and aerosol cloud generating procedures in dentistry can increase the risk of airborne transmission of diseases such as COVID-19. To gain insight into the diffusion of spatters and possible preventive measures, we measured the particle spatial-temporal distribution characteristic and evaluated the effectiveness of the control measures.Methods: We conducted an experiment to observe the emitted spatters obtained during the simulated dental preparation by using high-speed videography. We measured the particle size distributions by laser diffraction and preliminarily estimated its velocity. We qualitatively and quantitatively described the spatial-temporal distributions of spatters and their control measure effects. Results: Majority of the dental spatters were small droplets (diameter less than 50 μm). A large number of smallest droplets (diameter less than 10 μm) were generated by high-speed air turbine handpiece. At the oral outlet, the speed of large droplets could exceed 2.63 m/s, and the speed of aerosol clouds ranged from 0.31–2.37 m/s. The evolution of the spatters showed that the more fully developed the state, the greater the number of spatters and the wider the contamination range. When the operation mode was moved from the central incisor to the first molar, the spatter direction became increasingly concentrated, and the velocities were enhanced. Larger droplets randomly moved along trajectories and rapidly settled. The aerosol cloud tended to float as a mass that interacted with the surrounding air. The high-volume evacuation could effectively clear away most of the dental spatters. The suction air purifier could change the diffusion direction of the spatters, compress the contamination range, and control aerosol escape into surrounding air. Conclusions: Our view is that we should combine the ‘point’ control measure (high-volume evacuation) and ‘area’ control measure (suction air purifier) to reduce the scope of pollution and prevent the aerosol escape into the surroundings. The study contributes to devising more accurate infection control guidelines, establishing appropriate interventions for different oral treatments, and minimizing the spread of respiratory diseases so that we can reduce cost and achieve the best results when medical resources are limited.

The Impact of Music Intervention on COVID-19 Patients with Mental Disorders: A Protocol for a Systematic Review and Meta-analysis (preprint)

Background: The COVID-19 pandemic caused by the SARS-CoV-2 virus is a major health crisis that is affecting countries across the world. Patients infected with COVID-19 are often associated with mental health disorders, such as anxiety, depression, and sleep disorders. As a non-drug therapy applied in clinics for many years, music intervention is safe, effective, inexpensive, and devoid of side effects. Yet, there is a distinct lack of evidence to support the use of this technique. In this study, we aim to collect and evaluate the clinical evidence, in order to provide a basis for the efficacy and safety of music intervention in the treatment of COVID-19 patients with mental disorders.Methods: We plan to search a range of electronic databases from inception to the May 2021, including PubMed, Embase, Cochrane Library, Web of Science, China National Knowledge Infrastructure, Wanfang Database, Chinese Biomedical Literature Database, and Chinese Science and Technology Periodical Database (VIP). All randomized controlled trials featuring music intervention to treat mental disorders such as anxiety, depression, or sleep disorders, for patients with COVID-19, will be included. The primary outcomes will be quantitative scores for anxiety, depression, and sleep disorder. The secondary outcomes will be quality of life and the safety profile of music intervention, including adverse events. Two reviewers will carry out the selection of studies, data extraction independently. The Cochrane risk of bias tool will be used to evaluate the risk of bias for the studies. We will use Review Manager V.5.3 software for data analysis. Subgroup analyses and sensitivity analyses are planned to assess the heterogeneity and reliability.Discussion: This is an up-to-date systematic review and meta-analysis of the efficacy and safety of music intervention on mental disorders (anxiety, depression, or sleep disorder) in COVID-19 patients, in order to provide clinicians, researchers, and policy makers, with powerful reference guidelines to facilitate treatment and improve the quality of life in COVID-19 patients with mental disorders.Systematic review registration: OSF 10.17605/OSF.IO/9RCX5

Deep Neural Networks Guided Ensemble Learning for Point Estimation (preprint)

In modern statistics, interests shift from pursuing the uniformly minimum variance unbiased estimator to reducing mean squared error (MSE) or residual squared error. Shrinkage based estimation and regression methods offer better prediction accuracy and improved interpretation. However, the characterization of such optimal statistics in terms of minimizing MSE remains open and challenging in many problems, for example estimating treatment effect in adaptive clinical trials with pre-planned modifications to design aspects based on accumulated data. From an alternative perspective, we propose a deep neural network based automatic method to construct an improved estimator from existing ones. Theoretical properties are studied to provide guidance on applicability of our estimator to seek potential improvement. Simulation studies demonstrate that the proposed method has considerable finite-sample efficiency gain as compared with several common estimators. In the Adaptive COVID-19 Treatment Trial (ACTT) as an important application, our ensemble estimator essentially contributes to a more ethical and efficient adaptive clinical trial with fewer patients enrolled. The proposed framework can be generally applied to various statistical problems, and can be served as a reference measure to guide statistical research.

Withdrawal of Management Guidance During the COVID-19 Pandemic (preprint)

The Covid-19 pandemic has produced unprecedented adverse macroeconomic conditions for many firms. It has also provoked a surge in firms withdrawing prior guidance—a corporate disclosure phenomenon that has captured the attention of media and practitioners. In this paper, we examine how firms’ vulnerability to the pandemic affects their guidance withdrawals. Our empirical analysis reveals that firms more affected by the pandemic are more likely to withdraw guidance, consistent with firms’ unwillingness to publicly commit to targets when facing macroeconomic adversity. The effect is more pronounced for firms facing higher litigation risk and product market competition, which is consistent with these conditions making the firm less willing to publicly commit to targets in the face of adversity. We also provide evidence that firms with greater exposure to the pandemic are likely to take the combined action of withdrawing prior guidance and stopping the issuance of new guidance. Moreover, we find that among firms that withdraw guidance, those whose performance subsequently improves are more likely to reinstate their guidance. Finally, we document that firms with greater exposure to the pandemic mention the pandemic less often during earnings conference calls, suggesting that the unwillingness to disclose information about the pandemic extends beyond quantitative disclosure to qualitative disclosure. Overall, our paper offers early and novel evidence on how the pandemic has affected corporate disclosure.

Care for Patients with Stroke During the COVID-19 Pandemic: Physical Therapy and Rehabilitation Suggestions for Preventing Secondary Stroke.

Infection with the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes the development of the novel 2019 coronavirus disease (COVID-19) and associated clinical symptoms, which typically presents as an upper respiratory syndrome such as pneumonia. Growing evidence indicates an increased prevalence of neurological involvement (e.g., in the form of stroke) during virus infection. COVID-19 has been suggested to be more than a lung infection because it affects the vasculature of the lungs and other organs and increases the risk of thrombosis. Patients with stroke are vulnerable to secondary events as a result not only of their poor vascular condition but also of their lack of access to rehabilitation resources. Herein, we review current knowledge regarding the pathophysiology of COVID-19, its possible association with neurological involvement, and current drug therapies. Suggestions are also offered regarding the potential for current neurorehabilitation therapies to be taught and practiced at home.

Discriminant Models for the Prediction of Viral Shedding Time and Disease Progression in COVID-19 (preprint)

Background COVID-19 infection can cause life-threatening respiratory disease. This study aimed to fully characterize the clinical features associated with postponed viral shedding and disease progression, then develop and validate two prognostic discriminant models.Methods This study included 125 hospitalized patients with COVID-19. 44 parameters were recorded, including age, gender, underlying comorbidities, epidemic features, laboratory indexes, imaging characteristics and therapeutic regimen, et al. F-test and χ2 test were used for feature selection. All models were developed with 4-fold cross-validation, and the final performances of each model were compared by the Area Under Receiving Operating Curve (AUROC). After optimizing the parameters via L2 regularization, prognostic discriminant models were built to predict postponed viral shedding and disease progression of COVID-19 infection. The test set was then used to detect the predictive values via assessing models sensitivity and specificity.Results 69 patients had a postponed viral shedding time (> 14 days), and 28 of 125 patients progressed into severe cases. Eleven and six demographic, clinical features and therapeutic regimen were significantly associated with postponed viral shedding and disease progressing, respectively (p < 0.05). The optimal discriminant models are: y1 (postponed viral shedding) = -0.244 + 0.2829x1 (the interval from the onset of symptoms to antiviral treatment) + 0.2306x4 (age) + 0.234x28 (Urea) − 0.2847x34 (Dual-antiviral therapy) + 0.3084x38 (Treatment with antibiotics) + 0.3025x21 (Treatment with Methylprednisolone); y2 (disease progression) = -0.348–0.099x2 (interval from Jan 1st, 2020 to individualized onset of symptoms) + 0.0945x4 (age) + 0.1176x5 (imaging characteristics) + 0.0398x8 (short- term exposure to Wuhan) − 0.1646x19 (lymphocyte counts) + 0.0914x20 (neutrophil counts) + 0.1254x21 (neutrphil/lymphocyte ratio) + 0.1397x22 (C-Reactive Protein) + 0.0814x23 (Procalcitonin) + 0.1294x24 (Lactic dehydrogenase) + 0.1099x29 (Creatine kinase). The output ≥ 0 predicted postponed viral shedding or disease progressing to severe/critical state. These two models yielded the maximum AUROC, and faired best in terms of prognostic performance (sensitivity of 73.3%, 75%, and specificity of 78.6%, 75% for prediction of postponed viral shedding and disease severity, respectively).Conclusion The two discriminant models could effectively predict the postponed viral shedding and disease severity, and be used as early-warning tools for COVID-19.

Care for patients with musculoskeletal pain during the COVID-19 pandemic: Physical therapy and rehabilitation suggestions for pain management.

Coronavirus disease 2019 has severely affected public health. Under social distancing and lockdown policies, patients with musculoskeletal pain have fewer opportunities than usual to receive routine medical care for pain management in hospitals. Therefore, we provided some suggestions for such patients to manage musculoskeletal pain and techniques that may be performed at home during this period.

Asymptomatic novel coronavirus pneumonia presenting as acute cerebral infarction:case report and review of the literature / 中华急诊医学杂志

An outbreak of novel coronavirus pneumonia that began in Wuhan, China, has spread rapidly in December 2019, with cases now confirmed in multiple countries. As the number of cases increases, we pay more and more attention to asymptomatic novel coronavirus pneumonia,We report the first case of Asymptomatic novel coronavirus pneumonia presenting as acute cerebral infarction and describe the identification, diagnosis, clinical course, and emergency treatment, including. This case highlights the the importance of emergency medical teams in initial assessment of emergency public health emergencies, as well as the necessary of the emergency chest CT for screening asymptomatic novel coronavirus pneumonia.