TopEx: topic exploration of COVID-19 corpora - Results from the BioCreative VII Challenge Track 4.

TopEx is a natural language processing application developed to facilitate the exploration of topics and key words in a set of texts through a user interface that requires no programming or natural language processing knowledge, thus enhancing the ability of nontechnical researchers to explore and analyze textual data. The underlying algorithm groups semantically similar sentences together followed by a topic analysis on each group to identify the key topics discussed in a collection of texts. Implementation is achieved via a Python library back end and a web application front end built with React and D3.js for visualizations. TopEx has been successfully used to identify themes, topics and key words in a variety of corpora, including Coronavirus disease 2019 (COVID-19) discharge summaries and tweets. Feedback from the BioCreative VII Challenge Track 4 concludes that TopEx is a useful tool for text exploration for a variety of users and tasks. DATABSE URL: http://topex.cctr.vcu.edu.

Development and Validation of a Two-Step Predictive Risk Stratification Model for Coronavirus Disease 2019 In-hospital Mortality: A Multicenter Retrospective Cohort Study.

Objectives: An accurate prognostic score to predict mortality for adults with COVID-19 infection is needed to understand who would benefit most from hospitalizations and more intensive support and care. We aimed to develop and validate a two-step score system for patient triage, and to identify patients at a relatively low level of mortality risk using easy-to-collect individual information. Design: Multicenter retrospective observational cohort study. Setting: Four health centers from Virginia Commonwealth University, Georgetown University, the University of Florida, and the University of California, Los Angeles. Patients: Coronavirus Disease 2019-confirmed and hospitalized adult patients. Measurements and Main Results: We included 1,673 participants from Virginia Commonwealth University (VCU) as the derivation cohort. Risk factors for in-hospital death were identified using a multivariable logistic model with variable selection procedures after repeated missing data imputation. A two-step risk score was developed to identify patients at lower, moderate, and higher mortality risk. The first step selected increasing age, more than one pre-existing comorbidities, heart rate >100 beats/min, respiratory rate ≥30 breaths/min, and SpO2 <93% into the predictive model. Besides age and SpO2, the second step used blood urea nitrogen, absolute neutrophil count, C-reactive protein, platelet count, and neutrophil-to-lymphocyte ratio as predictors. C-statistics reflected very good discrimination with internal validation at VCU (0.83, 95% CI 0.79-0.88) and external validation at the other three health systems (range, 0.79-0.85). A one-step model was also derived for comparison. Overall, the two-step risk score had better performance than the one-step score. Conclusions: The two-step scoring system used widely available, point-of-care data for triage of COVID-19 patients and is a potentially time- and cost-saving tool in practice.

The Fibrosis-4 Index Is Associated With Need for Mechanical Ventilation and 30-Day Mortality in Patients Admitted With Coronavirus Disease 2019.

The Fibrosis-4 Index (FIB-4), developed to predict fibrosis in liver disease, was used to identify patients with coronavirus disease 2019 who will require ventilator support as well as those associated with 30-day mortality. Multivariate analysis found obesity (odds ratio [OR], 4.5), diabetes mellitus (OR, 2.55), and FIB-4 ≥2.67 (OR, 3.09) independently associated with need for mechanical ventilation. When controlling for ventilator use, sex, and comorbid conditions, FIB-4 ≥2.67 was also associated with increased 30-day mortality (OR, 8.4 [95% confidence interval, 2.23-31.7]). Although it may not be measuring hepatic fibrosis, its components suggest that increases in FIB-4 may be reflecting systemic inflammation associated with poor outcomes.