COVID-19 research

COVID-19 Sequelae and neuroscience

As the world emerges from the pandemic caused by SARS-CoV-2, there is a need to understand patient factors that determine the effects of COVID-19, as well as the diagnostic features that may be used to predict the occurrence of severe cases and mortality. Approximately 20% of SARS-CoV-2 infections lead to acute respiratory distress syndrome caused by the harmful actions of inflammatory mediators. Patients with severe COVID-19 are often afflicted with neurologic symptoms, and individuals with pre-existing neurodegenerative disease have an increased risk of severe COVID-19. Although collectively, these observations point to links between severe COVID-19 and neurologic disorders, little is known about the mechanisms. We conduct a study to determine the relationship between the lethality of COVID-19 and CNS-related symptoms.

The preliminary findings from the first part of the study, in which the electronic health records of several hundred Indiana patients with severe COVID were analyzed to identify the clinical characteristics most predictive of COVID-19 fatalities. Feature discovery was conducted by training a regularized logistic regression classifier that serves as a machine-learning model with an embedded feature selection capability. SHAP analysis using the trained classifier revealed that a small ensemble of readily observable clinical features, including characteristics associated with cognitive impairment, could predict in-hospital mortality with an accuracy greater than 0.85 (expressed as the area under the ROC curve of the classifier). These findings have important implications for the prioritization of clinical measures used to identify patients with COVID-19 (and, potentially, other forms of acute respiratory distress syndrome) having an elevated risk of mortality.

My COVID-19 research has been supported by the COMMITTM (COvid-19 unMet MedIcal needs and associated research exTension) Program of the Gilead Foundation.

Click here to download the poster presented at the Annual Meeting of the Society of Neuroscience (Nov. 2022).

Page updated on 2022-12-07 18:42:07 -0500