The determinants of COVID-induced brain dysfunctions after SARS-CoV-2 infection in hospitalized patients

Abstract

The severity of the pandemic and its consequences on health and social care systems were quite diverse and devastating. COVID-19 was associated with an increased risk of neurological and neuropsychiatric disorders after SARS-CoV-2 infection. We did a cross-sectional study of 3 months post-COVID consequences of 178 Cuban subjects. Our study has a unique CUBAN COVID-19 cohort of hospitalized COVID-19 patients and healthy subjects. We constructed a latent variable for pre-health conditions (PHC) through Item Response Theory (IRT) and for post-COVID neuropsychiatric symptoms (Post-COVID-NPS) through Factor Analysis (FA). There seems to be a potential causal relationship between determinants of CIBD and post-COVID-NPS in hospitalized COVID-19 patients. The causal relationships accessed by Structural Equation Modeling (SEM) revealed that PHC (p < 0.001) and pre-COVID cognitive impairments (p < 0.001) affect the severity of COVID-19 patients. The severity of COVID-19 eventually results in enhanced post-COVID-NPS (p < 0.001), even after adjusting for confounders (age, sex, and pre-COVID-NPS). The highest loadings in PHC were for cardiovascular diseases, immunological disorders, high blood pressure, and diabetes. On the other hand, sex (p < 0.001) and pre-COVID-NPS including neuroticism (p < 0.001), psychosis (p = 0.005), cognition (p = 0.036), and addiction (p < 0.001) were significantly associated with post-COVID-NPS. The most common neuropsychiatric symptom with the highest loadings includes pain, fatigue syndrome, autonomic dysfunctionalities, cardiovascular disorders, and neurological symptoms. Compared to healthy people, COVID-19 patients with pre-health comorbidities or pre-neuropsychiatric conditions will have a high risk of getting severe COVID-19 and long-term post-COVID neuropsychiatric consequences. Our study provides substantial evidence to highlight the need for a complete neuropsychiatric follow-up on COVID-19 patients (with severe illness) and survivors (asymptomatic patients who recovered).

Keywords: COVID-19; SARS-CoV-2; long-COVID; neuropsychology; post-COVID neuropsychiatric symptoms/disorders.

FIGURE 1

Overview of methodology (statistical analysis) for our CUBAN COVID-19 dataset.

FIGURE 2

A Quantile-Quantile (Q–Q) plot for the standardized residual vs. a theoretical normal distribution to ensure a linear trend.

FIGURE 3

Histogram for standardized regression residuals to check for normality using the “moments” package in R. It shows that the data has a symmetrical distribution and is not skewed.

FIGURE 4

A homoscedasticity graph (scatter plot) between standardized regression residuals and z-scored fitted value where the data spread is homogeneous.

FIGURE 5

Correlation plot for measured variables using the “corrplot” package in R. The blue color shows a positive correlation; the dark color depicts a value of 0.96.

FIGURE 6

Item characteristic curves for pre-health conditions (PHC) via item response theory analysis (IRT). Each item’s response indicates a certain degree of loadings on the latent traits. Simply put, the latent variable (θ) influences and distinguishes the likelihood of reporting positive on the items in pre-health conditions.

FIGURE 7

Relationship of COVID-19 with post-COVID-NPS of COVID-induced brain dysfunctions (CIBD) shown by structural equation modeling (SEM). A circle denotes a latent variable. A square denotes a measured variable. Directed arrows denote putative causal relations.