Neurophenotypes of COVID-19: Risk factors and recovery outcomes

Change in Neuropsychological Test Performance Seen in a Longitudinal Study of Patients With Post-acute Sequelae of COVID-19: A 6-Month Follow-up Study

BACKGROUND

Post-acute sequelae of COVID-19 may include physical, psychiatric, and neurocognitive symptoms. Few studies of cognitive symptoms have been longitudinal, with many following participants briefly after infection and relying on subjective complaints, screening instruments, or computerized testing. This group previously reported diminished neuropsychological (NP) test performance in over half of 60 individuals tested in-person 7 months post-COVID-19, particularly those seeking care for cognitive complaints. The current study describes the initial and 6-month follow-up results of an expanded cohort of 75 participants.

OBJECTIVE

To measure longitudinal changes in neuropsychological test performance, as well as medical and psychiatric changes, post-COVID-19.

METHODS

Participants underwent NP, psychiatric, and medical assessments approximately 7 months after acute COVID-19 infection. Sixty-three (84%) returned approximately 6 months later for repeat evaluation.

RESULTS

At the initial visit, 29 (38.7%) met criteria for low NP performance, and 16 (21.3%) met criteria for extremely low NP performance. At 6-month follow-up, several NP domains that were significantly below normative values at the initial visit were no longer abnormal, with the exception of language. Only measures of delayed memory and fatigue showed significant improvements between the 2 time points.

CONCLUSIONS

A substantial proportion of individuals recovered from acute COVID-19 infection have persistent neuropsychiatric symptoms over 1 year after infection. While the overall sample in this study showed some improvement in NP test performance relative to norms, only fatigue and delayed memory improved significantly between times 1 and 2. No individual declined in NP test performance, though relatively few individuals made significant clinical improvement, indicating the need for serial neuropsychiatric assessment and treatment supports. Longitudinal follow-up of this cohort is in progress.

Cognitive reserve, depressive symptoms, obesity, and change in employment status predict mental processing speed and executive function after COVID-19

The risk factors for post-COVID-19 cognitive impairment have been poorly described. This study aimed to identify the sociodemographic, clinical, and lifestyle characteristics that characterize a group of post-COVID-19 condition (PCC) participants with neuropsychological impairment. The study sample included 426 participants with PCC who underwent a neurobehavioral evaluation. We selected seven mental speed processing and executive function variables to obtain a data-driven partition. Clustering algorithms were applied, including K-means, bisecting K-means, and Gaussian mixture models. Different machine learning algorithms were then used to obtain a classifier able to separate the two clusters according to the demographic, clinical, emotional, and lifestyle variables, including logistic regression with least absolute shrinkage and selection operator (LASSO) (L1) and Ridge (L2) regularization, support vector machines (linear/quadratic/radial basis function kernels), and decision tree ensembles (random forest/gradient boosting trees). All clustering quality measures were in agreement in detecting only two clusters in the data based solely on cognitive performance. A model with four variables (cognitive reserve, depressive symptoms, obesity, and change in work situation) obtained with logistic regression with LASSO regularization was able to classify between good and poor cognitive performers with an accuracy and a weighted averaged precision of 72%, a recall of 73%, and an area under the curve of 0.72. PCC individuals with a lower cognitive reserve, more depressive symptoms, obesity, and a change in employment status were at greater risk for poor performance on tasks requiring mental processing speed and executive function. Study registration: www.ClinicalTrials.gov , identifier NCT05307575.

Cognitive phenotypes in late-onset epilepsy: results from the atherosclerosis risk in communities study

Introduction

Cognitive phenotyping is a widely used approach to characterize the heterogeneity of deficits in patients with a range of neurological disorders but has only recently been applied to patients with epilepsy. In this study, we identify cognitive phenotypes in older adults with late-onset epilepsy (LOE) and examine their demographic, clinical, and vascular profiles. Further, we examine whether specific phenotypes pose an increased risk for progressive cognitive decline.

Methods

Participants were part of the Atherosclerosis Risk in Communities Study (ARIC), a prospective longitudinal community-based cohort study of 15,792 individuals initially enrolled in 1987-1989. LOE was identified from linked Centers for Medicare and Medicaid Services claims data. Ninety-one participants with LOE completed comprehensive testing either prior to or after seizure onset as part of a larger cohort in the ARIC Neurocognitive Study in either 2011-2013 or 2016-2017 (follow-up mean = 4.9 years). Cognitive phenotypes in individuals with LOE were derived by calculating test-level impairments for each participant (i.e., ≤1 SD below cognitively normal participants on measures of language, memory, and executive function/processing speed); and then assigning participants to phenotypes if they were impaired on at least two tests within a domain. The total number of impaired domains was used to determine the cognitive phenotypes (i.e., Minimal/No Impairment, Single Domain, or Multidomain).

Results

At our baseline (Visit 5), 36.3% met criteria for Minimal/No Impairment, 35% for Single Domain Impairment (with executive functioning/ processing speed impaired in 53.6%), and 28.7% for Multidomain Impairment. The Minimal/No Impairment group had higher education and occupational complexity. There were no differences in clinical or vascular risk factors across phenotypes. Of those participants with longitudinal data (Visit 6; n = 24), 62.5% declined (i.e., progressed to a more impaired phenotype) and 37.5% remained stable. Those who remained stable were more highly educated compared to those that declined.

Discussion

Our results demonstrate the presence of identifiable cognitive phenotypes in older adults with LOE. These results also highlight the high prevalence of cognitive impairments across domains, with deficits in executive function/processing speed the most common isolated impairment. We also demonstrate that higher education was associated with a Minimal/No Impairment phenotype and lower risk for cognitive decline over time.