Assessment of subtle cognitive impairments in patients with post-COVID syndrome with the tablet-based Oxford Cognitive Screen-Plus (OCS-Plus).
Kozik, V.; Reuken, P.; Utech, I.; Gramlich, J.; Stallmach, Z.; Demeyere, N.; Stallmach, A.; Finke, K.
Show abstract
Background and objectivesCognitive symptoms persisting beyond three months following COVID-19 present a considerable disease burden. We aimed to establish a domain-specific cognitive profile of post-COVID syndrome (PCS) and relationships with subjective cognitive complaints and clinical variables to provide relevant information for the understanding of cognitive dysfunction and its predictors in a clinical cohort with PCS. MethodsIn this cross-sectional study, we compared cognitive performance on the clinically viable Oxford Cognitive Screen-Plus between a large post-COVID cohort (n = 282) and a socio-demographically matched healthy control group (n = 52). We assessed group differences in terms of fatigue and depression as well as relationships between cognitive dysfunction and clinical and patient-reported outcomes. ResultsOn a group-level, patients scored significantly lower on delayed verbal memory (non-parametric effect size r = .13), attention (r = .1), and executive functioning (r=.1) than healthy controls. In each of these domains, 10-20% of patients performed more than 1.5 SD below the healthy control mean. Delayed Memory was particularly affected and a small proportion of its variance was explained by hospitalisation ({beta} = -.72, p < .01) and age ({beta} = -.03, p < .05; R2adj. = .08). Attention scores were significantly predicted by hospitalisation ({beta} = -.78, p < .01) and fatigue ({beta} = -.04, p < .05; R2adj. = .06). DiscussionPCS is associated with long-term cognitive dysfunction, particularly in delayed verbal memory, attention, and executive functioning. Deficits in delayed memory performance seem to be of particular relevance to patients subjective experience of impairment. Initial disease severity, current level of fatigue, and age seem to predict cognitive performance, while time since infection, depression, and pre-existing conditions do not. Longitudinal data are needed to map long-term course of cognitive dysfunction in PCS.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Self-reported health, neuropsychological tests and biomarkers in fully recovered COVID-19 patients vs patients with post-COVID cognitive symptoms: a pilot study 96%
- The negative impact of COVID-19 on working memory revealed using a rapid online quiz 95%
- The Healthy Brain 9 (HB9): A New Instrument to Characterize Subjective Cognitive Decline, and Detect Anosognosia in Mild Cognitive Impairment 94%
Similar papers in this journal
- Patients Recovering from COVID-19 who Presented Anosmia During their Acute Episode have Behavioral, Functional, and Structural Brain Alterations 93%
- Correlates of patient-reported cognitive performance with regard to disability 92%
- A distinct symptom pattern emerges for COVID-19 Long-Haul: A nationwide study 91%
Similar papers in this journal
- Chronic post-COVID neuropsychiatric symptoms persisting beyond one year from infection: a case-control study and network analysis 93%
- mtDNA copy number, mtDNA total somatic deletions, Complex I activity, synapse number and synaptic mitochondria number are altered in schizophrenia and bipolar disorder 91%
- Metabolic Disturbances, Hemoglobin A1c, and Social Cognition Impairment in Schizophrenia Spectrum Disorders 91%
Similar papers in this journal
- The association between dysnatraemia during hospitalisation and post COVID-19 mental fatigue 95%
- Chronic fatigue, depression and anxiety symptoms in Long COVID are strongly predicted by neuroimmune and neuro-oxidative pathways which are caused by the inflammation during acute infection 93%
- Biopsychosocial response to the COVID-19 lockdown in people with major depressive disorder and multiple sclerosis 91%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.