RESUMO
BACKGROUND: Cognitive dysfunction is regarded as one of the most severe aftereffects following coronavirus disease 2019 (COVID-19). Eye movements, controlled by several brain areas, such as the dorsolateral prefrontal cortex and frontal-thalamic circuits, provide a potential metric for assessing cortical networks and cognitive status. We aimed to examine the utility of eye movement measurements in identifying cognitive impairments in long COVID patients. METHODS: We recruited 40 long COVID patients experiencing subjective cognitive complaints and 40 healthy controls and used a certified eye-tracking medical device to record saccades and antisaccades. Machine learning was applied to enhance the analysis of eye movement data. RESULTS: Patients did not differ from the healthy controls regarding age, sex, and years of education. However, the patients' Montreal Cognitive Assessment total score was significantly lower than healthy controls. Most eye movement parameters were significantly worse in patients. These included the latencies, gain (computed as the ratio between stimulus amplitude and gaze amplitude), velocities, and accuracy (evaluated by the presence of hypermetric or hypometria dysmetria) of both visually and memory-guided saccades; the number of correct memory saccades; the latencies and duration of reflexive saccades; and the number of errors in the antisaccade test. Machine learning permitted distinguishing between long COVID patients experiencing subjective cognitive complaints and healthy controls. CONCLUSION: Our findings suggest impairments in frontal subcortical circuits among long COVID patients who report subjective cognitive complaints. Eye-tracking, combined with machine learning, offers a novel, efficient way to assess and monitor long COVID patients' cognitive dysfunctions, suggesting its utility in clinical settings for early detection and personalized treatment strategies. Further research is needed to determine the long-term implications of these findings and the reversibility of cognitive dysfunctions.
RESUMO
BACKGROUND: Lymphopenia is a major concern in MS patients treated with dimethyl-fumarate (DMF) as it increases the risk of progressive multifocal leukoencephalopathy. A pronounced reduction in absolute lymphocyte counts (ALCs) early after treatment initiation has been suggested to be associated with the occurrence of lymphopenia thereafter. OBJECTIVES: To identify risk factors for DMF-induced lymphopenia and evaluate whether the degree of decrease in the ALCs three months after initiation of DMF treatment is a predictor of the subsequent development of lymphopenia. METHODS: In this real-world Spanish prospective multicenter study conducted in MS patients who started DMF between 2014 and 2019, we analyzed the association between DMF-related lymphopenia and the percentage of early ALCs decline using regression models, considering both, significant lymphopenia (grades 2 + 3) and severe lymphopenia (grade 3). The cutoff values of early ALCs declines were obtained using the ROC curve. RESULTS: Among 532 MS patients treated with DMF, 193 (36.3%) developed any grade of lymphopenia. Older age and lower ALCs at treatment onset predicted the risk for lymphopenia but the best predictive risk factor was the reduction of ALCs within the three first months of treatment. Specifically, a reduction in ALCs≥21.2% was associated with a 6.5-fold higher risk of developing significant lymphopenia, and a decrease in ALCs≥40.2% with a 12.7-fold higher risk of developing severe lymphopenia. CONCLUSIONS: A pronounced reduction in ALCs early after initiation of DMF in MS patients is the best predictive risk factor for the subsequent development of significant lymphopenia.
