Neuropsychological Predictors of Fatigue in Post-COVID Syndrome

• Published in: Journal of clinical medicine Vol. 11; no. 13; p. 3886
• Main Authors: Matias-Guiu, Jordi A., Delgado-Alonso, Cristina, Díez-Cirarda, María, Martínez-Petit, Álvaro, Oliver-Mas, Silvia, Delgado-Álvarez, Alfonso, Cuevas, Constanza, Valles-Salgado, María, Gil, María José, Yus, Miguel, Gómez-Ruiz, Natividad, Polidura, Carmen, Pagán, Josué, Matías-Guiu, Jorge, Ayala, José Luis
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 04.07.2022; MDPI
• Subjects: Algorithms; Clinical medicine; Cognitive ability; Coronaviruses; COVID-19; Fatigue; Infections; Machine learning; Multiple sclerosis; Neuropsychology; Pathophysiology; Severe acute respiratory syndrome coronavirus 2; Support vector machines
• ISSN: 2077-0383; 2077-0383
• DOI: 10.3390/jcm11133886

Fatigue is one of the most disabling symptoms in several neurological disorders and has an important cognitive component. However, the relationship between self-reported cognitive fatigue and objective cognitive assessment results remains elusive. Patients with post-COVID syndrome often report fatigue and cognitive issues several months after the acute infection. We aimed to develop predictive models of fatigue using neuropsychological assessments to evaluate the relationship between cognitive fatigue and objective neuropsychological assessment results. We conducted a cross-sectional study of 113 patients with post-COVID syndrome, assessing them with the Modified Fatigue Impact Scale (MFIS) and a comprehensive neuropsychological battery including standardized and computerized cognitive tests. Several machine learning algorithms were developed to predict MFIS scores (total score and cognitive fatigue score) based on neuropsychological test scores. MFIS showed moderate correlations only with the Stroop Color–Word Interference Test. Classification models obtained modest F1-scores for classification between fatigue and non-fatigued or between 3 or 4 degrees of fatigue severity. Regression models to estimate the MFIS score did not achieve adequate R2 metrics. Our study did not find reliable neuropsychological predictors of cognitive fatigue in the post-COVID syndrome. This has important implications for the interpretation of fatigue and cognitive assessment. Specifically, MFIS cognitive domain could not properly capture actual cognitive fatigue. In addition, our findings suggest different pathophysiological mechanisms of fatigue and cognitive dysfunction in post-COVID syndrome.