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
Department of Neurology, Hospital Clínico San Carlos Health Research Institute "San Carlos" (IdISCC), Universidad Complutense de Madrid, 28040 Madrid, Spain.
Department of Electronic Engineering, Universidad Politécnica de Madrid, 28040 Madrid, Spain.
J Clin Med. 2022 Jul 4;11(13):3886. 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 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.
疲劳是几种神经系统疾病中最使人丧失能力的症状之一，并且具有重要的认知成分。然而，自我报告的认知疲劳与客观认知评估结果之间的关系仍然不明确。新冠后综合征患者在急性感染后的几个月里经常报告疲劳和认知问题。我们旨在使用神经心理学评估来开发疲劳预测模型，以评估认知疲劳与客观神经心理学评估结果之间的关系。我们对113名新冠后综合征患者进行了一项横断面研究，使用改良疲劳影响量表（MFIS）和包括标准化及计算机化认知测试的综合神经心理测验组对他们进行评估。基于神经心理测试分数开发了几种机器学习算法来预测MFIS分数（总分和认知疲劳分数）。MFIS仅与斯特鲁普颜色-文字干扰测试显示出中等相关性。分类模型在疲劳与非疲劳之间或3或4级疲劳严重程度之间的分类中获得了适度的F1分数。用于估计MFIS分数的回归模型未达到足够的指标。我们的研究未发现新冠后综合征中认知疲劳的可靠神经心理学预测因素。这对疲劳和认知评估的解释具有重要意义。具体而言，MFIS认知领域无法正确捕捉实际的认知疲劳。此外，我们的研究结果表明新冠后综合征中疲劳和认知功能障碍存在不同的病理生理机制。
