A data-driven approach to complement the A/T/(N) classification system using CSF biomarkers.

AIMS: The AT(N) classification system not only improved the biological characterization of Alzheimer's disease (AD) but also raised challenges for its clinical application. Unbiased, data-driven techniques such as clustering may help optimize it, rendering informative categories on biomarkers' values. METHODS: We compared the diagnostic and prognostic abilities of CSF biomarkers clustering results against their AT(N) classification. We studied clinical (patients from our center) and research (Alzheimer's Disease Neuroimaging Initiative) cohorts. The studied CSF biomarkers included Aß(1-42), Aß(1-42)/Aß(1-40) ratio, tTau, and pTau. RESULTS: The optimal solution yielded three clusters in both cohorts, significantly different in diagnosis, AT(N) classification, values distribution, and survival. We defined these three CSF groups as (i) non-defined or unrelated to AD, (ii) early stages and/or more delayed risk of conversion to dementia, and (iii) more severe cognitive impairment subjects with faster progression to dementia. CONCLUSION: We propose this data-driven three-group classification as a meaningful and straightforward approach to evaluating the risk of conversion to dementia, complementary to the AT(N) system classification.

Validation of two new scales for the assessment of fatigue in Multiple Sclerosis: F-2-MS and FACIT-F.

BACKGROUND: Fatigue is one of the most common symptoms in neurology, especially in MS patients with a prevalence of 65%. It is described as the most disabling symptom by 40% of MS patients. This study aimed to validate the Functional Assessment of Chronic Illness Therapy fatigue version (FACIT-F) and the F-2-MS scale, a new tool to distinguish between fatigue and fatigability. METHODS: One hundred and fifteen patients with relapsing-remitting MS were enrolled. All patients completed a comprehensive neuropsychological battery, previously validated in MS. Fatigue was evaluated using the Fatigue Severity Scale (FSS), the Modified version of the Fatigue Impact Scale (MFIS), the Functional Assessment of Chronic Illness Therapy measure system (fatigue version) (FCIT-F), and a new tool for the assessment of fatigue and fatigability: the F-2-MS scale. Internal consistency was estimated with Cronbach's Alpha. For intergroup comparisons, Student's t-test and Pearson's chi-squared test were used. Pearson's correlation test was calculated for quantitative variables. Cohen's d was calculated to evaluate the effect size. Binary logistic regression was performed, considering the presence of fatigue as a criterion variable, and the FACIT-F and F-2-MS scores were added as predictor variables. ROC curves were also estimated. We conducted a confirmatory factor analysis for the F-2-MS scale, considering two latent factors. RESULTS: FACIT-F and F-2-MS showed high internal consistency. Both scales were highly correlated with MFIS and FSS, and showed a low correlation with Symbol Digit Modalities Test. There were significant differences between fatigued and non-fatigued patients on FACIT-F and F-2-MS scores with large effect sizes. Both scales showed AUC > 0.90 and achieved a correct classification >87%. Confirmatory factor analysis showed moderate evidence of two dimensions on the F-2-MS scale. CONCLUSIONS: The FACIT-F and F-2-MS scales showed appropriated psychometric properties to be used as fatigue measures in clinical and research settings, allowing a correct distinction between patients with and without fatigue, and contributing to the understanding of the complexities of fatigue in MS.