2 resultados para Structural-Parametrical Optimization


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Background. A software based tool has been developed (Optem) to allow automatize the recommendations of the Canadian Multiple Sclerosis Working Group for optimizing MS treatment in order to avoid subjective interpretation. METHODS: Treatment Optimization Recommendations (TORs) were applied to our database of patients treated with IFN beta1a IM. Patient data were assessed during year 1 for disease activity, and patients were assigned to 2 groups according to TOR: "change treatment" (CH) and "no change treatment" (NCH). These assessments were then compared to observed clinical outcomes for disease activity over the following years. RESULTS: We have data on 55 patients. The "change treatment" status was assigned to 22 patients, and "no change treatment" to 33 patients. The estimated sensitivity and specificity according to last visit status were 73.9% and 84.4%. During the following years, the Relapse Rate was always higher in the "change treatment" group than in the "no change treatment" group (5 y; CH: 0.7, NCH: 0.07; p < 0.001, 12 m - last visit; CH: 0.536, NCH: 0.34). We obtained the same results with the EDSS (4 y; CH: 3.53, NCH: 2.55, annual progression rate in 12 m - last visit; CH: 0.29, NCH: 0.13). CONCLUSION: Applying TOR at the first year of therapy allowed accurate prediction of continued disease activity in relapses and disability progression.

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BACKGROUND Type 2 diabetes mellitus (T2DM) is an emerging risk factor for cognitive impairment. Whether this impairment is a direct effect of this metabolic disorder on brain function, a consequence of vascular disease, or both, remains unknown. Structural and functional neuroimaging studies in patients with T2DM could help to elucidate this question. OBJECTIVE We designed a cross-sectional study comparing 25 T2DM patients with 25 age- and gender-matched healthy control participants. Clinical information, APOE genotype, lipid and glucose analysis, structural cerebral magnetic resonance imaging including voxel-based morphometry, and F-18 fluorodeoxyglucose positron emission tomography were obtained in all subjects. METHODS Gray matter densities and metabolic differences between groups were analyzed using statistical parametric mapping. In addition to comparing the neuroimaging profiles of both groups, we correlated neuroimaging findings with HbA1c levels, duration of T2DM, and insulin resistance measurement (HOMA-IR) in the diabetic patients group. Results: Patients with T2DM presented reduced gray matter densities and reduced cerebral glucose metabolism in several fronto-temporal brain regions after controlling for various vascular risk factors. Furthermore, within the T2DM group, longer disease duration, and higher HbA1c levels and HOMA-IR were associated with lower gray matter density and reduced cerebral glucose metabolism in fronto-temporal regions. CONCLUSION In agreement with previous reports, our findings indicate that T2DM leads to structural and metabolic abnormalities in fronto-temporal areas. Furthermore, they suggest that these abnormalities are not entirely explained by the role of T2DM as a cardiovascular risk factor.