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During a 5-year of observation there were 260 relapses in 101 MS patients. The number of relapses showed a significantly negative correlation with the number of days with NSR < 2 (p = -0.31; p < 0.01) and a positive correlation with the mean whole daily cloudiness (p < 0.05), mean daily cloudiness at 7 a.m. (p < 0.05) and 2 p.m. (p < 0.01). We found a significantlly positive correlation (p < 0.05) between the reduced number of relapses during the period of high vitamin D season, i.e. July-October. There was a statistically significant increase (p < 0.01) of the number of relapses during spring (x = 6.53; SD = 3.98) compared to the other three seasons. The joint presence of lower number of days with NSR < 2 during low vitamin D season (January- April) correlated with a statistically significant increase of the number of relapses in MS patients (F = 5.06, p < 0.01). CON- CLUSION: The obtained results confirmed the influence of air pollution and climate seasonal conditions on disease relapses in MS patients based on a long-term observation. Lower numbers of days with low air pollution during the periods with low vitamin D (January-April), especially with increased cloudiness at 2 p.m, induce a higher risk of MS relapses in southern continental parts of Europe.

Numerous outcome prediction models have been developed for mortality and functional outcome after spontaneous intracerebral haemorrhage (ICH). However, no outcome prediction model for ICH has considered the impact of care restriction. To develop and compare results of the artificial neural networks (ANN) and logistic regression (LR) models, based on initial clinical parameters, for prediction of mortality after spontaneous ICH. Analysis has been conducted on consecutive dataset of patients with spontaneous ICH, over 5-year period in tertiary care academic hospital. Patients older than 18 years were eligible for inclusion if they had been presented within 6 h from the start of symptoms and had evidence of spontaneous supratentorial ICH on initial brain computed tomography within 24 h. Initial clinical parameters have been used to develop LR and ANN prediction models for hospital mortality as outcome measure. Models have been accessed for discrimination and calibration abilities. We have analyzed 411 patients (199 males and 212 females) with spontaneous ICH, medically treated and not withdrawn from therapy, with average age of 67.35 years. From them, 256 (62.29%) patients died during hospital treatment and 155 (37.71%) patients survived. In the observed dataset, ANN model overall correctly classified outcome in 93.55% of patients, compared with 79.32% of correct classification for the LR model. Discrimination and calibration parameters indicate that both models show an adequate fit of expected and observed values, with superiority of ANN model. Our results favour the ANN model for prediction of mortality after spontaneous ICH. Further studies of the strengths and limitations of this method are needed with larger prospective samples.