Generalized Net Model for Monitoring the Degree of Disability in Patients With Multiple Sclerosis Based on Neurophysiologic Criteria

Abstract

Multiple sclerosis (MS) is a chronic disease of the central nervous system, which affects most often the young age. The disease has a social importance, as it leads to disability in active age. The course of the disease is individual and requires an adequate approach in selecting the appropriate treatment strategy. This requires periodic monitoring of the patients for early registration of the disease progression. Most commonly used tests are clinical investigation, brain MRI and other paraclinical methods, including neurophysiological assessment (usually evoked potentials). Evoked potential (EP) is a reliable method for quantifying the severity of damage to the white matter in patients with MS. The aim of this study is to develop a model for Generalized NET (GN)-registration of the direction of the course of disease based on neurophysiological evaluation of multimodal evoked potentials. Also, to make a comparison of the results obtained by the EP with the degree of disability as measured by the scale of Kurtzke (Expanded disability Status Scale-EDSS). We have followed up 48 patients with clinically definite MS over a period of 1 year. The patients were tested both clinically and neurophysiologically at Clinic of Neurology in MHAT- NHH, Sofia. The three main modalities evoked potentials were applied: visual evoked potentials with reversive pattern (VEPRP); Brainstem auditory evoked potentials (BAEP); Somatosensory evoked potentials (SSEP), taken during stimulation of median nerve. As a result it is established that the abnormalities of EP correlate significantly with the clinical findings. Based on the obtained results is developed a GN-model generating candidate predictive rules for the progression of the illness. In the end it has been found that abnormalities of EP significantly correlated with clinical findings. Based on the obtained results a GN model was developed. The model has a high sensitivity (SEN), specificity (SPE), PPV (Positive predictive value) and NPV (Negative predictive value) for the disease progression.