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Model predicting the onset of antiepileptic drug resistance in women of reproductive age with epilepsy: analytical study

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dc.contributor.author Duca, Victoria
dc.contributor.author Gavriliuc, Mihail
dc.date.accessioned 2024-03-18T08:35:11Z
dc.date.available 2024-03-18T08:35:11Z
dc.date.issued 2023
dc.identifier.citation DUCA, Victoria, GAVRILIUC, Mihail. Model predicting the onset of antiepileptic drug resistance in women of reproductive age with epilepsy: analytical study. In: Revista de Științe ale Sănătății din Moldova = Moldovan Journal of Health Sciences. 2023, nr. 4(10), pp. 11-18. ISSN 2345-1467. DOI: https://doi.org/10.52645/MJHS.2023.4.02 en_US
dc.identifier.issn 2345-1467
dc.identifier.uri https://cercetare.usmf.md/sites/default/files/inline-files/Victoria%20Duca%2C%20Mihail%20Gavriliuc%20Model%20predicting%20the%20onset%20of%20antiepileptic%20drug%20resistance%20in%20women%20of%20reproductive%20age%20with%20epilepsy%20analytical%20study.pdf
dc.identifier.uri https://doi.org/10.52645/MJHS.2023.4.02
dc.identifier.uri http://repository.usmf.md/handle/20.500.12710/26920
dc.description.abstract Introduction. In attempt to find an answer regarding the possible scenarios of epilepsy evolution in women of reproductive age (e.g. worsening, remission, antiepileptic drug resistance, status epilepticus occurence), preferably - objective, based on simple, replicable, observable indicators that can be included in a mathematical probability estimation model, could significantly improve their quality of life and increase the effectiveness of prescribed treatments. Materials and methods. Bidirectional, cohort, descriptive-analytical study, conducted between 2016-2020. Primary data were collected in the Diomid Gherman Institute of Neurology and Neurosurgery, the State Hospital of Republic of Moldova and the Excellence Private Medical Institution. Out of 366 unique parameters, which were recorded in the 159 patients enrolled in the study at each visit (total, 4 documentation visits over 5 years period), 10 parameters were selected for multivariate analysis, considered relevant for predicting clinically significant outcomes. Criteria for parameter relevance were: reaching p≤0.1 in univariate analysis, easy documentation. Subsequently, testing for multicollinearity (calculation of variance inflation factor) and the contribution of each parameter in the formula was performed using the Akaike informativeness criteria. The performance of the developed predictive models was expressed by the area under the ROC curve, positive and negative prognostic power. Statistical analysis: GraphPad Prism, v. 9 trial (Graph Pad Software, Boston, USA). Results. Age at onset of the disease 14.0±6.3 years; age at first referral to specialist 24.0±7.2 years. The developed predictive model, based on 3 parameters (depressive state, annual frequency of seizures, presence of brain lesions on MRI) has a positive predictive value of 83%, negative of 62%, with an area under the ROC curve of 0.72 (95%CI = 0.56 to 0.88) and a probability of occurrence of 96%. Conclusions. Depressed patients with documented structural lesions on MRI and a high frequency of epileptic seizures have a progressive, significant risk (an OR of 5.3-24.0) of developing resistance to antiepileptic drugs. en_US
dc.language.iso en en_US
dc.publisher Instituţia Publică Universitatea de Stat de Medicină şi Farmacie „Nicolae Testemiţanu” din Republica Moldova en_US
dc.relation.ispartof Revista de Științe ale Sănătății din Moldova = Moldovan Journal of Health Sciences en_US
dc.subject resistance en_US
dc.subject antiepileptic drugs en_US
dc.subject epilepsy en_US
dc.subject women of reproductive age en_US
dc.subject predictive model en_US
dc.subject.ddc UDC: 616.853-085.213-055.2 en_US
dc.title Model predicting the onset of antiepileptic drug resistance in women of reproductive age with epilepsy: analytical study en_US
dc.type Article en_US


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