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EVALUATION OF ANTIHYPERTENSIVE TREATMENT EFFICACY USING THE PREDICTIVE MARKOV MODEL. Liana Suciu 1 , Carmen Cristescu 1 , Lucreția Udrescu 3 , Mirela Voicu 1 , Lenuța -Maria Șuta 4 , M. Udrescu 5 , Valentina Buda 1 , Mirela Tomescu 2

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  1. EVALUATION OF ANTIHYPERTENSIVE TREATMENT EFFICACY USING THE PREDICTIVE MARKOV MODEL Liana Suciu1, Carmen Cristescu1, Lucreția Udrescu3, Mirela Voicu1, Lenuța-Maria Șuta4, M. Udrescu5, Valentina Buda1, Mirela Tomescu2 1Universitatea de MedicinășiFarmacie “Victor Babeș” Timișoara, Farmacologie-FarmacieClinică, Facultatea de Farmacie, Timișoara, Romania2Universitatea de MedicinășiFarmacie “Victor Babeș” Timișoara, SemiologieMedicală II, Facultatea de Medicină, Timișoara, Romania3Universitatea de MedicinășiFarmacie “Victor Babeș” Timișoara, ControlulMedicamentului, Facultatea de Farmacie, Timișoara, Romania4Universitatea de MedicinășiFarmacie “Victor Babeș” Timișoara, TehnologieFarmaceutică, BiofarmacieșiFarmacinetică, FacultateaFarmacie, Timișoara, Romania5Universitatea Politehnica, Calculatoare, Timișoara, Romania Background Cardiovascular diseases represent the main cause of premature death in industrialized countries and their prevalence has been increased also in the developing countries. Although most patients with mild to moderatehypertension are asymptomatic, the quality of life is affected because of associated conditions and complexity of therapy. The symptoms of hypertension are vague. Common complaints as: headache, dizziness, tiredness are not considered serious symptoms by the patients, but they may impair patient’s daily life. Moreover, end-organ damage induced by hypertension complicates the course of the disease, causing more symptoms and deterioration of patient’s life. The aim of the study was to determine, by applying Markov mathematical prediction model, the probability of hypertensive patients, after the treatment, to normalize their blood pressure values and how the disease can affect their life expectancy. Methods: 289 patients, diagnosed with essential arterial hypertension, were evaluated at the Cardiology Clinic “Ascar” Timișoara, from June 2012 until May 2013. According to the type of antihypertensive medicine administrated, they were divided into three groups: group A (106 patients) treated with nebivolol, group B (104 patients) with perindopril and group C (79 patients) with candesartancilexetil. Systolic and diastolic pressure values were evaluated at baseline, 6 and 12 months of treatment. Results Table 1. Demographic and clinical data of the study group (n=289) Figure 1. The evolution of blood pressure values for all study patients evaluated through “network analysis” B A D C Variation in systolic blood pressure (A, B) and diastolic (C, D) blood pressure of the study patients before and after treatment (green spots represent patients with normal blood pressure values and red spots represent patients with high pressure values). Considering these data and the incidence of hypertensive pathology in Romanian population, according to the Sephar I and II studies, after the application of Markov predictive method,  a life expectancy of 11.46 years, 12.04 years and 13.88 years was obtain for patients treated with nebivolol, perindopril and candesartancilexetil, respectively. After 12 months of treatment, 68.25% of hypertensive patients treated with candesartancilexetil, 52.04% of those treated with perindopril and 46.93% of those treated with nebivolol had normalised blood pressure values. • References • Barabási AL. Network medicine--from obesity to the "diseasome". N Engl J Med. 2007 26;357; • Bastian M., Heymann S. Gephi: An Open Source Software for Exploring and Manipulating Networks; Association for the Advancement of Artificial Intelligence (www.aaai.org). 2009; • Kumar MM, Kannan KS. Markov model for acute hypertension analysis. J Comp and Math Sci. 2011; 2(2):296-302; Conclusion The present study demonstrates that by applying Markov model, hypertensive patients treated with candesartancilexetilpresent the longest survival period. This mathematical method of prediction may provide clues to the patients life expectancy, being useful in clinical practice but also in specific clinical studies.

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