Prognosis of essential hypertension progression in patients with abdominal obesity
Fragment książki (Rozdział monografii pokonferencyjnej)
MNiSW
20
Poziom I
| Status: | |
| Autorzy: | Koval Sergiy M., Snihurska I. O., Vysotska Olena V., Strashnenko H. M., Wójcik Waldemar, Dassibekov Khassen |
| Dyscypliny: | |
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| Wersja dokumentu: | Drukowana | Elektroniczna |
| Arkusze wydawnicze: | 0,8 |
| Język: | angielski |
| Strony: | 275 - 288 |
| Scopus® Cytowania: | 6 |
| Bazy: | Scopus |
| Efekt badań statutowych | NIE |
| Materiał konferencyjny: | NIE |
| Publikacja OA: | NIE |
| Abstrakty: | angielski |
| Essential hypertension combined with Abdominal Obesity (AO) is characterised by a particularly high risk of development of severe cardiovascular complications, which substantiates the relevance of development of informative methods for predicting hypertension progression in patients with abdominal obesity. The study involved 126 patients with grade 2-3 hypertension combined with grade I-II abdominal obesity (80 men and 46 women), mean age (63 ± 4.6) years. The examination included general clinical, laboratory, instrumental methods for diagnosis of essential hypertension and abdominal obesity, their degrees, risk factors, target organ damage, symptomatic cardiovascular diseases, kidney damage, and general cardiovascular risk according to European guidelines (2013 ESH/ESC Guidelines for the management of arterial hypertension). The method of logistic regression was used to detect predictors of essential hypertension progress in patients with abdominal obesity. To investigate the quality of the synthesised mathematical model, ROC analysis was performed with the use of application software SPSS 19.0. As a result of the work, five most significant predictors of essential hypertension progression from grade 2 to 3 were revealed: angiopoietin-2 blood level, Intima-Media Thickness (IMT) of Common Carotid Arteries (CCA), Total Cholesterol (TC) level and Body Mass Index (BMI) and Left Ventricular Mass (LVM) index. The high degree of adequacy of the created model with real data was demonstrated, high sensitivity (92.1%) and specificity (84%) of the test and high diagnostic (pre- diction) value of the developed model were proved. |
