Right Ventricular and Pulmonary Artery Evaluation by CMR

Pulmonary Hypertension

Pulmonary hypertension results in right ventricle dysfunction. Cardiac magnetic resonance derived RV ejection fraction is a prognostic value in this condition. Right ventricular geometry, function, morphology as well as pulmonary arterial stiffness and size may be evaluated by cardiac magnetic resonance imaging and could have great importance in evaluation of pulmonary hypertension prognosis and outcomes.

Pulmonary hypertension is defined as an increase in mean pulmonary artery pressure \> 25 mmHg evaluated by right heart catheterisation. Over time increased pulmonary artery pressure eventually causes vascular remodelling and right ventricular dysfunction. The right and left ventricles interact sharing a common pericardial sac and interventricular septum. Frequently right ventricle dysfunction reflects clinical PH worsening. The aim of this study was to assess the geometry, function and morphology of right ventricle and also pulmonary artery diameter and stiffness in pulmonary hypertension patients by cardiovascular magnetic resonance. Patients are recruited from the Hospital of Lithuanian University of Health Sciences Kaunas clinics Pulmonary hypertension center. Study inclusion criteria: 1. Patient with diagnosed pulmonary hypertension 2. RHC, 6MWT, echocardiography, NT-pro BNP, CMR performed in one month period. 3. Patients who signed study informed consent form. Study exclusion criteria: 1. Coronary and valvular heart disease 2. Atrial fibrillation 3. Expressed shortness of breath 4. Claustrophobia. Suspected number of participants - 100. Data Analysis Statistical analyses will be performed using SPSS 22.0 package (SPSS, Chicago, IL, USA). Continuous variables will be expressed as mean +/- standard deviation, skewed variables as median (interquartile range). Chi square test will be used for qualitative parameters. For continuous variables, differences between two groups will be compared using non-parametric Mann-Whitney U test. Relation between variables will be assessed using Spearman correlation coefficient. Univariate analysis of predictors for survival will be used. In order to assess minimally false negative and minimally false positive results with greatest accuracy, the method of ROC (Receiver Operating Characteristics) curve will be used. The optimal values will be separated out the different groups (survival/non-survival) with the highest accuracy (minimal false negative and false positive). Survival curves will be established by the Kaplan-Meier estimation method. Cox regression analysis will be used to identify independent predictor of outcomes. Two-tailed probability values at p\<0,05 will be considered statistically significant.

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