Identification of patients affected by mitral valve prolapse with severe regurgitation: A multivariable regression model

Abstract

Background. Mitral valve prolapse (MVP) is the most common cause of severe mitral regurgitation. Besides echocardiography, up to now there are no reliable biomarkers available for the identification of this pathology. We aim to generate a predictive model, based on circulating biomarkers, able to identify MVP patients with the highest accuracy. Methods. We analysed 43 patients who underwent mitral valve repair due to MVP and compared to 29 matched controls. We assessed the oxidative stress status measuring the oxidized and the reduced form of glutathione by liquid chromatography-tandem mass spectrometry method. Osteoprotegerin (OPG) plasma levels were measured by an enzyme-linked immunosorbent assay. The combination of these biochemical variables was used to implement several logistic regression models. Results. Oxidative stress levels and OPG concentrations were significantly higher in patients compared to control subjects (0.116±0.007 versus 0.053±0.013 and 1748±100.2 versus 1109±45.3 pg/mL, respectively; p<0.0001). The best regression model was able to correctly classify 62 samples out of 72 with accuracy in terms of area under the curve of 0.92. Conclusions. To the best of our knowledge, this is the first study to show a strong association between OPG and oxidative stress status in patients affected by MVP with severe regurgitation.