Mitral leaflet separation to evaluate the severity of mitral stenosis: Validation of the index by transesophageal three‐dimensional echocardiography. Issue 3 (21st January 2018)

• Record Type: Journal Article
• Authors: Bigdelu, Leila; Poorzand, Hoorak; Azari, Ali; Jarahi, Lida; Ghaderi, Fereshteh; Fazlinejad, Afsoon; Alimi, Hedieh; Rohani, Atooshe; Manavifar, Negar
• Is Part Of: Echocardiography. Volume 35:Issue 3(2018)
• Page Start: 361
• Page End: 367
• Publication Date: 2018-01-21
• Subjects: mitral leaflet separation -- mitral stenosis -- multiplane echocardiography -- planimetry -- three‐dimensional -- validation; Echocardiography -- Periodicals; Echocardiography -- Periodicals; 616.1207543
• DOI: 10.1111/echo.13805
• Languages: English
• ISSNs: 0742-2822

Abstract

Abstract : Background: Determining severity of mitral stenosis (MS) by planimetry of mitral valve orifice area (MVA) has been a challenging issue in clinical practice, especially for less experienced cardiologists. Mitral leaflet separation (MLS) has shown a good correlation with MVA measurements. However, it has never been validated against multiplane 3DTEE planimetry (MVA3D ). We aimed to evaluate the accuracy of MLS index (MLSI2D ) in predicting MS severity. Methods: We prospectively enrolled 144 patients with MS who underwent clinically indicated 2DTTE and 3DTEE. MLSI2D was yield by averaging the maximal leaflet tip distance in diastole, in parasternal long‐axis and apical four‐chamber views. MVA3D was used as the reference method. Results: MLSI2D showed an excellent discriminatory ability between different grades of MS ( P < .001). There was a significant positive correlation between MLSI2D and MVA3D ( r = .93, P < .001) irrespective of concurrent mitral regurgitation ( r = .94, P < .001) and/or atrial fibrillation ( r = .92, P < .001). By receiver operating characteristic (ROC) curves, MLSI2D ≤ 8.6 mm showed 100% sensitivity and 76% specificity for very severe MS. MLSI2D ≥ 11.2 mm determined progressive MS with 100% sensitivity and 82% specificity. The study population was then divided into a derivation group and a validation group. A regression equation for MVA by MLSI2D was derived in first group. Then, the MVA was calculated by this equation in validation group and was not significantly different from MVA3D . Conclusion: MLSI2D showed an excellent ability to assess MS severity and correlates well with planimetered MVA measured by 3DTEE.