A Regression Model to Predict Diameter of Mitral Valve Prosthesis Preoperatively: An Aid to Clinical Decision Making
Journal Title: Journal of Advances in Medicine and Medical Research - Year 2015, Vol 8, Issue 4
Abstract
Aim: Accurate estimation of the prosthetic valve size pre-operatively can aid to the efficiency and effectiveness to mitral valve surgery. Traditionally Two dimensional Echocardiography is being used for this purpose but cannot be claimed as optimum tool. This study computes and analyses several linear regression equations in order to obtain a best fit model for predicting mitral prosthesis size well before operation. Materials and Methods: This hospital based longitudinal study was conducted in a tertiary care Cardio Thoracic Vascular Department from August 2011 to August 2012. A total of 67 participants suffering from Rheumatic Heart Disease (with severe mitral valve disease) were recruited. Short and long axis of the mitral annulus were measured through 2-dimensional transthoracic echocardiography. Further three regression models were plots using short axis, long axis and area of the annulus as independent variables and diameter of prosthetic valve as dependent variable. Results: Among the three predictors namely the anterio-posterior axis (short axis), commissure-commissural axis (long axis) and area of mitral valve annulus; the regression equation with short axis predicted the diameter of prosthesis more accurately. The optimum regression model for short axis is calculated as -Diameter of prosthetic valve= -0.165 + 0.769X short axis of Ellipse. The coefficient of determination for this equation (R2) is 0.905. This equation offers the explanation for maximum observations in the model (F=609.48). Conclusion: The size of optimum prosthetic valve may be determined preoperatively as a function of anterio-posterior axis (short axis) of annulus through non invasive 2-D Echocardiography. However this finding is amenable for further multi-centric validation.
Authors and Affiliations
Praveen Sharma, Ankur Joshi, B. S. Yadav, Saket Kale, M. C. Songara
Keywords
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- EP ID EP347842
- DOI 10.9734/BJMMR/2015/16835
- Views 82
- Downloads 0
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