Quantitative prediction of improvement in cardiac function after revascularization with MR imaging and modeling: Initial results

Purpose: To evaluate a model that can be used quantitatively to predict cha nges in postrevascularization left ventricular function based on classifica tion of myocardial tissue as hibernating, scarred, or normal with cine magn etic resonance (MR) imaging. Materials and methods: Eleven patients with chronic left ventricular dysfun ction were studied before and after revascularization with cine MR imaging. Regional myocardial contractility and wall thickness were used in the mode l to predict postrevascularization ejection fraction (EF). The actual EF fr om the postrevascularization MR images was compared with the EF from the pr erevascularization images predicted with the model by using regression anal ysis and Bland-Altman analysis. Results: Correlation between the actual EF after revascularization and the EF predicted by using the model yielded an R value of 0.98, with a standard error of 1.3 EF percentage points. Predicting changes in function in a myo cardial segment was less successful because only 55% of segments classified as hibernating actually improved resting function after revascularization. In nonimproved segments, 78% were either adjacent to infarcted segments or had nontransmural wall thinning. Conclusion: A simple mathematical model combined with functional informatio n provided by MR imaging was used to predict improvements in global EF resu lting from revascularization.