Usefulness of Two-Dimensional Strain Echocardiography to Predict Segmental Viability Following Acute Myocardial Infarction and Optimization Using Bayesian Logistic Spatial Modeling

摘要

Viability assessment following acute myocardial infarction (MI) is important to guide revascularization. Two-dimensional strain echocardiography (2DSE) was shown to predict viability but the methodology assumed strain in each segment is independent of contiguous segments. We tested the hypotheses that segmental strain post-MI are spatially correlated and that using Bayesian approach improves prediction of non-viable myocardium. 21 subjects (58±12 years, 6 females) with ≥2 weeks MI underwent 2DSE and late gadolinium enhancement (LGE) cardiac magnetic resonance imaging within 48-hours of each other. The heart was divided into 16 segments and longitudinal, radial and circumferential strains were measured using software. Using similar segmentation, LGE was measured and segments with >50% LGE were considered nonviable. Spearman analyses assessed spatial correlation of strain and receiver operating characteristic curve analysis was used to determine prediction of non-viable myocardium without and with Bayesian logistic spatial conditionally autoregressive (CAR) model. There is significant spatial correlation in strain and LGE, especially in the apex. Longitudinal strain was the best predictor of non-viability and was impaired in non-viable myocardium (-12.1±0.6, -8.0±0.6 and -4.6±1% for 0, 1-50, >50% LGE, respectively, p<0.001). Use of CAR model improved the area under the curve for detection of non-viable myocardium (0.7 to 0.94). A CAR probabilistic score of 0.17 had 88% sensitivity and 86% specificity for detecting non-viable myocardium. In conclusion, longitudinal strain from 2DSE can predict myocardial viability following MI and exploiting spatial correlations in segmental strain using Bayesian CAR enhances the ability of 2D strain to predict non-viable myocardium.