Material stiffness parameters as potential predictors of presence of left ventricle myocardial infarction: 3D echo-based computational modeling study. Issue 1 (December 2016)

• Record Type: Journal Article
• Authors: Fan, Longling; Yao, Jing; Yang, Chun; Wu, Zheyang; Xu, Di; Tang, Dalin
• Is Part Of: Biomedical engineering online. Volume 15:Issue 1(2016)
• Page Start: 1
• Page End: 16
• Publication Date: 2016-12
• Subjects: Ventricle material property -- Prediction -- Ventricle model -- Ventricle mechanics -- Left ventricle; Biomedical engineering -- Periodicals; 610.2805
• DOI: 10.1186/s12938-016-0151-8
• Languages: English
• ISSNs: 1475-925X

Abstract

Abstract Background Ventricle material properties are difficult to obtain under in vivo conditions and are not readily available in the current literature. It is also desirable to have an initial determination if a patient had an infarction based on echo data before more expensive examinations are recommended. A noninvasive echo-based modeling approach and a predictive method were introduced to determine left ventricle material parameters and differentiate patients with recent myocardial infarction (MI) from those without. Methods Echo data were obtained from 10 patients, 5 with MI (Infarct Group) and 5 without (Non-Infarcted Group). Echo-based patient-specific computational left ventricle (LV) models were constructed to quantify LV material properties. All patients were treated equally in the modeling process without using MI information. Systolic and diastolic material parameter values in the Mooney-Rivlin models were adjusted to match echo volume data. The equivalent Young's modulus (YM) values were obtained for each material stress–strain curve by linear fitting for easy comparison. Predictive logistic regression analysis was used to identify the best parameters for infract prediction. Results The LV end-systole material stiffness (ES-YMf ) was the best single predictor among the 12 individual parameters with an area under the receiver operating characteristic (ROC) curve of 0.9841. LV wall thickness (WT), material stiffness in fiber direction at end-systole (ES-YMf ) and material stiffness variation (∆YMf ) had positive correlations with LV ejection fraction with correlation coefficients r = 0.8125, 0.9495 and 0.9619, respectively. The best combination of parameters WT + ∆YMf was the best over-all predictor with an area under the ROC curve of 0.9951. Conclusion Computational modeling and material stiffness parameters may be used as a potential tool to suggest if a patient had infarction based on echo data. Large-scale clinical studies are needed to validate these preliminary findings.