Hypertrophic cardiomyopathy (HCM) is a genetic heart disease that is associated with many pathological features, such as a reduction in global longitudinal strain (GLS), myofiber disarray and hypertrophy. The effects of these features on left ventricle (LV) function are, however, not clear in two phenotypes of HCM, namely, obstructive and non-obstructive. To address this issue, we developed patient-specific computational models of the LV using clinical measurements from 2 female HCM patients and a control subject. Left ventricular mechanics was described using an active stress formulation and myofiber disarray was described using a structural tensor in the constitutive models. Unloaded LV configuration for each subject was first determined from their respective end-diastole LV geometries segmented from the cardiac magnetic resonance images, and an empirical single-beat estimation of the end-diastolic pressure volume relationship. The LV was then connected to a closed-loop circulatory model and calibrated using the clinically measured LV pressure and volume waveforms, peak GLS and blood pressure. Without consideration of myofiber disarray, peak myofiber tension was found to be lowest in the obstructive HCM subject (60 kPa), followed by the non-obstructive subject (242 kPa) and the control subject (375 kPa). With increasing myofiber disarray, we found that peak tension has to increase in the HCM models to match the clinical measurements. In the obstructive HCM patient, however, peak tension was still depressed (cf. normal subject) at the largest degree of myofiber disarray found in the clinic. The computational modeling workflow proposed here can be used in future studies with more HCM patient data.
Hypertrophic cardiomyopathy (HCM) is a genetic heart disease that is associated with many pathological features, such as a reduction in global longitudinal strain (GLS), myofiber disarray and hypertrophy. The effects of these features on left ventricle (LV) function are, however, not clear in two phenotypes of HCM, namely, obstructive and non-obstructive. To address this issue, we developed patient specific computational models of the LV using clinical measurements of 2 female HCM patients and a control subject. Left ventricular mechanics was described using an active stress formulation and myofiber disarray was described using a structural tensor in the constitutive models. Unloaded LV configuration for each subject was first determined from their respective end-diastole LV geometries segmented from the cardiac magnetic resonance images, and an empirical single-beat estimation of the end-diastolic pressure volume relationship. The LV was then connected to a closed-loop circulatory model and calibrated using the clinically measured LV pressure and volume waveforms, peak GLS and blood pressure. Without consideration of myofiber disarray, peak myofiber tension was found to be lowest in the obstructive HCM subject (60 kPa), followed by the non-obstructive subject (242 kPa) and the control subject (375 kPa). With increasing myofiber disarray, we found that peak tension has to increase in the HCM models to match the clinical measurements. In the obstructive HCM patient, however, peak tension is still depressed (cf. normal subject) at the largest degree of myofiber disarray found in the clinic. The computational modeling workflow proposed here can be used in future studies with more HCM patient data.
Background: Little data exist regarding interreader variability of diastolic measurements and their application by the 2016 American Society of Echocardiography left ventricular (LV) diastolic function guidelines.Methods: Volunteers (n = 49) were recruited from an outpatient cardiology practice.The presence and grade of diastology dysfunction (DD) was determined by the 2016 LV diastology guideline algorithm. We determined the mean, standard deviation, coefficient of variation, and intraclass correlation coefficient (ICC) for each measurement and Fleiss K-statistic to define differences in grading DD. We determined predictors associated with disagreement of DD grade using odds ratios. Results:The mean LVEF was 56%, LAVI 32 ml/m 2 , and peak TR velocity was 2.3 m/s.The ICC for mitral inflow and tissue Doppler velocities were >.90, for LV volumes were .80-.86, and for LA volume was .56. The Fleiss K-value for the agreement of the presence of DD was .68 and for DD grade was .59. Variables with increased odds of disagreement were (1) at least one reader considering a TR signal uninterpretable (OR 12.0; 95% CI 1.3-109.6), (2) at least one reader assessing both LVEF 50%-55% and LAVI 29-39 ml/m 2 (OR 9.3; 95% CI 1.0-87), and (3) at least one reader assessing LVEF 50-55% (OR 3.8;). Conclusions:Using the 2016 ASE/EACVI diastology guidelines, we found excellent interrater reliability of Doppler measurements, moderate-good interrater reliability of volumetric measurements, and moderate-good but not excellent agreement for diastology grade.
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