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Growth Modeling of the Normal Human Fetal Left Ventricle and a Patient-Specific Case Study of Hypoplastic Left Heart Syndrome


Congenital heart defects such as hypoplastic left heart syndrome (HLHS) develop during gestation due to altered biomechanical stimuli during fetal growth. Currently, predicting growth behavior in hypoplastic hearts using mid gestational fetal echocardiography is a clinical challenge. In order to more accurately predict and optimize the outcomes of congenital heart defects on individual patients, first a comprehensive understanding of normal fetal growth and its sensitivity to various biomechanical stimuli is necessary. Computational models based on realistic in-vivo geometry contribute significantly to the understanding of cardiac physiology and mechanics. Though structural and functional development of the human heart is well understood, there are limited computational models of this process, specifically at the fetal stage. Therefore, there is a growing need for a robust computational model of the normal human fetal heart based on clinical measurements that can predict organ-level growth and can be used as a benchmark to compare against disease models. A novel three-dimensional finite element (FE) model of the human fetal left ventricle (LV) was developed using human fetal geometry at 22 weeks gestation. The model, in which cardiac myocyte growth rates as a function of end-diastolic strain, which correlates with ventricular filling, can predict organ-level growth. Predictions from the model were validated with LV echocardiographic dimensions from 22 to 40 weeks. An extreme sensitivity analysis was conducted to study the effects of size, shape, preload, ventricular filling, and material properties on fetal LV growth. The model provides insight into the parameters that growth is most sensitive to, in which growth is quantified as changes in LV cavity volume, wall volume, cavity shape, and wall thickness from mid gestation to birth. This is extremely useful when prioritizing patient-specific model parameters and improving the predictive capability of the model. In addition, a retrospective case study for a severe HLHS patient was conducted using mid-gestation echocardiographic data. The model predicted a severely hypoplastic LV consistent with the patient’s diagnosis and replicated LV short-axis and long-axis dimensions from late-gestation data. The work presented in this study is a step towards the development of a clinical tool that may be used to predict LV size and shape at birth based on mid-gestation data.

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