Abstract
Diastolic dysfunction of the heart is present in most forms of cardiac failure. Left ventricular (LV) diastolic chamber stiffness has been proposed as a metric for obtaining insights into the progression of this disease and help to inform treatment decisions. However, the challenges in robustly estimating chamber stiffness have limited the evaluation of its prognostic value. This study aimed to develop an automated workflow that enables routine estimation of chamber stiffness from haemodynamic measurements and real-time 3D echocardiographic data to enable such investigations. The workflow was demonstrated on a cohort of 20 patients with heart failure (HF), 7 patients with aortic regurgitation (AR) without HF, and 12 control subjects. A mixed-effects linear regression model was used to examine the differences in diastolic chamber stiffness among the patient groups taking into account the beat-to-beat variations in chamber stiffness estimates. The variances of the standard deviation in chamber stiffness estimates for each patient groups were also evaluated to investigate the influence of beat-to-beat variations in LV pressure on diastolic chamber stiffness estimates. Overall, chamber stiffness was found to be significantly higher int the heart failure with preserved ejection fraction (HFpEF) group (2.4 ± 0.9 mmHg/mL, p = 0.02) and the heart failure with reduced ejection fraction (HFrEF) group (2.1 ± 1.7 mmHg/mL, p = 0.017) compared to the control group (1.1 ± 0.5 mmHg/mL). The lowest estimates were observed in the AR without heart failure group (1 ± 0.4 mmHg/mL, p = 0.84). HFrEF patient group exhibited the largest variance of the standard deviation in chamber stiffness estimates, followed by the HFpEF group, suggesting the beat-to-beat variations in LV pressures had a substantial effect in these groups. Future work will seek to apply this novel automated methodology to support estimation of chamber stiffness in a robust and reproducible manner in larger clinical studies to further elucidate its benefits for patient diagnosis and management.
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Acknowledgements
We gratefully acknowledge the financial support from the Health Research Council of New Zealand (17/608). We also acknowledge the important roles of our research nurses Mariska Oakester Bals, Jane Hannah, Anna Taylor, and Gracie Hoskin for their invaluable assistance in participant recruitment and data collection.
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Hasaballa, A.I. et al. (2023). Automated Estimation of Left Ventricular Diastolic Chamber Stiffness: Application to Patients with Heart Failure and Aortic Regurgitation. In: Bernard, O., Clarysse, P., Duchateau, N., Ohayon, J., Viallon, M. (eds) Functional Imaging and Modeling of the Heart. FIMH 2023. Lecture Notes in Computer Science, vol 13958. Springer, Cham. https://doi.org/10.1007/978-3-031-35302-4_64
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DOI: https://doi.org/10.1007/978-3-031-35302-4_64
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