Novel Estimation of Left Ventricular Filling Pressure Using 3-D Speckle-Tracking Echocardiography: Assessment in a Decompensated Systolic Heart Failure Model

Takeda, Saori; Asanuma, Toshihiko; Masuda, Kasumi; Nakatani, Satoshi

doi:10.1016/j.ultrasmedbio.2021.02.008

Cited by 2 publications

(4 citation statements)

References 31 publications

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“…Thus, clinical PV loops data to validate the model are needed in the future. Besides, LVP diastolic reveals a clinically significant difference between non-HF and HF, 44,9 thus it is included in the classification of HF in the following research.…”

Section: Limitationsmentioning

confidence: 99%

“…Thus, clinical PV loops data to validate the model are needed in the future. Besides,

LV{P}_{diastolic}

reveals a clinically significant difference between non‐HF and HF, 44,9 thus it is included in the classification of HF in the following research. We extracted 2D‐A4C views of all the data and estimated the volume since the volume could not be calculated from a single plane.…”

Section: Limitationsmentioning

confidence: 99%

“…PV loops contain a considerable amount of information about cardiac performance and its analysis can clarify pathophysiological mechanisms of HF, including HFpEF, myocardial and valvular heart diseases. [9][10][11] VA coupling, mechanical potential energy (PE), ventricular efficiency (𝜂) and other parameters obtained from PV loops can be used to evaluate cardiac function and provide guidance for clinical diagnosis. 12,13 Although a lot of literature has studied the relationship between PV loops and HF, it does not specifically quantify the importance of PV loops parameters to the classification of HF, that is give the importance ranking of the parameters.…”

Section: Introductionmentioning

confidence: 99%

“…Hence, PV loops have been the gold standard for assessing direct, real‐time heart function that may be characterized by ventricular changes in pressure and volume. PV loops contain a considerable amount of information about cardiac performance and its analysis can clarify pathophysiological mechanisms of HF, including HFpEF, myocardial and valvular heart diseases 9–11 . VA coupling, mechanical potential energy (PE), ventricular efficiency (η) and other parameters obtained from PV loops can be used to evaluate cardiac function and provide guidance for clinical diagnosis 12,13 .…”

Section: Introductionmentioning

confidence: 99%

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Heart failure classifications via non‐invasive pressure volume loops from echocardiography

Liu,

Chen,

Xia

et al. 2023

Echocardiography

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BackgroundLeft ventricular pressure‐volume (LV‐PV) loops provide comprehensive characterization of cardiovascular system in both health and disease, which are the essential element of the hemodynamic evaluation of heart failure (HF). This study attempts to achieve more detailed HF classifications by non‐invasive LV‐PV loops from echocardiography and analyzes contribution of parameters to HF classifications.MethodsFirstly, non‐invasive PV loops are established by time‐varying elastance model where LV volume curves were extracted from apical‐four‐chambers view of echocardiographic videos. Then, 16 parameters related to cardiac structure and functions are automatically acquired from PV loops. Next, we applied six machine learning (ML) methods to divide four categories. On this premise, we choose the best performing classifier among machine learning approaches for feature ranking. Finally, we compare the contributions of different parameters to HF classifications.ResultsBy the experimental, the PV loops were successfully acquired in 1076 cases. When single left ventricular ejection fraction (LVEF) is used for HF classifications, the accuracy of the model is 91.67%. When added parameters extracted from ML‐derived LV‐PV loops, the classification accuracy is 96.57%, which improved by 5.1%. Especially, our parameters have a great improvement in the classification of non‐HF controls and heart failure with preserved ejection fraction (HFpEF).ConclusionsWe successfully presented the classification of HF by machine derived non‐invasive LV‐PV loops, which has the potential to improve the diagnosis and management of heart failure in clinic. Moreover, ventriculo‐arterial (VA) coupling and ventricular efficiency were demonstrated important factors for ML‐based HF classification model besides LVEF.

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Section: Limitationsmentioning

confidence: 99%

“…Thus, clinical PV loops data to validate the model are needed in the future. Besides,