Key result
MLP machine learning model accurately reconstructs RV pressure waveforms from peak pressure alone.
Why the study?
Conventional echocardiographic parameters of RV function are afterload-dependent, so incorporating RV pressures may enable new parameters that accurately reflect intrinsic RV function.
Does an artificial intelligence-based method using only peak right ventricular pressure accurately reconstruct the continuous right ventricular pressure curve?
Cross-Sectional (n=46)
Yes
Does an artificial intelligence-based method using only peak right ventricular pressure accurately reconstruct the continuous right ventricular pressure curve?
Answers are grounded in this paper and cite their sources.
A novel machine learning model accurately reconstructs the entire right ventricular pressure curve using only the peak systolic pressure, offering a foundation for non-invasive, afterload-adjusted echocardiographic assessment of RV function.
Aims Conventional echocardiographic parameters of right ventricular (RV) function are afterload-dependent. Therefore, incorporating RV pressures may enable the formulation of new parameters that reflect intrinsic RV function accurately. Accordingly, we sought to develop an artificial intelligence–based method to reconstruct the RV pressure curve based on the peak RV pressure. Methods and Results We invasively acquired RV pressure in 29 heart failure patients before and after implanting a left ventricular (LV) assist device. Using these tracings, we trained various machine learning models to reconstruct the RV pressure curve of the entire cardiac cycle based on the peak value of the curve. The best-performing model was compared with two other methods that estimated RV pressures based on a reference LV and RV pressure curve, respectively. Seventeen consecutive patients from another centre who underwent right heart catheterization and simultaneous echocardiography served as an external validation cohort. Among the evaluated algorithms, multilayer perceptron (MLP) achieved the best performance with an R2 of 0.887 (0.834–0.941). The RV and LV reference curve–based methods achieved R2 values of 0.879 (0.815–0.943) and 0.636 (0.500–0.771), respectively. During external validation, MLP exhibited similarly good performance [R2 0.911 (0.873–0.948)], which decreased only modestly if the echocardiography-derived peak RV pressure was used instead of the invasively measured peak RV pressure [R2 0.802 (0.694–0.909)]. Conclusions The proposed method enables the reconstruction of the RV pressure curve using only the peak value as input. Thus, it may serve as a fundamental component for developing new echocardiographic tools targeting the afterload-adjusted assessment of RV function.
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Szijártó et al. (2024) conducted a cross-sectional in Heart Failure (n=46). Multilayer perceptron (MLP) machine learning model vs. RV and LV reference pressure curve-based estimations was evaluated on Coefficient of determination (R2) for reconstruction of the right ventricular pressure curve during internal validation (R2 0.887, 95% CI 0.834-0.941). The multilayer perceptron machine learning model accurately reconstructed the right ventricular pressure curve using only the peak pressure value, achieving an R2 of 0.887 internally and 0.911 externally.
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