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Assessment of aortic valve opening during rotary blood pump support using pump signals.

During left ventricular support by rotary blood pumps (RBPs), the biomechanics of the aortic valve (AV) are altered, potentially leading to adverse events like commissural fusion, valve insufficiency, or thrombus formation. To avoid these events, assessment of AV opening and consequent adaptation of pump speed seem important. Additionally, this information provides insight into the heart-pump interaction. The aim of this study was to develop a method to assess AV opening from the pump flow signal. Data from a numerical model of the cardiovascular system and animal experiments with an RBP were employed to detect the AV opening from the flow waveform under different hemodynamic conditions. Three features calculated from the pump flow waveform were used to classify the state of the AV: skewness, kurtosis, and crest factor. Three different classification algorithms were applied to determine the state of the AV based on these features. In the model data, the best classifier resulted in a percentage of correctly identified beats with a closed AV (specificity) of 99.9%. The percentage of correctly identified beats with an open AV (sensitivity) was 99.5%. In the animal experiments, specificity was 86.8% and sensitivity reached 96.5%. In conclusion, a method to detect AV opening independently from preload, afterload, heart rate, contractility, and degree of support was developed. This algorithm makes the evaluation of the state of the AV possible from pump data only, allowing pump speed adjustment for a frequent opening of the AV and providing information about the interaction of the native heart with the RBP.