BackgroundCalcific aortic valve disease (CAVD) is often undiagnosed in asymptomatic patients, especially in underserved populations. Although artificial intelligence has improved murmur detection in auscultation exams, murmur manifestation depends on hemodynamic factors that can be independent of aortic valve (AoV) calcium load and function. The aim of this study was to determine if the presence of AoV calcification directly influences the S2 heart sound.MethodsAdult C57BL/6J mice were assigned to the following 12-week-long diets: (1) Control group (n = 11) fed a normal chow, (2) Adenine group (n = 4) fed an adenine-supplemented diet to induce chronic kidney disease (CKD), and (3) Adenine + HP (n = 9) group fed the CKD diet for 6 weeks, then supplemented with high phosphate (HP) for another 6 weeks to induce AoV calcification. Phonocardiograms, echocardiogram-based valvular function, and AoV calcification were assessed at endpoint.ResultsMice on the Adenine + HP diet had detectable AoV calcification (9.28 ± 0.74% by volume). After segmentation and dimensionality reduction, S2 sounds were labeled based on the presence of disease: Healthy, CKD, or CKD + CAVD. The dataset (2,516 S2 sounds) was split subject-wise, and an ensemble learning-based algorithm was developed to classify S2 sound features. For external validation, the areas under the receiver operating characteristic curve of the algorithm to classify mice were 0.9940 for Healthy, 0.9717 for CKD, and 0.9593 for CKD + CAVD. The algorithm had a low misclassification performance of testing set S2 sounds (1.27% false positive, 1.99% false negative).ConclusionOur ensemble learning-based algorithm demonstrated the feasibility of using the S2 sound to detect the presence of AoV calcification. The S2 sound can be used as a marker to identify AoV calcification independent of hemodynamic changes observed in echocardiography.

Understanding aortic valve (AV) mechanics is crucial in elucidating both the mechanisms that drive the manifestation of valvular diseases as well as the development of treatment modalities that target these processes. Genetically modified mouse models have provided mechanistic insight into AV development and disease. However, very little is known about mouse aortic valve leaflet (MAVL) tensile properties due to their microscopic size (~500µm long and 45µm thick) and the lack of proper mechanical testing modalities to assess uniaxial and biaxial tensile properties of the tissue. We developed a method in which the biaxial tensile properties of MAVL tissues can be assessed by adhering the tissues to a silicone rubber membrane utilizing dopamine as an adhesive. Applying equiaxial tensile loads on the tissue-membrane composite and tracking the engineering strains on the surface of the tissue resulted in the characteristic orthotropic response of AV tissues seen in human and porcine tissues. Our data suggests that the circumferential direction is approximately 155 kPa stiffer than the radial direction (n=6, P=0.0006) in MAVL tissues. This method can be implemented in future studies involving longitudinal mechanical stimulation of genetically modified MAVL tissues bridging the gap between cellular and biomolecular mechanisms and valve mechanics in popular mouse models of valve disease.

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Introduction: Heart valve diseases (HVDs) often remain undiagnosed until late stages, especially in underserved populations, leading to significant comorbidities. Artificial intelligence has improved murmur detection in digital phonocardiograms (PCG). However, the presence of murmurs depends on hemodynamic factors unrelated to HVD. Screening tests that directly measure features of valvular remodeling regardless of symptoms would improve HVD diagnosis. We used a mouse model of chronic kidney disease (CKD) induced aortic valve (AoV) remodeling to determine whether S2 sound changes can be used to detect early stages of AoV calcification. Methods: Eight-week old C57BL/6J mice (n=24) were assigned the following diets for 12 weeks: 1) control group fed a normal chow diet, 2) CKD group fed an adenine-supplemented diet to induce CKD, and 3) CKD+HP group fed the CKD diet for 6 weeks, then an adenine and high phosphate (HP)-supplemented diet for another 6 weeks to induce AoV calcification. PCG signals were recorded every 6 weeks. Endpoint AoV calcification and echocardiogram measures of valvular function were assessed. We developed a clustering method based on time domain principal component analysis of S2 sounds collected at week 6 (W6) to classify week 12 (W12) S2 sounds and identify S2 sound alterations due to HP. Results: The high phosphate diet induced AoV calcification in CKD+HP mice (1a). Of the total number of S2 sounds per mouse, an average of 84.18% and 81.15% S2 sounds (1b and 1c) were accurately classified as control or CKD mice with a specificity and sensitivity of 90.91% and 100%, respectively. CKD+HP mice had an average of 59.22% W12 S2 sounds that neither classified as control nor CKD. Conclusions: Mice that have AoV calcification show distinct S2 sounds features that were absent in the control and CKD groups. In this study, we demonstrate the feasibility of using the S2 sound not only to detect AoV calcification prior to expected hemodynamic changes but also to detect CKD.