S2 Heart Sound Detects Aortic Valve Calcification Independent of Hemodynamic Changes in Mice.
S2 Heart Sound Detects Aortic Valve Calcification Independent of Hemodynamic Changes in Mice.
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DOI：
10.3389/fcvm.2022.809301
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发表时间：
2022
影响因子：
3.6
通讯作者：
中科院分区：
文献类型：
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作者：
钙化性主动脉瓣疾病（CAVD）通常在无症状患者中无法诊断，特别是在服务不足的人群中。虽然人工智能已经改善了听诊检查中的杂音检测，但杂音表现取决于血流动力学因素，而这些因素可能与主动脉瓣（AoV）钙负荷和功能无关。本研究的目的是确定AoV钙化的存在是否直接影响S2心音。将成年C57 BL/6 J小鼠分配至以下为期12周的饮食：（1）对照组（n = 11）喂食正常食物，（2）腺嘌呤组（n = 4）喂食补充腺嘌呤的饮食以诱导慢性肾病（CKD），和（3）腺嘌呤+ HP组（n = 9）喂食CKD饮食6周，然后再补充高磷酸盐（HP）6周以诱导AoV钙化。终点时评估心音图、基于超声心动图的瓣膜功能和AoV钙化。服用腺嘌呤+ HP饲料的小鼠具有可检测到的AoV钙化（9.28 ± 0.74%（体积））。分割和降维后，S2声音根据疾病的存在进行标记：健康、CKD或CKD + CAVD。将数据集（2，516个S2声音）按主题进行分割，并开发了一种基于集成学习的算法来对S2声音特征进行分类。对于外部验证，用于对小鼠进行分类的算法的受试者工作特征曲线下面积对于健康小鼠为0.9940，对于CKD为0.9717，对于CKD + CAVD为0.9593。该算法对测试集S2音具有较低的误分类性能（1.27%的假阳性，1.99%的假阴性）。我们的基于集成学习的算法证明了使用S2声音检测AoV钙化存在的可行性。S2音可作为一个标记，以识别主动脉瓣钙化，而不依赖于超声心动图中观察到的血流动力学变化。
Calcific 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. Adult 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. Mice 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). Our 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.
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DOI：
10.2967/jnumed.114.152355
发表时间：
2015-06
期刊：
Journal of nuclear medicine : official publication, Society of Nuclear Medicine
影响因子：
--
作者：
Jung JJ;Razavian M;Challa AA;Nie L;Golestani R;Zhang J;Ye Y;Russell KS;Robinson SP;Heistad DD;Sadeghi MM
通讯作者：
Sadeghi MM
DOI：
10.1115/1.4046921
发表时间：
2020-10-01
影响因子：
1.7
作者：
Chaparro, Daniel;Dargam, Valentina;Hutcheson, Joshua D.
通讯作者：
Hutcheson, Joshua D.
DOI：
10.1161/atvbaha.112.300794
发表时间：
2013-03
期刊：
Arteriosclerosis, thrombosis, and vascular biology
影响因子：
--
作者：
Chu Y;Lund DD;Weiss RM;Brooks RM;Doshi H;Hajj GP;Sigmund CD;Heistad DD
通讯作者：
Heistad DD
影响因子：
1.2
作者：
Casaclang-Verzosa, Grace;Enriquez-Sarano, Maurice;Miller, Jordan D.
通讯作者：
Miller, Jordan D.
影响因子：
3.3
作者：
Arlot, Sylvain;Celisse, Alain
通讯作者：
Celisse, Alain