Tokodi Márton, Shah Rohan, Jamthikar Ankush, Craig Neil, Hamirani Yasmin, Casaclang-Verzosa Grace, Hahn Rebecca T, Dweck Marc R, Pibarot Philippe, Yanamala Naveena, Sengupta Partho P
Division of Cardiovascular Diseases and Hypertension, Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey, USA; Heart and Vascular Center, Semmelweis University, Budapest, Hungary.
Division of General Internal Medicine, Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey, USA.
JACC Cardiovasc Imaging. 2025 Feb;18(2):150-165. doi: 10.1016/j.jcmg.2024.07.017. Epub 2024 Sep 18.
The development and progression of aortic stenosis (AS) from aortic valve (AV) sclerosis is highly variable and difficult to predict.
The authors investigated whether a previously validated echocardiography-based deep learning (DL) model assessing diastolic dysfunction (DD) could identify the latent risk associated with the development and progression of AS.
The authors evaluated 898 participants with AV sclerosis from the ARIC (Atherosclerosis Risk In Communities) cohort study and associated the DL-predicted probability of DD with 2 endpoints: 1) the new diagnosis of AS; and 2) the composite of subsequent mortality or AV interventions. Validation was performed in 2 additional cohorts: 1) in 50 patients with mild-to-moderate AS undergoing cardiac magnetic resonance (CMR) imaging and serial echocardiographic assessments; and 2) in 18 patients with AV sclerosis undergoing F-sodium fluoride (NaF) and F-fluorodeoxyglucose positron emission tomography (PET) combined with computed tomography (CT) to assess valvular inflammation and calcification.
In the ARIC cohort, a higher DL-predicted probability of DD was associated with the development of AS (adjusted HR: 3.482 [95% CI: 2.061-5.884]; P < 0.001) and subsequent mortality or AV interventions (adjusted HR: 7.033 [95% CI: 3.036-16.290]; P < 0.001). The multivariable Cox model (incorporating the DL-predicted probability of DD) derived from the ARIC cohort efficiently predicted the progression of AS (C-index: 0.798 [95% CI: 0.648-0.948]) in the CMR cohort. Moreover, the predictions of this multivariable Cox model correlated positively with valvular F-NaF mean standardized uptake values in the PET/CT cohort (r = 0.62; P = 0.008).
Assessment of DD using DL can stratify the latent risk associated with the progression of early-stage AS.
从主动脉瓣（AV）硬化发展至主动脉瓣狭窄（AS）的过程具有高度变异性且难以预测。
作者研究了一种先前经验证的基于超声心动图的深度学习（DL）模型，该模型用于评估舒张功能障碍（DD），能否识别与AS发生和进展相关的潜在风险。
作者评估了社区动脉粥样硬化风险（ARIC）队列研究中的898名AV硬化参与者，并将DL预测的DD概率与两个终点相关联：1）AS的新诊断；2）随后的死亡率或AV干预的综合情况。在另外两个队列中进行了验证：1）50例接受心脏磁共振（CMR）成像和系列超声心动图评估的轻至中度AS患者；2）18例接受氟代氟化钠（NaF）和氟代脱氧葡萄糖正电子发射断层扫描（PET）联合计算机断层扫描（CT）以评估瓣膜炎症和钙化的AV硬化患者。
在ARIC队列中，DL预测的较高DD概率与AS的发生（校正风险比：3.482 [95%置信区间：2.061 - 5.884]；P < 0.001）以及随后的死亡率或AV干预（校正风险比：7.033 [95%置信区间：3.036 - 16.290]；P < 0.001）相关。源自ARIC队列的多变量Cox模型（纳入DL预测的DD概率）在CMR队列中有效预测了AS的进展（C指数：0.798 [95%置信区间：0.648 - 0.948]）。此外，该多变量Cox模型的预测与PET/CT队列中瓣膜F-NaF平均标准化摄取值呈正相关（r = 0.62；P = 0.008）。
使用DL评估DD可对与早期AS进展相关的潜在风险进行分层。
