Sanabria Melissa, Tastet Lionel, Pelletier Simon, Leclercq Mickael, Ohl Louis, Hermann Lara, Mattei Pierre-Alexandre, Precioso Frederic, Coté Nancy, Pibarot Philippe, Droit Arnaud
Centre hospitalier universitaire de Québec - Université Laval, Québec City, Québec, Canada.
Université Côte d'Azur, Inria, CNRS, I3S, Maasai, Sophia Antipolis, France.
JACC Adv. 2024 Sep 11;3(10):101234. doi: 10.1016/j.jacadv.2024.101234. eCollection 2024 Oct.
Aortic valve stenosis (AS) is a progressive chronic disease with progression rates that vary in patients and therefore difficult to predict.
The aim of this study was to predict the progression of AS using comprehensive and longitudinal patient data.
Machine and deep learning algorithms were trained on a data set of 303 patients enrolled in the PROGRESSA (Metabolic Determinants of the Progression of Aortic Stenosis) study who underwent clinical and echocardiographic follow-up on an annual basis. Performance of the models was measured to predict disease progression over long (next 5 years) and short (next 2 years) terms and was compared to a standard clinical model with usually used features in clinical settings based on logistic regression.
For each annual follow-up visit including baseline, we trained various supervised learning algorithms in predicting disease progression at 2- and 5-year terms. At both terms, LightGBM consistently outperformed other models with the highest average area under curves across patient visits (0.85 at 2 years, 0.83 at 5 years). Recurrent neural network-based models (Gated Recurrent Unit and Long Short-Term Memory) and XGBoost also demonstrated strong predictive capabilities, while the clinical model showed the lowest performance.
This study demonstrates how an artificial intelligence-guided approach in clinical routine could help enhance risk stratification of AS. It presents models based on multisource comprehensive data to predict disease progression and clinical outcomes in patients with mild-to-moderate AS at baseline.
主动脉瓣狭窄（AS）是一种进行性慢性疾病，其进展速度在患者中各不相同，因此难以预测。
本研究的目的是使用全面的纵向患者数据预测AS的进展。
在纳入PROGRESSA（主动脉瓣狭窄进展的代谢决定因素）研究的303例患者的数据集中训练机器学习和深度学习算法，这些患者每年接受临床和超声心动图随访。测量模型预测长期（未来5年）和短期（未来2年）疾病进展的性能，并与基于逻辑回归的临床环境中通常使用的特征的标准临床模型进行比较。
对于包括基线在内的每次年度随访，我们训练了各种监督学习算法来预测2年和5年时的疾病进展。在这两个时间段，LightGBM在预测患者随访期间疾病进展方面始终优于其他模型（2年时曲线下平均面积为0.85,5年时为0.83）基于循环神经网络的模型（门控循环单元和长短期记忆）和XGBoost也显示出强大预测能力而临床模型表现最差。
本研究展示了临床常规中人工智能指导方法如何有助于加强AS的风险分层。它提出了基于多源综合数据的模型以预测基线时轻度至中度AS患者的疾病进展和临床结局。
