Dasi Anoushka, Lee Beom, Polsani Venkateshwar, Yadav Pradeep, Dasi Lakshmi Prasad, Thourani Vinod H
Department of Biomedical Engineering, Ohio State University, Columbus, Ohio.
Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, Ga.
JTCVS Tech. 2023 Nov 30;23:5-17. doi: 10.1016/j.xjtc.2023.11.011. eCollection 2024 Feb.
After transcatheter aortic valve replacement, the mean transvalvular pressure gradient indicates the effectiveness of the therapy. The objective is to develop artificial intelligence to predict the post-transcatheter aortic valve replacement aortic valve pressure gradient and aortic valve area from preprocedural echocardiography and computed tomography data.
A retrospective study was conducted on patients who underwent transcatheter aortic valve replacement due to aortic valve stenosis. A total of 1091 patients were analyzed for pressure gradient predictions (mean age 76.8 ± 9.2 years, 57.8% male), and 1063 patients were analyzed for aortic valve area predictions (mean age 76.7 ± 9.3 years, 57.2% male). An artificial intelligence learning model was trained (training: n = 663 patients, validation: n = 206 patients) and tested (testing: n = 222 patients) to predict pressure gradient, and a separate artificial intelligence learning model was trained (training: n = 640 patients, validation: n = 218 patients) and tested (testing: n = 205 patients) for predicting aortic valve area.
The mean absolute error for pressure gradient and aortic valve area predictions was 3.0 mm Hg and 0.45 cm, respectively. Valve sheath size, body surface area, and age were determined to be the top 3 predictors for pressure gradient, and valve sheath size, left ventricular ejection fraction, and aortic annulus mean diameter were identified to be the top 3 predictors of post-transcatheter aortic valve replacement aortic valve area. A training dataset size of more than 500 patients demonstrated good robustness of the artificial intelligence models for pressure gradient and aortic valve area.
The artificial intelligence-based algorithm has demonstrated potential in predicting post-transcatheter aortic valve replacement transvalvular pressure gradient predictions for patients with aortic valve stenosis. Further studies are necessary to differentiate pressure gradient between valve types.
经导管主动脉瓣置换术后，平均跨瓣压差可表明治疗效果。目标是开发人工智能，根据术前超声心动图和计算机断层扫描数据预测经导管主动脉瓣置换术后的主动脉瓣压差和主动脉瓣面积。
对因主动脉瓣狭窄接受经导管主动脉瓣置换术的患者进行回顾性研究。共分析了1091例患者的压差预测情况（平均年龄76.8±9.2岁，男性占57.8%），以及1063例患者的主动脉瓣面积预测情况（平均年龄76.7±9.3岁，男性占57.2%）。训练（训练组：n = 663例患者，验证组：n = 206例患者）并测试（测试组：n = 222例患者）一个人工智能学习模型以预测压差，同时训练（训练组：n = 640例患者，验证组：n = 218例患者）并测试（测试组：n = 205例患者）另一个单独的人工智能学习模型以预测主动脉瓣面积。
压差和主动脉瓣面积预测的平均绝对误差分别为3.0 mmHg和0.45 cm。确定瓣膜鞘尺寸、体表面积和年龄为压差的前3个预测因素，瓣膜鞘尺寸、左心室射血分数和主动脉瓣环平均直径被确定为经导管主动脉瓣置换术后主动脉瓣面积的前3个预测因素。超过500例患者的训练数据集规模表明人工智能模型对压差和主动脉瓣面积具有良好的稳健性。
基于人工智能的算法在预测主动脉瓣狭窄患者经导管主动脉瓣置换术后的跨瓣压差方面已显示出潜力。有必要进行进一步研究以区分不同瓣膜类型之间的压差。
