International journal of surgery (London, England) · 2025
Clinical value of aortic arch morphology in transfemoral TAVR: artificial intelligence evaluation.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Mao Yu, Liu Yang, Zhai Mengen, Jin Ping, Chen Fangyao, Yang Yuhui, Zhu Guangyu, Yang Tingting, Zhang Gejun, Xu Kai, Shang Xiaoke, Zhao Yuan, Ni Buqing, Li Hongxin, Tang Min, Jian Zhao, Yang Yining, Zhang Haibo, Wei Lai, Liu Jian, Noterdaeme Timothée, Lange Ruediger, Guo Yingqiang, Pan Xiangbin, Wu Yongjian, Yang Jian
Аннотация
BACKGROUND: The impact of aortic arch (AA) morphology on the management of the procedural details and the clinical outcomes of the transfemoral artery (TF)-transcatheter aortic valve replacement (TAVR) has not been evaluated. The goal of this study was to evaluate the AA morphology of patients who had TF-TAVR using an artificial intelligence algorithm and then to evaluate its predictive value for clinical outcomes.
MATERIALS AND METHODS: A total of 1480 consecutive patients undergoing TF-TAVR using a new-generation transcatheter heart valve at 12 institutes were included in this retrospective study. The AA measurements were evaluated by deep learning, and then the approach index (I A ) was determined. The machine learning algorithm was used to construct the predictive model and was validated externally.
RESULTS: The area under the curve of the I A model using random forest and logistic regression was 0.675 [95% confidence interval (CI): 0.586-0.764] and 0.757 (95% CI: 0.665-0.849), respectively. The I A model was validated externally, and consistent distinctions were obtained. After we used a generalized propensity score matching method for continuous exposure, the I A was the strongest correlation factor for major procedural events (odds ratio: 3.87; 95% CI: 2.13-7.59, P < 0.001). When leaflet morphology or transcatheter heart valve type was an interactive item with I A , neither of them was statistically significant in terms of clinical outcomes.
CONCLUSION: I A may be used to identify the impact of AA morphology on procedural and clinical outcomes in patients having TF-TAVR and to help to predict the procedural complications.
MATERIALS AND METHODS: A total of 1480 consecutive patients undergoing TF-TAVR using a new-generation transcatheter heart valve at 12 institutes were included in this retrospective study. The AA measurements were evaluated by deep learning, and then the approach index (I A ) was determined. The machine learning algorithm was used to construct the predictive model and was validated externally.
RESULTS: The area under the curve of the I A model using random forest and logistic regression was 0.675 [95% confidence interval (CI): 0.586-0.764] and 0.757 (95% CI: 0.665-0.849), respectively. The I A model was validated externally, and consistent distinctions were obtained. After we used a generalized propensity score matching method for continuous exposure, the I A was the strongest correlation factor for major procedural events (odds ratio: 3.87; 95% CI: 2.13-7.59, P < 0.001). When leaflet morphology or transcatheter heart valve type was an interactive item with I A , neither of them was statistically significant in terms of clinical outcomes.
CONCLUSION: I A may be used to identify the impact of AA morphology on procedural and clinical outcomes in patients having TF-TAVR and to help to predict the procedural complications.