RT Journal Article
SR Electronic
T1 Prediction of futile recanalisation after endovascular treatment in acute ischaemic stroke: development and validation of a hybrid machine learning model
JF Stroke and Vascular Neurology
JO Stroke Vasc Neurol
FD BMJ Publishing Group Ltd
SP 631
OP 639
DO 10.1136/svn-2023-002500
VO 9
IS 6
A1 Nie, Ximing
A1 Yang, Jinxu
A1 Li, Xinxin
A1 Zhan, Tianming
A1 Liu, Dongdong
A1 Yan, Hongyi
A1 Wei, Yufei
A1 Liu, Xiran
A1 Chen, Jiaping
A1 Gong, Guoyang
A1 Wu, Zhenzhou
A1 Yang, Zhonghua
A1 Wen, Miao
A1 Gu, Weibin
A1 Pan, Yuesong
A1 Jiang, Yong
A1 Meng, Xia
A1 Liu, Tao
A1 Cheng, Jian
A1 Li, Zixiao
A1 Miao, Zhongrong
A1 Liu, Liping
YR 2024
UL http://svn.bmj.com/content/9/6/631.abstract
AB Background Identification of futile recanalisation following endovascular therapy (EVT) in patients with acute ischaemic stroke is both crucial and challenging. Here, we present a novel risk stratification system based on hybrid machine learning method for predicting futile recanalisation.Methods Hybrid machine learning models were developed to address six clinical scenarios within the EVT and perioperative management workflow. These models were trained on a prospective database using hybrid feature selection technique to predict futile recanalisation following EVT. The optimal model was validated and compared with existing models and scoring systems in a multicentre prospective cohort to develop a hybrid machine learning-based risk stratification system for futile recanalisation prediction.Results Using a hybrid feature selection approach, we trained and tested multiple classifiers on two independent patient cohorts (n=1122) to develop a hybrid machine learning-based prediction model. The model demonstrated superior discriminative ability compared with other models and scoring systems (area under the curve=0.80, 95% CI 0.73 to 0.87) and was transformed into a web application (RESCUE-FR Index) that provides a risk stratification system for individual prediction (accessible online at fr-index.biomind.cn/RESCUE-FR/).Conclusions The proposed hybrid machine learning approach could be used as an individualised risk prediction model to facilitate adherence to clinical practice guidelines and shared decision-making for optimal candidate selection and prognosis assessment in patients undergoing EVT.Data are available on reasonable request.