Deep Learning in Prediction of Late Major Bleeding After Transcatheter Aortic Valve Replacement

Abstract

Yuheng Jia1 *, Gaden Luosang2,3 *, Yiming Li,1 Jianyong Wang,2 Pengyu Li,4 Tianyuan Xiong,1 Yijian Li,1 Yanbiao Liao,1 Zhengang Zhao,1 Yong Peng,1 Yuan Feng,1 Weili Jiang,2 Wenjian Li,2 Xinpei Zhang,2 Zhang Yi,2 Mao Chen1 1Department of Cardiology, West China Hospital, Sichuan University, Chengdu, Sichuan, People’s Republic of China; 2Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, Sichuan, People’s Republic of China; 3Department of Information Science and Technology, Tibet University, Lhasa City, People’s Republic of China; 4West China Medical School, Sichuan University, Chengdu, Sichuan, People’s Republic of China*These authors contributed equally to this workCorrespondence: Mao ChenDepartment of Cardiology, West China Hospital, Sichuan University, No. 37 Guoxue Street, Chengdu, 610041, People’s Republic of ChinaTel [phone]Fax [phone]Email [email protected] YiMachine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, 610065, People’s Republic of ChinaTel [phone]Fax [phone]Email [email protected]: Late major bleeding is one of the main complications after transcatheter aortic valve replacement (TAVR). We aimed to develop a risk prediction model based on deep learning to predict major or life-threatening bleeding complications (MLBCs) after TAVR.Patients and Methods: This was a retrospective study including TAVR patients from West China Hospital of Sichuan University Transcatheter Aortic Valve Replacement Registry (ChiCTR2000033419) between April 17, 2012 and May 27, 2020. A deep learning-based model named BLeNet was developed with 56 features covering baseline, procedural, and post-procedural characteristics. The model was validated with the bootstrap method and evaluated using Harrell’s concordance index (c-index), receiver operating characteristics (ROC) curve, calibration curve, and Kaplan–Meier estimate. Captum interpretation library was applied to identify feature importance. The BLeNet model was compared with the traditional Cox proportional hazard (Cox-PH) model and the random survival forest model in the metrics mentioned above.Results: The BLeNet model outperformed the Cox-PH and random survival forest models significantly in discrimination [optimism-corrected c-index of BLeNet vs Cox-PH vs random survival forest: 0.81 (95% CI: 0.79– 0.92) vs 0.72 (95% CI: 0.63– 0.77) vs 0.70 (95% CI: 0.61– 0.74)] and calibration (integrated calibration index of BLeNet vs Cox-PH vs random survival forest: 0.007 vs 0.015 vs 0.019). In Kaplan–Meier analysis, BLeNet model had great performance in stratifying high- and low-bleeding risk patients (p < 0.0001).Conclusion: Deep learning is a feasible way to build prediction models concerning TAVR prognosis. A dedicated bleeding risk prediction model was developed for TAVR patients to facilitate well-informed clinical decisions.Keywords: deep learning, transcatheter aortic valve replacement, major or life-threatening bleeding complications, prediction model

Keywords

• deep learning
• transcatheter aortic valve replacement
• major or life-threatening bleeding complications
• prediction model