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

Abstract

Purpose

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

Abbreviation

AUC, area under the curve; AV, aortic valve; CI, confidential interval; Cox-PH, Cox proportional hazard; DL, deep learning; HR, hazard ratio; ICI, integrated calibration index; MLBCs, major or life-threatening bleeding complications; NYHA, New York Heart Association; ROC, receiver operating characteristics; STS, Society of Thoracic Surgeons; TAVR, transcatheter aortic valve replacement.

Data Sharing Statement

The data that support the findings of this study are available from West China Hospital, but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the corresponding authors upon reasonable request and with permission of West China Hospital.

Ethical Approval and Consent to Participate

The patients of this study were drawn from West China Hospital of Sichuan University Transcatheter Aortic Valve Replacement Registry (WATCH TAVR registry, ChiCTR2000033419), which consecutively recruited patients undergoing TAVR in West China Hospital. The study was approved by Ethics Committee on Biomedical Research, West China Hospital of Sichuan University, and conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained from all study participants.

Acknowledgments

The authors thank the TAVR patients and their families for cooperating with this work.

Author Contributions

All authors contributed to data analysis, drafting or revising the article, have agreed on the journal to which the article will be submitted, gave final approval of the version to be published, and agreed to be accountable for all aspects of the work.

Disclosure

None of the authors declare any conflict of interest in this paper.

Additional information

Funding

This work was supported by the National Major Science and Technology Projects (grant number 2018AAA0100201) to ZY; the National Natural Science Foundation of China (grant 81970325 to MC; grant number 61906127 to JW); the 1.3.5 project for disciplines of excellence, West China Hospital, Sichuan University to MC; the Science and Technology Achievement Transformation Fund of West China Hospital of Sichuan University (CGZH19009) to MC.