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

Figures

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  Figure 1. Overview of study design and the architecture of BLeNet model. Input data included TAVR patients’ baseline characteristics, lab tests, CT characteristics, echocardiographic features, procedural details and antithrombotic medications. After data preparation, each patient information was summarized as a triplet value \documentclass[12pt]{minimal} \usepackage{wasysym} \usepackage[substack]{amsmath} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage[mathscr]{eucal} \usepackage{mathrsfs} \DeclareFontFamily{T1}{linotext}{} \DeclareFontShape{T1}{linotext}{m}{n} {linotext }{} \DeclareSymbolFont{linotext}{T1}{linotext}{m}{n} \DeclareSymbolFontAlphabet{\mathLINOTEXT}{linotext} \begin{document} $$\left\{ {\left({{x_i}, {e_i}, {t_i}} \right), i= 1, 2, 3 \ldots ,n} \right\}$$ \end{document}, where \documentclass[12pt]{minimal} \usepackage{wasysym} \usepackage[substack]{amsmath} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage[mathscr]{eucal} \usepackage{mathrsfs} \DeclareFontFamily{T1}{linotext}{} \DeclareFontShape{T1}{linotext}{m}{n} {linotext }{} \DeclareSymbolFont{linotext}{T1}{linotext}{m}{n}…

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  Figure 2. Flow chart of the study population.

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  Figure 3. Receiver operating characteristics curve based on 3-year incidence of MLBCs. Compared with the Cox-PH model (Orange line) and random survival forest model (green line), the proposed BLeNet model (blue line) reached a higher AUC (0.84, 95% CI: 0.76–0.91), indicating that this model had better discriminative performance than the Cox-PH and random survival forest models.

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  Figure 4. Calibration curve for 3-year incidence of MLBCs after TAVR. The calibration curve demonstrates the agreement between predicted risk (x-axis) and observed risk (y-axis). The diagonal line demonstrates perfect calibration. BLeNet model (blue line) was better calibrated than the Cox-PH model (Orange line) and random survival forest model (green line) with a calibration curve closer to the perfect line. ICI indicates the weighted difference between observed and predicted probabilities, and E50 indicates the median percentile of absolute difference. The bar histogram indicates the counts of patients with predicted risk on the x-axis.

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  Figure 5. Cumulative incidence of MLBCs in high- and low-risk patients stratified by BLeNet, Cox-PH and random survival forest model. (A) Cumulative incidence of MLBCs in high- and low-bleeding risk patients stratified by BLeNet model according to the best cutoff point in the ROC curve. (B) Cumulative incidence of MLBCs in high- and low-bleeding risk patients stratified by Cox-PH model according to the best cutoff point in the ROC curve. (C) Cumulative incidence of MLBCs in high- and low-bleeding risk patients stratified by random survival forest model according to the best cutoff point in the ROC curve.