A novel interpretative tool for early prediction of low cardiac output syndrome after valve surgery: online machine learning models

Figures

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  Figure 1.. Graphical abstract. We developed novel machine learning models for early predicting low cardiac output syndrome after valve surgery preoperatively and 0.5 h postoperatively.

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  Figure 2.. The flow chart for enrollment and exclusion of patients with low cardiac output syndrome.

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  Figure 6.. The ranking of feature weights based on Shapley Additive exPlanations (SHAP) values in the preoperative model. (a) The standard bar chart shows the SHAP values for each feature and was sorted by weight. (b) In the scatter plot of feature density, red dots indicate higher value, and blue dots indicate lower value. More red dots on the right side of the vertical axis indicate positive correlation with low cardiac output syndrome, while more red dots on the left side indicate negative correlation. EF: ejection fraction; LVDd: left ventricular diastolic diameter; LVPW: left ventricular posterior wall thickness; BMI: body mass index.

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  Figure 7.. The ranking of feature weights based on Shapley Additive exPlanations (SHAP) values in the postoperative model. (a) The standard bar chart shows the SHAP values for each feature and was sorted by weight. (b) In the scatter plot of feature density, red dots indicate higher value, and blue dots indicate lower value. More red dots on the right side of the vertical axis indicate positive correlation with low cardiac output syndrome, while more red dots on the left side indicate negative correlation. EF: ejection fraction; LVDd: left ventricular diastolic diameter; MAP: mean arterial pressure; BMI: body mass index; LVPW: left ventricular posterior wall thickness; actime: aortic clamping time; CVP: central venous pressure; TWA-CVP: time weighted area under the curve for central venous pressure above the threshold; TWA-MAP: time weighted area under the curve for mean arterial pressure below the threshold.

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  Figure 3.. The prediction and calibration capability of the preoperative machine learning model for predicting low cardiac output syndrome. (a) The receiver operating characteristic curve (ROC) for the testing cohort. (b) The calibration curve for the area under the curve of the testing cohort. AUROC: area under the ROC; LR: logistic regression; SVM: support vector machine; RFC: random forest classifier; XGB: extreme gradient boost; DNN: deep neural network.

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  Figure 4.. The prediction and calibration capability of the postoperative machine learning model for predicting low cardiac output syndrome. (a) The receiver operating characteristic curve (ROC) for the testing cohort. (b) The calibration curve for the area under the curve of the testing cohort. AUROC: area under the ROC; LR: logistic regression; SVM: support vector machine; RFC: random forest classifier; XGB: extreme gradient boost; DNN: deep neural network.

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  Figure 5.. The receiver operating characteristic curve (ROC) of vasoactive-inotropic score (VIS), and preoperative and postoperative machine learning models. AUROC: area under the ROC.