Identification of intraoperative hypoxemia and hypoproteinemia as prognostic indicators in anastomotic leakage post-radical gastrectomy: an 8-year multicenter study utilizing machine learning techniques

Figures

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  Figure 1. Model-making process and flowchart of the study. (A) Study design flow chart. (B) Flow diagram of patients included in the study.

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  Figure 2. Evaluation of the three models for predicting prognosis. (A) ROC curves for the training set of three models predicting patient death at one year. (B) ROC curves for the validation set of three models predicting patient death at one year. (C) Calibration plots of the three models predicting patient death at one year. (D) DCA curves of the three models predicting patient death at one year. (E) ROC curves for the training set of three models predicting patient death at three years. (F) ROC curves for the validation set of three models predicting patient death at three years. (G) Calibration plots of the three models predicting patient death at three years. (H) DCA curves of the three models predicting patient death at three years. (I) ROC curves for the training set of three models predicting patient death at five years. (J) ROC curves for the validation set of three models predicting patient death at five years. (K) Calibration plots of the three models predicting patient death at five years. (L) DCA curves of the three models predicting patient death at five years.

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  Figure 3. Internal validation of the XGBoost model. (A) ROC curve for the training set of the XGBoost model predicting patient death at one year. (B) ROC curves for the validation set of the XGBoost model predicting patient death at one year. (C) ROC curves for the test set of the XGBoost model predicting patient death at one year. (D) External validation of the XGBoost model predicting patient death at one year. (E) ROC curves for the training set of the XGBoost model predicting patient death at three years. (F) ROC curves for the validation set of the XGBoost model predicting patient death at three years. (G) ROC curves for the test set of the XGBoost model predicting patient death at three years. (H) External validation of the XGBoost model predicting patient death at three years. (I) ROC curves for the training set of the XGBoost model predicting patient death at five years. (J) ROC curves for the validation set of the XGBoost model predicting patient death at five years. (K) ROC curves for the test set of the XGBoost model predicting patient death at five years. (L) External validation of the XGBoost model predicting patient death at five years.

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  Figure 4. SHAP summary plot. Risk factors are arranged along the y-axis based on their importance, which is given by the mean of their absolute Shapley values. The higher the risk factor is positioned in the plot, the more important it is for the model. (A) SHAP summary plot of models predicting patient death at one year. (B) SHAP summary plot of models predicting patient death at three years. (C) SHAP summary plot of models predicting patient death at five years.