A Supervised Explainable Machine Learning Model for Perioperative Neurocognitive Disorder in Liver-Transplantation Patients and External Validation on the Medical Information Mart for Intensive Care IV Database: Retrospective Study

Figures

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  Figure 1. Diagram of experimental procedure and flowchart, (A) brief diagram of the experimental procedure and (B) flowchart for patient enrollment, development and selection of machine learning model. LGB: light gradient boosting machine; LR: logistic regression; MIMIC-Ⅳ: the Medical Information Mart for Intensive Care Ⅳ; ML: machine learning; MLP: multilayer perceptron classifier; RF: random forest classifier; SVM: support vector machine; XGB: extreme gradient boosting with classification trees.

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  Figure 2. Performance metrics for six ML models. (A) ROC curves of six ML models. (B) Details of the model performance metrics. Accuracy=(TP+TN)/(TP+TN+FP+FN); AUC, the area under the receiver-operating curve; F1=2*Precision*Recall/ (Precision + Recall); FN: false negative; FP: false positive; LGB: light gradient boosting machine; LR: logistic regression; MLP: multilayer perceptron classifier; RF: random forest classifier; Sensitivity=TP/ (TP + FN); Specificity (Recall)=TN/ (TN + FP); SVM: support vector machine; TN: true negative; TP: true positive; XGB: extreme gradient boosting with classification trees.

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  Figure 3. SHAP analysis of the LR model and model performance in horizontal comparison and external validation. (A) Horizontal comparison of predicting performance between the LR model and MELD/SOFA scores in the internal validation set. (B-C) The SHAP summary plot demonstrated the general importance of each feature in LR model. The color bar on the right indicates the relative value of a feature in each case, with red color representing higher value and blue color representing lower value. (D-E) ROC curves and model performance in the external validation. AST: aspartate aminotransferase; AUC: the area under the receiver-operating curve; CHE: cover hepatic encephalopathy; EBL: estimated blood loss; eGFR: estimated glomerular filtration rate; hsCRP: hypersensitive C-reactive protein; LR: logistic regression; MELD scores: model for end-stage liver disease score; MIMIC-IV: Medical Information Mart for Intensive Care Ⅳ; PLT: platelet; PT: prothrombin time; SHAP: Shapley additive explanations; SOFA scores: sequential organ failure assessment score.

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  Figure 4. Post–liver transplantation survival associated with perioperative neurocognitive dysfunction. Patients with post–liver transplantation perioperative neurocognitive dysfunction showed a significantly lower survival rate. LT: liver transplantation; PND: perioperative neurocognitive dysfunction.

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  Figure 5. Online calculator for the clinical interface of the post–liver transplantation perioperative neurocognitive dysfunction risk prediction logistic regression model. (A) Patient No. 48 post–liver transplantation perioperative neurocognitive dysfunction will occur (probability of perioperative neurocognitive dysfunction: 94%); (B) Patient No. 122 post–liver transplantation perioperative neurocognitive dysfunction will not occur (probability of perioperative neurocognitive dysfunction: 17%).