Machine Learning to Dynamically Predict In-Hospital Venous Thromboembolism After Inguinal Hernia Surgery: Results From the CHAT-1 Study

Abstract

Background: The accuracy of current prediction tools for venous thromboembolism (VTE) events following hernia surgery remains insufficient for individualized patient management strategies. To address this issue, we have developed a machine learning (ML)-based model to dynamically predict in-hospital VTE in Chinese patients after hernia surgery.

Methods: ML models for the prediction of postoperative VTE were trained on a cohort of 11 305 adult patients with hernia from the CHAT-1 trial, which included patients across 58 institutions in China. In data processing, data imputation was conducted using random forest (RF) algorithm, and balanced sampling was done by adaptive synthetic sampling algorithm. Data were split into a training cohort (80%) and internal validation cohort (20%) prior to oversampling. Clinical features available pre-operatively and postoperatively were separately selected using the Sequence Forward Selection algorithm. Nine-candidate ML models were applied to the pre-operative and combined datasets, and their performance was evaluated using various metrics, including area under the receiver operating characteristic curve (AUROC). Model interpretations were generated using importance scores, which were calculated by transforming model features into scaled variables and representing them in radar plots.

Results: The modeling cohort included 2856 patients, divided into 2536 cases for derivation and 320 cases for validation. Eleven pre-operative variables and 15 combined variables were explored as predictors related to in-hospital VTE. Acceptable-performing models for pre-operative data had an AUROC ≥ 0.60, including logistic regression, support vector machine with linear kernel (SVM_Linear), attentive interpretable Tabular learning (TabNet), and RF. For combined data, logistic regression, SVM_Linear, and TabNet had better performance, with an AUROC ≥ 0.65 for each model. Based on these models, 7 pre-operative predictors and 10 combined predictors were depicted in radar plots.

Conclusions: A ML-based approach for the identification of in-hospital VTE events after hernia surgery is feasible. TabNet showed acceptable performance, and might be useful to guide clinical decision making and VTE prevention. Further validated study will strengthen this finding.

Keywords: algorithm; inguinal hernia; logistic regression; machine learning; risk factors; venous thromboembolism.

Figure 1.

The model development strategy. Abbreviations: AUROC, area under the receiver operating characteristic curve; TabNet, attentive interpretable Tabular learning; VTE, venous thromboembolism.

Figure 2.

AUROC and 95% confidence intervals for machine learning prediction models. Abbreviations: AUROC, area under the receiver operating characteristic curve; Adaboost, adaptive boosting; Catboost, gradient boosting with categorical features support; LightGBM, light gradient boosting machine; SVM_Liner, support vector machine with linearity kernel; SVM_RBF, support vector machine with radial basis function kernel; TabNet, attentive interpretable Tabular learning; XGBoost, extreme gradient boosting.

Figure 3.

Radar plot for the most important predictors of postoperative VTE based on pre-operative (A) and combined data (B). Abbreviations: SVM_Liner, support vector machine with linearity kernel; TabNet, attentive interpretable Tabular learning; VTE, venous thromboembolism.