Evaluation of postoperative bleeding risk after dental extractions in patients on antithrombotic medication: A comparison of machine learning and clinical experience

TL;DR

This study compares machine learning models and a surgeon's predictions for bleeding risk after dental extractions in patients on blood-thinning medications.

Contribution

The study demonstrates that machine learning algorithms outperformed an experienced surgeon in predicting postoperative bleeding risk in anticoagulated patients.

Findings

Machine learning algorithms (logistic regression, XGBoost, random forest, KNN) achieved higher balanced accuracy than an experienced surgeon in predicting bleeding risk.

Dual anticoagulation significantly increased the risk of postoperative bleeding.

Algorithms can objectively assess bleeding risk and identify key predictive variables for clinical guidance.

Abstract

The aim of this study was to identify high-risk dental extractions in patients taking antiplatelet (AP) medication or anticoagulants (ACs) and to compare an experienced surgeon’s decisions with machine learning (ML) algorithms.

Our study included 2000 procedures, of which 1788 were conducted in patients under monotherapy with AP medication, vitamin K antagonists (VKAs), heparin, or direct oral anticoagulants (DOACs), 426 were performed under dual therapy, and 27 under triple therapy. Four algorithms, logistic regression (LR), eXtreme gradient boost (XGB), random forest (RF), and K-nearest neighbors (KNN), were trained with 80% (1600 procedures) of the derived data. Afterwards, an experienced oral surgeon and the algorithms were tested on the remaining 20% (400 procedures) of the data to evaluate the predictive power with respect to bleeding incidents.

The incidence of hemorrhagic events was low (4.35%). Dual anticoagulation significantly affected the risk of bleeding. Evaluating the results of the predictions, all four algorithms outperformed the surgeon in terms of balanced accuracy (LR: 58%; RF: 59%; XGB: 61%; KNN: 62%; surgeon: 53%).

Decision-making based on various parameters influencing bleeding risk is complex, and surgeons tend to overestimate this risk. Both the algorithms and the surgeon had a share of false positive predictions; however, in a medical context, preventive overcaution does less damage than underestimation.

Algorithms can provide an objective assessment of bleeding risk and help determine risk profiles, uncover variables with the highest predictive power, and serve as guidance on postoperative observation periods.

This study was approved by the Ethics Committee of the Medical Faculty of RWTH Aachen (Decision Number 24–353). This was a retrospective clinical study designed to analyze postoperative bleeding after dental extractions in patients under antithrombotic medication and to evaluate the prediction of bleeding events by different algorithms and human experience.

The online version contains supplementary material available at 10.1007/s00784-025-06590-0.

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