Predicting antibiotic resistance in patients with postoperative infections using machine learning based models

Abstract

Introduction
In the era of growing antimicrobial resistance, early detection and immediate treatment of antibiotic-resistant infections are crucial to ensuring successful outcomes in critically ill patients. The aim of this study is to apply machine learning (ML) to create classifiers that predict antibiotic resistance in postoperative infections caused by gram-negative pathogens, based on information retrievable from the Electronic Health Record. To determine if training prediction models on specific cultures improve the predictions’ performance, eight sub-datasets have been created that only included specific culture sources or cultures containing specific bacteria.
Methods
All surgical procedures in the LUMC between January 2015 and May 2023 after which gram-negative bacteria had been cultured within 30 days after surgery were included. Logistic regression, random forest and support vector machine models were developed, evaluated using area under the receiver operating characteristics (AUROC) metric and calibrated afterwards. For each model, the most important predictors were determined using SHAP values.
Results
In total 27 models were created for the dataset and eight sub-datasets. The dataset containing all positive postoperative cultures within 30 days of surgery consisted of 5777 procedures with a resistance rate of 27.4%. The AUROC for the models developed for the whole dataset ranged between 0.65-0.68 on an unseen test set. The AUROC on unseen data for models developed on specific culture sources ranged between 0.63-0.79 and for those trained on specific bacteria between 0.63-0.75. The included features that were deemed most important are the presence of a previous resistant culture, abdominal surgery, the presence of an indwelling device after surgery, and the patient’s sex, considering all (sub-)datasets.
Conclusions
This study shows that ML holds promise for predicting antimicrobial resistance. However, the current results are insufficient to support the implementation of these models in clinical practice to assist clinicians in choosing appropriate antibiotic therapy prior to receiving antibiogram results. With the current research, it cannot be proved with certainty that training models to particular postoperative infections enhances the predictions’ performance. To further investigate the potential clinical benefit of applying ML prediction models in the context of antibiotic resistance, further research is necessary on extracting more features, with increased quality, which are available at the time the culture is taken.