Imagine cracking a secret code that reveals which older patients are at risk of developing postoperative delirium (POD). Scientists have developed a machine-learning model that uses intraoperative frontal electroencephalographic (EEG) signatures to predict the likelihood of POD in elderly patients undergoing surgery. By analyzing routine EEG data and clinical information from over 1,200 patients, the model was able to assess the risk of POD with impressive accuracy. When combined with clinical parameters, the model achieved an area under the ROC curve (AUC-ROC) score of 0.77, indicating its effectiveness in early identification of at-risk patients. Furthermore, when evaluating different anesthetic groups separately, the model showed even higher AUC-ROC scores, ranging from 0.73 to 0.80. This breakthrough could provide valuable insight into personalizing intraoperative and postoperative therapies to mitigate the risk of POD. The study not only demonstrates the potential of machine learning in healthcare but also highlights the importance of monitoring EEG signatures as a predictive tool for cerebral dysfunction. Dive deeper into this cutting-edge research to uncover how EEG analysis is revolutionizing patient care.

ObjectiveIn older patients receiving general anesthesia, postoperative delirium (POD) is the most frequent form of cerebral dysfunction. Early identification of patients at higher risk to develop POD could provide the opportunity to adapt intraoperative and postoperative therapy. We, therefore, propose a machine learning approach to predict the risk of POD in elderly patients, using routine intraoperative electroencephalography (EEG) and clinical data that are readily available in the operating room.MethodsWe conducted a retrospective analysis of the data of a single-center study at the Charité-Universitätsmedizin Berlin, Department of Anesthesiology [ISRCTN 36437985], including 1,277 patients, older than 60 years with planned surgery and general anesthesia. To deal with the class imbalance, we used balanced ensemble methods, specifically Bagging and Random Forests and as a performance measure, the area under the ROC curve (AUC-ROC). We trained our models including basic clinical parameters and intraoperative EEG features in particular classical spectral and burst suppression signatures as well as multi-band covariance matrices, which were classified, taking advantage of the geometry of a Riemannian manifold. The models were validated with 10 repeats of a 10-fold cross-validation.ResultsIncluding EEG data in the classification resulted in a robust and reliable risk evaluation for POD. The clinical parameters alone achieved an AUC-ROC score of 0.75. Including EEG signatures improved the classification when the patients were grouped by anesthetic agents and evaluated separately for each group. The spectral features alone showed an AUC-ROC score of 0.66; the covariance features showed an AUC-ROC score of 0.68. The AUC-ROC scores of EEG features relative to patient data differed by anesthetic group. The best performance was reached, combining both the EEG features and the clinical parameters. Overall, the AUC-ROC score was 0.77, for patients receiving Propofol it was 0.78, for those receiving Sevoflurane it was 0.8 and for those receiving Desflurane 0.73. Applying the trained prediction model to an independent data set of a different clinical study confirmed these results for the combined classification, while the classifier on clinical parameters alone did not generalize.ConclusionA machine learning approach combining intraoperative frontal EEG signatures with clinical parameters could be an easily applicable tool to early identify patients at risk to develop POD.

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