Optimizing anesthesia management based on early identification of electroencephalogram burst suppression risk in non-cardiac surgery patients: a visualized dynamic nomogram

Burst suppression (BS) is a specific electroencephalogram (EEG) pattern that may contribute to postoperative delirium and negative outcomes. Few prediction models of BS are available and some factors such as frailty and intraoperative hypotension (IOH) which have been reported to promote the occurrence of BS were not included. Therefore, we look forward to creating a straightforward, precise, and clinically useful prediction model by incorporating new factors, such as frailty and IOH. We retrospectively collected 540 patients and analyzed the data from 418 patients. Univariate analysis and backward stepwise logistic regression were used to select risk factors to develop a dynamic nomogram model, and then we developed a web calculator to visualize the process of prediction. The performance of the nomogram was evaluated in terms of discrimination, calibration, and clinical utility. According to the receiver operating characteristic (ROC) analysis, the nomogram showed good discriminative ability (AUC = 0.933) and the Hosmer–Lemeshow goodness-of-fit test demonstrated the nomogram had good calibration (<i>p</i> = 0.0718). Age, Clinical Frailty Scale (CFS) score, midazolam dose, propofol induction dose, total area under the hypotensive threshold of mean arterial pressure (MAP_AUT), and cerebrovascular diseases were the independent risk predictors of BS and used to construct nomogram. The web-based dynamic nomogram calculator was accessible by clicking on the URL: https://eegbsnomogram.shinyapps.io/dynnomapp/ or scanning a converted Quick Response (QR) code. Incorporating two distinctive new risk factors, frailty and IOH, we firstly developed a visualized nomogram for accurately predicting BS in non-cardiac surgery patients. The model is expected to guide clinical decision-making and optimize anesthesia management. We firstly developed a dynamic nomogram to accurately predict the risk of burst suppression (BS) in non-cardiac surgery, and provided a Quick Response (QR) code based on a web calculator to visualize it.The accuracy of the model is enhanced by the inclusion of frailty and intraoperative hypotension (IOH).Our model aims to help clinicians effectively identify the risk of BS, thus guiding clinical decision-making and optimizing anesthesia management. We firstly developed a dynamic nomogram to accurately predict the risk of burst suppression (BS) in non-cardiac surgery, and provided a Quick Response (QR) code based on a web calculator to visualize it. The accuracy of the model is enhanced by the inclusion of frailty and intraoperative hypotension (IOH). Our model aims to help clinicians effectively identify the risk of BS, thus guiding clinical decision-making and optimizing anesthesia management.