• Jian Chen, Wanxia Li, Qianping Chen, Zhou Zhou, Chen Chen, Yuping Hu, Yanna Si, and Jianjun Zou.
• Department of Anesthesiology, Perioperative and Pain Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
• Ann. Med. 2024 Dec 1; 56 (1): 24070672407067.

BackgroundBurst 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.Materials And MethodsWe 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.ResultsAccording 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 (p = 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.ConclusionsIncorporating 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.

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