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 (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.
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.

爆发抑制（Burst suppression, BS）是一种特异性脑电图（electroencephalogram, EEG）模式，其可能与术后谵妄及不良预后相关。目前针对BS的预测模型较为匮乏，且既往研究报道的可促进BS发生的部分因素（如衰弱与术中低血压（intraoperative hypotension, IOH））尚未被纳入模型。因此，本研究拟纳入衰弱、IOH等新型因素，构建一款简便精准、具备临床实用价值的BS预测模型。

本研究回顾性收集了540例患者的临床资料，最终纳入418例患者的数据进行分析。采用单变量分析与后退逐步logistic回归筛选危险因素，构建动态列线图预测模型，并开发了一款网页计算器以可视化预测流程。随后从区分度、校准度与临床实用性三方面评估该列线图的性能。

受试者工作特征（receiver operating characteristic, ROC）曲线分析显示，该列线图具备良好的区分能力（曲线下面积（area under the curve, AUC）=0.933）；Hosmer-Lemeshow拟合优度检验结果表明模型校准度良好（p=0.0718）。最终筛选出年龄、临床衰弱量表（Clinical Frailty Scale, CFS）评分、咪达唑仑用量、丙泊酚诱导剂量、平均动脉压低血压阈值下总面积（mean arterial pressure, MAP_AUT）以及脑血管疾病作为BS的独立危险因素，用于构建列线图。该基于网页的动态列线图计算器可通过访问链接https://eegbsnomogram.shinyapps.io/dynnomapp/ 或扫描转换后的快速响应码（Quick Response, QR）进行使用。

本研究首次纳入衰弱与IOH这两个新颖的危险因素，构建了一款可可视化的列线图模型，用于精准预测非心脏手术患者的BS发生风险。该模型有望指导临床决策，优化麻醉管理方案。

本研究首次构建了一款动态列线图模型，可精准预测非心脏手术患者的爆发抑制（BS）发生风险，并基于网页计算器开发了配套的快速响应码（QR）以实现可视化预测。
该模型通过纳入衰弱与术中低血压（IOH）提升了预测精度。
本模型旨在帮助临床医师高效识别BS发生风险，进而指导临床决策、优化麻醉管理方案。

本研究首次构建了一款动态列线图模型，可精准预测非心脏手术患者的爆发抑制（BS）发生风险，并基于网页计算器开发了配套的快速响应码（QR）以实现可视化预测。
该模型通过纳入衰弱与术中低血压（IOH）提升了预测精度。
本模型旨在帮助临床医师高效识别BS发生风险，进而指导临床决策、优化麻醉管理方案。