Delayed cerebral ischemia (DCI) is the leading complication of subarachnoid hemorrhage (SAH). Because DCI was traditionally thought to be caused by large vessel vasospasm, transcranial Doppler ultrasounds (TCDs) have been the standard of care. Continuous EEG has emerged as a promising complementary monitoring modality and predicts increased DCI risk. Our objective was to determine whether combining EEG and TCD data improves prediction of DCI after SAH. We hypothesize that integrating these diagnostic modalities improves DCI prediction.

We retrospectively assessed patients with moderate to severe SAH (2011–2015; Fisher 3–4 or Hunt-Hess 4–5) who had both prospective TCD and EEG acquisition during hospitalization. Middle cerebral artery (MCA) peak systolic velocities (PSVs) and the presence or absence of epileptiform abnormalities (EAs), defined as seizures, epileptiform discharges, and rhythmic/periodic activity, were recorded daily. Logistic regressions were used to identify significant covariates of EAs and TCD to predict DCI. Group-based trajectory modeling (GBTM) was used to account for changes over time by identifying distinct group trajectories of MCA PSV and EAs associated with DCI risk.

We assessed 107 patients; DCI developed in 56 (51.9%). Univariate predictors of DCI are presence of high-MCA velocity (PSV ≥200 cm/s, sensitivity 27%, specificity 89%) and EAs (sensitivity 66%, specificity 62%) on or before day 3. Two univariate GBTM trajectories of EAs predicted DCI (sensitivity 64%, specificity 62.75%). Logistic regression and GBTM models using both TCD and EEG monitoring performed better. The best logistic regression and GBTM models used both TCD and EEG data, Hunt-Hess score at admission, and aneurysm treatment as predictors of DCI (logistic regression: sensitivity 90%, specificity 70%; GBTM: sensitivity 89%, specificity 67%).

EEG and TCD biomarkers combined provide the best prediction of DCI. The conjunction of clinical variables with the timing of EAs and high MCA velocities improved model performance. These results suggest that TCD and cEEG are promising complementary monitoring modalities for DCI prediction. Our model has potential to serve as a decision support tool in SAH management.

This study provides Class II evidence that combined TCD and EEG monitoring can identify delayed cerebral ischemia after SAH.

迟发性脑缺血（DCI）是蛛网膜下腔出血（SAH）的主要并发症。由于传统上认为 DCI 是由大血管血管痉挛引起的，因此经颅多普勒超声 (TCD) 一直是治疗的标准。连续 EEG 已成为一种有前途的补充监测方式，并预测 DCI 风险增加。我们的目标是确定结合 EEG 和 TCD 数据是否可以改善 SAH 后 DCI 的预测。我们假设整合这些诊断方式可以改善 DCI 预测。

我们回顾性评估了住院期间同时获得预期 TCD 和 EEG 的中度至重度 SAH 患者（2011-2015；Fisher 3-4 或 Hunt-Hess 4-5）。每天记录大脑中动脉 (MCA) 峰值收缩速度 (PSV) 和癫痫样异常 (EA) 的存在与否，定义为癫痫发作、癫痫样放电和节律性/周期性活动。逻辑回归用于识别 EA 和 TCD 的显着协变量以预测 DCI。基于组的轨迹建模 (GBTM) 通过识别与 DCI 风险相关的 MCA PSV 和 EA 的不同组轨迹来解释随时间的变化。

我们评估了 107 名患者；DCI 发展于 56 年（51.9%）。DCI 的单变量预测因子是在第 3 天或之前存在高 MCA 速度（PSV ≥200 cm/s，敏感性 27%，特异性 89%）和 EA（敏感性 66%，特异性 62%）。EA 的两个单变量 GBTM 轨迹预测的 DCI（敏感性 64%，特异性 62.75%）。同时使用 TCD 和 EEG 监测的逻辑回归和 GBTM 模型表现更好。最佳逻辑回归和 GBTM 模型同时使用 TCD 和 EEG 数据、入院时的 Hunt-Hess 评分和动脉瘤治疗作为 DCI 的预测因子（逻辑回归：敏感性 90%，特异性 70%；GBTM：敏感性 89%，特异性 67%） .

EEG 和 TCD 生物标志物相结合提供了对 DCI 的最佳预测。临床变量与 EA 的时间和高 MCA 速度的结合提高了模型性能。这些结果表明，TCD 和 cEEG 是用于 DCI 预测的有希望的补充监测模式。我们的模型有潜力作为 SAH 管理中的决策支持工具。

本研究提供了 II 类证据，表明 TCD 和 EEG 联合监测可以识别 SAH 后的迟发性脑缺血。