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Published on: June 18, 2021
Combining Transcranial Doppler and EEG Data to Predict Delayed Cerebral Ischemia After Subarachnoid Hemorrhage
Hsin Yi Chen1, Jonathan Elmer1, Sahar F Zafar1
1From the Department of Neurology (H.Y.C., E.J.G., L.J.H., H.P.Z., K.N.S., N.H.P., J.A.K.), Yale University, New Haven, CT; Department of Critical Care Medicine (J.E.), University of Pittsburgh Medical Center, PA; and Department of Neurology (S.F.Z., M.G., V.M.J., E.S.R., M.B.W.), Massachusetts General Hospital, Boston.
Insights
Combining EEG and TCD monitoring improves prediction of delayed cerebral ischemia (DCI) after subarachnoid hemorrhage (SAH). This integrated approach offers a more accurate assessment of DCI risk in SAH patients.
Area of Science:
- Neuroscience
- Neurology
- Critical Care Medicine
Background:
- Delayed cerebral ischemia (DCI) is a primary complication following subarachnoid hemorrhage (SAH).
- Transcranial Doppler (TCD) ultrasound has been the standard for monitoring, traditionally focusing on vasospasm.
- Continuous electroencephalography (EEG) monitoring shows promise in predicting DCI risk.
Purpose of the Study:
- To evaluate if combining EEG and TCD data enhances the prediction of DCI in SAH patients.
- To test the hypothesis that integrated diagnostic modalities improve DCI prediction accuracy.
Main Methods:
- Retrospective analysis of 107 SAH patients (Fisher 3-4 or Hunt-Hess 4-5) with TCD and EEG data.
- Daily monitoring of Middle Cerebral Artery (MCA) peak systolic velocities (PSVs) and epileptiform abnormalities (EAs).
- Utilized logistic regression and Group-Based Trajectory Modeling (GBTM) to identify predictors and temporal patterns of DCI.
Main Results:
- DCI occurred in 56.9% of patients.
- Univariate predictors by day 3 included high MCA PSV (≥200 cm/s) and EAs.
- Combined logistic regression and GBTM models incorporating TCD, EEG, Hunt-Hess score, and aneurysm treatment achieved high sensitivity (90%) and specificity (70%).
Conclusions:
- Combined EEG and TCD monitoring provides superior prediction of DCI compared to individual modalities.
- Integrating clinical variables with temporal EEG and TCD data significantly improves DCI risk assessment.
- This combined monitoring approach can serve as a valuable decision support tool for managing SAH patients.
Background And Objectives:
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.
Methods:
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.
Results:
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%).
Discussion:
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.
Classification Of Evidence:
This study provides Class II evidence that combined TCD and EEG monitoring can identify delayed cerebral ischemia after SAH.
