Electroencephalography, Hospital Complications, and Longitudinal Outcomes After Subarachnoid Hemorrhage

Abstract

Background: Following non-traumatic subarachnoid hemorrhage (SAH), in-hospital delayed cerebral ischemia is predicted by two chief events on continuous EEG (cEEG): new or worsening epileptiform abnormalities (EAs) and deterioration of cEEG background frequencies. We evaluated the association between longitudinal outcomes and these cEEG biomarkers. We additionally evaluated the association between longitudinal outcomes and other in-hospital complications.

Methods: Patients with nontraumatic SAH undergoing ≥ 3 days of cEEG monitoring were enrolled in a prospective study evaluating longitudinal outcomes. Modified Rankin Scale (mRS) was assessed at discharge, and at 3- and 6-month follow-up time points. Adjusting for baseline severity in a cumulative proportional odds model, we modeled the mRS ordinally and measured the association between mRS and two forms of in-hospital cEEG deterioration: (1) cEEG evidence of new or worsening epileptiform abnormalities and (2) cEEG evidence of new background deterioration. We compared the magnitude of these associations at each time point with the association between mRS and other in-hospital complications: (1) delayed cerebral ischemia (DCI), (2) hospital-acquired infections (HAI), and (3) hydrocephalus. In a secondary analysis, we employed a linear mixed effects model to examine the association of mRS over time (dichotomized as 0-3 vs. 4-6) with both biomarkers of cEEG deterioration and with other in-hospital complications.

Results: In total, 175 mRS assessments were performed in 59 patients. New or worsening EAs developed in 23 (39%) patients, and new background deterioration developed in 24 (41%). Among cEEG biomarkers, new or worsening EAs were independently associated with mRS at discharge, 3, and 6 months, respectively (adjusted cumulative proportional odds 4.99, 95% CI 1.60-15.6; 3.28, 95% CI 1.14-9.5; and 2.71, 95% CI 0.95-7.76), but cEEG background deterioration lacked an association. Among hospital complications, DCI was associated with discharge, 3-, and 6-month outcomes (adjusted cumulative proportional odds 4.75, 95% CI 1.64-13.8; 3.4; 95% CI 1.24-9.01; and 2.45, 95% CI 0.94-6.6), but HAI and hydrocephalus lacked an association. The mixed effects model demonstrated that these associations were sustained over longitudinal assessments without an interaction with time.

Conclusion: Although new or worsening EAs and cEEG background deterioration have both been shown to predict DCI, only new or worsening EAs are associated with a sustained impairment in functional outcome. This novel finding raises the potential for identifying therapeutic targets that may also influence outcomes.

Keywords: Delayed cerebral ischemia; Electroencephalography; Hydrocephalus; Outcomes; Subarachnoid hemorrhage.

Conflict of interest statement

India Lissak has nothing to disclose. Dr. Locascio has nothing to disclose. Dr. Zafar reports grants from Sage therapeutics, grants from K23NS114201, outside the submitted work. Riana Schleicher has nothing to disclose. Dr. Patel reports personal fees from Penumbra, personal fees from Microvention, personal fees from Medtronic, outside the submitted work. Dr. Leslie-Mazwi has nothing to disclose. Dr. Stapleton has nothing to disclose. Dr. Koch has nothing to disclose. Dr. Kim has nothing to disclose. Kasey Anderson has nothing to disclose. Dr. Rosand reports grants from National Institutes of Health, grants from OneMind for Research, grants from American Heart Association, personal fees from Boehringer Ingelheim, outside the submitted work. Dr. Westover reports grants from NIH, during the conduct of the study. Dr. Kimberly reports grants and personal fees from Biogen, grants and personal fees from NControl Therapeutics, outside the submitted work; In addition, Dr. Kimberly has a patent 62/460,229 licensed to NControl Therapeutics. Dr. Rosenthal reports personal fees from UCB Pharmaceuticals, personal fees from Ceribell, Inc, grants from Department of Defense, outside the submitted work.

© 2021. Springer Science+Business Media, LLC, part of Springer Nature and Neurocritical Care Society.

Figures

Fig. 1

CONSORT diagram

Fig. 2

Percentage of patients achieving good outcome (mRS 0–3) at discharge, 3 months and 6 months. a Patients with new or worsening EAs had a significantly better outcome at discharge (Univariate OR 4.24; 95% CI 1.1–16.97; p  = 0.04), 3 months (Univariate OR 7.77; 95% CI 2.36–25.6; p  = 0.0007) and 6 months (Univariate OR 4.27; 95% CI 1.3–14.2; p  = 0.02) compared to those without new or worsening EAs. b Patients with worsening background deterioration did not have a significantly different outcome at discharge (Univariate OR 0.97; 95% CI 0.31–3.1; p  = 1.0), 3 months (Univariate OR 1.4; 95% CI 0.47–3.99; p  = 0.59) and 6 months (Univariate OR 0.74; 95% CI 0.23–2.4; p  = 0.77) compared to those without worsening background deterioration. c Delayed cerebral ischemia was significantly associated with poor outcome at discharge (Univariate OR 7.58; 95% CI 1.88–30.6; p  = 0.002), 3 months (OR 4.92; 95% CI 1.55–15.64; p  = 0.005) and 6 months (Univariate OR 3.25; 95% CI 0.96–10.97; p  = 0.05). d HAI and e hydrocephalus were not associated with poor outcome at any time point. (*p  < 0.05; **p  < 0.01; ***p  < 0.001)

Fig. 3

Distribution of mRS scores at discharge, 3 months and 6 months for a new or worsening EAs, b worsening background activity, c DCI, d HAI and e hydrocephalus

Fig. 4

Forest Plots displaying the independent odds ratio point estimates and confidence intervals from individual cumulative proportional odds models for a discharge, b 3-month, and c 6-month outcome measured ordinally on the modified Rankin Scale. New or worsening EAs (p  = 0.006; p  = 0.03; p  = 0.06) had a higher cumulative proportional odds for a shift to higher mRS scores, independent of age, Hunt-Hess clinical grade, and modified Fisher Scale radiologic grade. Worsening background deterioration was not associated with a shift in outcomes (p  = 0.76; p  = 0.81; p  = 0.97, respectively) at each time point. Each model adjusts for admission factors including age, HH clinical grade, and mFS radiologic grade