Diagnostic accuracy of quantitative EEG to detect delayed cerebral ischemia after subarachnoid hemorrhage: A preliminary study

Accepted Manuscript Diagnostic accuracy of quantitative EEG to detect delayed cerebral ischemia after subarachnoid hemorrhage: A preliminary study B. Balança, F. Dailler, S. Boulogne, T. Ritzenthaler, F. Gobert, S. Rheims, N. Andre-Obadia PII: DOI: Reference:

S1388-2457(18)31133-7 https://doi.org/10.1016/j.clinph.2018.06.013 CLINPH 2008578

To appear in:

Clinical Neurophysiology

Received Date: Revised Date: Accepted Date:

18 December 2017 24 April 2018 15 June 2018

Please cite this article as: Balança, B., Dailler, F., Boulogne, S., Ritzenthaler, T., Gobert, F., Rheims, S., AndreObadia, N., Diagnostic accuracy of quantitative EEG to detect delayed cerebral ischemia after subarachnoid hemorrhage: A preliminary study, Clinical Neurophysiology (2018), doi: https://doi.org/10.1016/j.clinph. 2018.06.013

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Diagnostic accuracy of quantitative EEG to detect delayed cerebral ischemia after subarachnoid hemorrhage: a preliminary study † Balança B 1,2,3 *, Dailler F 1, Boulogne S Andre-Obadia N 4,7.

2,4

, Ritzenthaler T

1,5

, Gobert F

1,6

, Rheims S

2,4

,

1

Hospices Civils de Lyon, Hôpital Pierre Wertheimer, Department of anesthesiology and intensive care medicine, Lyon, France 2 Inserm U1028, CNRS UMR 5292, Lyon Neuroscience Research Centre, Team TIGER, Lyon, France 3 Université Claude Bernard Lyon 1, Centre Lyonnais d’Enseignement par la Simulation en Santé, SAMSEI, Lyon, France 4 Hospices Civils de Lyon, Hôpital Pierre Wertheimer, Department of Functional Neurology and Epileptology, Lyon, France 5 CREATIS, CNRS UMR 5220INSERM U1044, Université Lyon 1, Villeurbanne, France 6 Inserm U1028, CNRS UMR 5292, Lyon Neuroscience Research Centre, Team IMAPCT, Lyon, France 7 Inserm U1028, CNRS UMR 5292, Lyon Neuroscience Research Centre, Team NEUROPAIN, Lyon, France * Corresponding author: Dr Baptiste Balança, Department of neurologic anesthesiology and intensive care medicine, Hôpital Pierre Wertheimer, Hospices Civils de Lyon, Lyon, France. Tel: +33 6 23 91 05 94 mail: [email protected] †

This study was performed in the Department of anesthesiology and intensive care medicine of the Pierre Wertheimer Hospital, Hospices Civils de Lyon, Lyon, France.

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Abstract Objective Delayed cerebral ischemia (DCI) is the most important and preventable morbidity cause after subarachnoid hemorrhage (SAH). Therefore, DCI early detection is a major challenge. Yet, neurological examination can be unreliable in poor grade SAH patients. EEG provides information from most superficial cortical area, with ischemia-related changes. This study aims at defining an alpha-theta/delta (AT/D) ratio decrease thresholds to detect DCI. Methods We used EEG with a montage matching vascular territories (right and left anterior central and posterior) and compared them to follow-up brain imaging. Results 15 SAH patients (Fischer≥3, World Federation of Neurological Surgeons scale ≥4, 9 DCI) were monitored during 6.4 [4-8] days (min=2d, max=13d). AT/D changes could follow three different patterns: (1) prolonged or (2) transient decrease and (3) no decrease or progressive increase. A regional 30% decrease outlasting 3.7h reached 100% sensitivity and 88.9% specificity to detect DCI. Only 22.6% were in a zone of uncertain diagnosis (3.7-8.04h). These prolonged decreases, with a loss of transient changes, started in cortical areas evolving toward DCI, and preceded intracranial changes when available. Conclusion Although this study has a small sample size, prolonged AT/D decrease seems to be a reliable biomarker of DCI. Significance cEEG changes are likely to precede cerebral infarction and could be useful at the bedside to detect DCI before irreversible damage.

Highlights 1. An EEG montage matching vascular territories is relevant to monitor subarachnoid hemorrhage patients. 2. Prolonged alpha-theta/delta ratio (AT/D) decrease starting in one territory seems to be a good biomarker of delayed cerebral ischemia. 3. Other AT/D patterns and EEG changes can complement daily neurological examination.

Keywords: delayed cerebral ischemia, alpha-theta/delta ratio, continuous electroencephalography, subarachnoid hemorrhage.

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Abbreviations AT/D alpha-theta/delta ratio cEEG continuous electroencephalography CT computed tomography DCI delayed cerebral ischemia EVD External ventricular drainage EEG electroencephalography GCS Glasgow coma score ICP intra-cranial pressure MRI Magnetic resonance imaging NICU Neurological Intensive Care Unit PbtO2 partial brain tissue oxygen tension SAH sub-arachnoid hemorrhage TCD Transcranial Doppler WFNS World Federation of Neurologic Surgeons

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1 Introduction Case fatality and morbidity of aneurysmal subarachnoid hemorrhage (SAH) decreased during the past decades (Nieuwkamp et al., 2009). Despite improvement of both its prevention (Pickard et al., 1989) and management strategies (Nieuwkamp et al., 2009; Connolly et al., 2012), delayed cerebral ischemia (DCI), which occurs between three to 21 days after bleeding (Charpentier et al., 1999), is the most important and preventable cause of SAH morbidity (Francoeur and Mayer, 2016). DCI pathophysiology is still debated and encompass proximal vasospasm, capillary vasospasm, and spreading depolarizations (Dreier, 2011; Francoeur and Mayer, 2016). In any event, new ischemia results from disturbances in brain homeostasis including hypoperfusion, toxic disturbance or metabolic supply-demand mismatch, that initiate the cascade leading to cell death (von Bornstädt et al., 2015). If these disturbances outlast a threshold duration, the so-called commitment point, neurons will die (Hossmann, 1994; Somjen, 2004). Conversely, neurons can survive if the tissue is re-perfused before the commitment point (Memezawa et al., 1992). Therefore, DCI early detection is a major challenge during SAH patients’ management, to grant physician more time to treat. DCI definition is based on clinical examination (i.e. new focal deficit or global neurological impairment) and/or a new cerebral infarction evidenced on follow-up computed tomography (CT) or magnetic resonance imaging (MRI) (Francoeur and Mayer, 2016). However almost 20% of patients are comatose with a Glasgow coma score (GCS) under eight (Quintard et al., 2016) making clinical evaluation unreliable among them, despite a greatest risk of DCI (Charpentier et al., 1999). Transcranial doppler (TCD) has been proposed to monitor large vessels spasm as a surrogate of DCI, regardless of clinical exam. However, it cannot measure small vessels narrowing and has only a 45-80% sensitivity and a 78-84% specificity to predict proximal vasospasm depending on the artery (Miller and Armonda, 2014). Thus, in these poor-grade patients, DCI detection relies mostly on brain imaging as direct proofs of deterioration are usually missed. Therefore, silent infarction occurs in up to 30% patients (Helbok et al., 2011). Unlike regional monitoring (e.g. brain oxygen tension PbtO2 or cerebral microdialysis), electroencephalography (EEG) is a non-invasive technique providing functional information from most superficial cortical area, with ischemia-related changes occurring before the commitment point (Symon et al., 1977; Sundt et al., 1982). Cerebral blood flow (CBF) decrease, may result in polymorphic delta, and attenuation of fast activity (Cohn and Raines, 1948; Claassen et al., 2006). Moreover, these changes can be reversible for example after thrombolysis of an ischemic stroke, supporting the assumption that EEG changes occur before the commitment point (Finnigan and van Putten, 2013). Since the advance of digital EEG, time-frequency analysis has been applied to continuous EEG (cEEG) signals to quantify EEG changes over time. For instance, changes in total EEG power, alpha, theta or delta power, alpha power variability index, alpha or theta/delta power ratio were evaluated to detect ischemia after SAH (Tab.1). The alpha/delta power ratio combines the decrease in fast activity and the increase in delta rhythm due to a CBF reduction. Indeed, several authors found this ratio to be the most sensitive and specific to predict DCI compared to single band power changes (decrease in alpha, theta, or global power, as well as increase in delta (Claassen et al., 2004; Gollwitzer et al., 2015; Rots et al., 2015)). Therefore, the most reliable parameter seems to be the alpha/delta power ratio. However, the topography of qEEG changes after SAH, and comparisons to DCI location on imaging, have not been investigated in detail previously. qEEG changes temporal course and relation in time to DCI occurrence have also not been well investigated. Hence, there is still a lack of a clear quantitative EEG pattern usable clinically to trigger therapeutic intervention at the bedside. Moreover, severity of SAH was moderate in most studies and very few data are available in comatose SAH patients in whom cEEG might be 4

most helpful (Claassen, et al., 2004b; Gollwitzer et al., 2015; Rots et al., 2015). In such comatose patients, the background activity is usually below the alpha band with an increase in posterior theta and diffuse delta power, at the expense of a reduction in posterior alpha power (Claassen et al., 2016). Such alpha slowing has been reported in several acute brain injuries, suggesting that in this situation the theta band includes normal brain oscillation as well as alpha slowing activity (Schleiger et al., 2017). Thus, we chose to study an alpha-theta/delta ratio (6-12/1-3Hz, AT/D) instead of the alpha/delta one. In the present study, we aimed at defining an AT/D decrease duration threshold as a biomarker of cerebral infarction on CT or MRI in comatose SAH patients. We used cEEG with a simplified montage related to relevant vascular territories (right and left anterior middle/central and posterior territories) and compared them to follow-up brain imaging (i.e. follow up CT and MRI as well as perfusion CT performed in case DCI was suspected). We hypothesized that the AT/D decreases slowly in the DCI area, and could be used as a surrogate of progressive CBF reduction before irreversible cerebral infarction. 2 Methods 2.1 Subjects This study took place in the neurological intensive care unit (ICU) at Lyon’s University Hospital, where cEEG is part of the usual care for epileptic event detection, and visual analysis of brain activity changes in comatose patient after acute brain injury, as suggested by international guidelines (Steiner et al., 2013; André-Obadia et al., 2015). Data were collected retrospectively from clinical database with post-analysis, and patient management was thus not influenced by this study. Therefore, according to French law (last version n°2004-806, 9 August 2004), this study did not need to be approved by a research ethics committee. It was conducted in accordance with the law on data protection (last version n°2004-801, 6 August 2004), and in accordance with the Helsinki Protocol, and European Union Directive 95/46/EC about data protection. This retrospective study included consecutive patients admitted in the neurological ICU, between October 2013 and January 2017, with poor grade SAH (classified as grade 4 and 5 according to the Hunt and Hess or the World Federation of Neurosurgical Societies (WFNS) grading scales (de Manoel et al., 2016)). The inclusion criteria were: (1) SAH with WFNS ≥ 4 (2) GCS < 8 after aneurysm treatment ± external ventricular drainage (EVD) (3) available cEEG set-up after aneurysm treatment. cEEG was discontinued as patients recovered consciousness enabling clinical evaluation for the detection of new neurological deficit. Quantitative cEEG analyses were computed post-hoc and were not used to influence clinical decision making. Other monitoring data were also available post-hoc hourly. Patients were managed according to international SAH treatment guidelines (Connolly et al., 2012; Steiner et al., 2013). DCI prevention included daily oral nimodipine. DCI and vasospasm detection involved clinical examination, TCD as well as PbtO2 (using a flexible polarographic Licox Clark-type probe; LICOX; Integra Neurosciences, Kiel, Germany) implanted in an area most at risk of DCI if possible or in the right frontal lobe (Tholance et al., 2017). Induced hypertension and volume optimization were the first lines treatments of newly developing vasospasm, or neurological impairment. Apart from during intra-cranial hypertension or respiratory failure, patients did not receive continuous sedation. Patientventilator asynchrony were managed with low doses of remifentanil continuous infusion (<1.5µg.kg-1.min-1). 2.2 DCI definition CT were obtained routinely (a) at admission, (b) after aneurysm treatment, (c) following intracranial monitoring or EVD placement, (d) after any neurological change or unexplained, 5

sustained intracranial pressure elevation and (e) at distance from the hemorrhagic event to evaluate the brain injury. Perfusion CT was performed prior to arteriography, when physicians suspected DCI or vasospasm (GCS deterioration, increase in velocities evidenced by TCD, PbtO2 decrease). MRI were also availed in survivors to evaluate brain injuries at distance from the hemorrhagic event. We chose to define DCI as a new cerebral infarction instead of a change in clinical examination since all patients had consciousness deficit (GCS < 8) with an unreliable exam. DCI was defined as any cerebral infarction objectified by a new hypo-density on follow-up CT which was not present between 24 and 48h after early aneurysm occlusion, not attributable to other causes such as surgical clipping or endovascular treatment and detected within six weeks post SAH (Vergouwen et al., 2010; Francoeur and Mayer, 2016). Thus, small punctiform ischemia likely the consequence of embolism during endovascular procedure were not consider as DCI. Brainstem and small white matter infractions, without cortical involvement, were also not classified as DCI. 2.3 cEEG Acquisition cEEG was performed using the BRAIN QUICK video bedside monitoring system (Micromed, Treviso Italy) with a 256Hz sampling rate, 50Hz notch filter, and a 24 bits analog/digital conversion. We used a simplified 10-20 montage (i.e. Fp2, F4, C4, T4, P4, O2, Fp1, F3, C3, T3, P3 and O1 channels) with silver cup reusable electrodes (PACTRONIC, Lyon, France). The reference electrode was placed on the nasion and the ground on the head front (close to Fpz location). 2.4 EEG analyses and AT/D calculation cEEG signals and spectral analysis (i.e. fast Fourier transformation and power spectral density calculation) were performed with SystemPlus Evolution software (Micromed, Macon, France). The high and low pass filters were set at 0.53Hz and 60Hz respectively. The AT/D was calculated as the absolute alpha-theta (6-12Hz) power divided by the absolute delta power (1-3Hz). The AT/D was computed on sequential 30sec window devoid of artifacts on each bipolar derivation: Fp2-F4, Fp1-F3, C4-T4, C3-T3, P4-O2, P3-O1. Although the electrodes were distant from each other, their placement were in front of 6 different cortical regions matching vascular territories: anterior circulation covering the frontal cortex were monitored by Fp2-F4 and Fp1-F3, middle cerebral artery circulation covering the parieto-temporal cortex by C4-T4 and C3-T3, and the posterior circulation covering the parieto-occipital cortex by P4-O2 and P3-O1. Therefore, the location of DCI seen on brain imaging could be compared to these territories. Movement artifact due to nursing procedures were usually marked on EEG recordings by nurses and removed from the analyses. Other movement artefact as well as electrode detachment lead to high AT/D with larger changes compared to physiological fluctuation. Thus, every EEG 30s windows with AT/D changes exceeding 5 standard deviation of a 1h sliding window was visually checked for artifact and eventually removed from the analyses. Large Ocular artefacts were not seen since all patients were spontaneously comatose with their eyelid closed. Ocular movement occurred mostly during nursing procedure and were thus removed from analyses. Using this approach, we had in all EEG recordings of the 15 patients between 451 and 2880 30s EEG windows available per 24h (i.e. 2541 [1930 – 2720]) The AT/D ratio displayed low amplitude changes with a 5min to 15min period. Yet previous studies investing EEG after SAH showed that changes outlasting a few hours were linked to DCI that is supposed to be a slow process (Kondziella et al., 2015). For that matter we were more interested in changes enduring more than 1h and applied a 1h low pass filter to AT/D time decay. Then we computed the AT/D percentage of changes compared to baseline 6

([AT/D-baseline]*100/baseline). Baseline levels preceding each AT/D value were defined as the AT/D median during the earlier 48h recordings if available or from cEEG onset (i.e. during the first 48h recording). AT/D changes were classified into different patterns by a neuro-intensivist trained in clinical neurophysiology (B.B.) and a neurophysiologist (N.A.O) looking at consecutives 8h time windows (i.e. maximal length of a continuous recording before the system automatically starts a new one): (1) Transient changes were defined as several AT/D increases and decreases within an 8h window; (2) Prolonged decrease was defined as any AT/D progressive decrease enduring more than 4 hours; (3) Increase was define as a progressive AT/D increase over the 8h window; and (4) no AT/D changes. The ROC analyses of the AT/D decrease duration were intended to give a more precise cut-off to distinguish transient from prolonged changes. Transient AT/D changes frequencies and amplitudes were analyzed with a fast Fourier transform on 8h windows: either on the derivation showing a prolonged decrease before its occurrence, or on the derivation with the largest changes. 2.5 Statistical analysis AT/D were compare between derivation with a Friedman test with a Conover post-hoc test using a Bonferroni correction. Other quantitative variables were compared with a Wilcoxon test. Five ROC curves were calculated for AT/D decrease over 10%, 30%, 50%, 70% and 90% under baseline, in at least one bipolar derivation. This exploratory approach intended to define the best decrease duration threshold using a pre-specified DCI definition based on CT and distant MRI (Connolly et al., 2012; Steiner et al., 2013; Francoeur and Mayer, 2016). Best duration thresholds were defined as the closest top-left point on the ROC curve so as to minimize ((1 - sensitivities)^2 + (1- specificities)^2). 95% confident intervals (CI95) were computed with a bootstrapping algorithm. Biomarkers help to stratify the risk of a condition and guide clinical decision making. Therefore, rather than a single cut-off which dichotomize the population, another approach is to asses a grey zone with diagnostic uncertainty allowing for a certain continuum in risk stratification. The first cutoff is chosen to include the diagnosis with near-certainty (i.e. privilege sensitivity). The second cut-off is chosen to exclude the diagnosis with nearcertainty (i.e., privilege specificity). When values of the biomarker fall into the grey zone between the two cutoffs, uncertainty exists, and the physician should pursue a diagnosis using additional tools. Thus, we defined these two limits as a short duration threshold with a sensitivity >90% (lower limit, privilege sensitivity) and a long one with a specificity > 90% (upper limit, privilege specificity) (Ray et al., 2010). AT/D were computed automatically from EEG data before clinical information and reference standard results (CT or MRI) were available to the assessor. Signal processing and statistical analysis were performed with the R software (R Foundation for Statistical Computing, Vienna, Austria, version 3.3.1). Receiver operator curves (ROC) calculations were computed using the pROC library (Robin et al., 2011), and signal processing with the signal library. Data will be presented as their median and [interquartile range (0.25-0.75)].

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3 Results 3.1 Patients Fifteen SAH patients (54 [48-57] years old, 3 males) were monitored with cEEG during 6.4 [4-8] days (min=2d, max=13d). cEEG recordings started 3 [2-4] days after bleeding (min=1d, max=11d). SAH patients were classified as Fisher 3 (n=1) or 4 (n=14) and WFNS 4 (n=8) or 5 (n=7), with a 46.7 [42-50.3] IGSII score. 60% (n=9/15) patients had DCI (Tab.2) and 73% (n=11/15) needed EVD. Patients 5, 7 and 14 underwent hematoma evacuation, 5 and 7 with decompressive craniectomy (Tab.2). Patient 5 had aneurysm clipping, while other patients had aneurysm coiling during endovascular procedure. Patients 2, 10 and 14 had ischemic lesions on delayed MRI that were not considered as DCI. These lesions encompassed brainstem, white matter or insular punctiform lesion likely due to embolism during the endovascular procedure although not seen in post-procedure CT (Tab.2). Background activity at cEEG onset was assessed by a neurophysiologist (N.A.O.) in the 6 bipolar derivations and are presented in Table 3. The main oscillation frequency was between 5 and 8Hz. 9/15 patients had superimposed intermittent diffuse delta waves. No significant difference between territories was noted by the assessor although the AT/D were different between derivations (p=0.005). The AT/D was lower in the anterior derivations (0.29 [0.160.44]) compared to the central (0.38 [0.22-0.51], p<0.001) and posterior ones (0.45 [0.21-0.6], p<0.001). Patients 1, 4, 5 and 7 (Tab.2 and 3) had first AT/D asymmetry due to intracranial or subdural hematoma. The asymmetry in patient 12 was not the correlate of structural lesions or epileptic events. Patients 5 and 6 (Tab.2) displayed spikes without discharges. Only patient 6 (Tab.2) had recurrent discharges with one non-convulsive seizures during the first monitoring day with the need of levetiracetam and lacosamide treatment. This short-lasting event did not significantly change the AT/D. 3.2 Qualitative analysis Qualitative AT/D analyses revealed three different patterns: (1) prolonged decrease, (2) transient decrease and (3) no decrease or progressive increase, as defined in the method section 2.4. AT/D transient decrease were seen in 12 over 15 patients with or without DCI (Tab. 3). They occurred in all derivations at the same time, with a period of 160 [120-240] min. In patients who presented DCI, transient changes were more frequent before prolonged AT/D decrease (Fig.1 and 2). Their maximal spectral amplitude was lower during a prolonged AT/D decrease (p=0.04). Indeed, prolonged AT/D decrease (associated to a disappearance of the usual transient changes) were always seen in patients with DCI (Tab. 3). Finally, AT/D increase in all derivations occurred in patients 2, 6 and 14, followed at least one day later by an improvement in neurological examination (Fig.3). Lateralized periodic discharges were sometimes seen within a few hours after AT/D decrease (Tab.2). 3.3 Quantitative analysis The study aimed at defining the optimal thresholds for both the AT/D percentage of decrease and its duration associated to the existence of a cortical infarction. Among the percentage of decrease thresholds, over 30% and 50% decrease had the best area under the curve of ROC analysis, respectively of 94.4 [82.3-100] and 92.6 [79.4-100]. Their best duration thresholds with sensitivity, specificity and likelihood ratio are reported in the table 4 and were not significantly different. As biomarker thresholds belong to a continuum, we used a grey zone approach to stratify the risk of DCI. The grey zones for the 30% duration threshold (3.7h 8.04h) included 26.7% of patient, and 33.3% for the 50% one (0.9h - 2.6h). The more patients in this grey area of diagnosis uncertainty, the less useful the biomarker. These thresholds can 8

be used to separate transient from prolonged AT/D changes at the bedside. If a threshold is reached too often it can lead to imaging overuse and therefore not be relevant for clinicians. Thus, we analyzed how often each best threshold were reached. the 30% duration threshold (3.7h) was reached 0.33 [0.13-0.5] times a day for a maximum duration of 10.6h [6.5-24.1], and the upper limit of the grey zone (8.04h) was reach less often (i.e. 0.12 [0-0.24] time a day, p=0.009). the 50% duration threshold was shorter (1.4h) and reached more often (i.e. 0.67 [0.26-0.7] times a day, p=0.009), for a maximum time of 4.8h [2.7-6.1]. In 6/9 patients the AT/D decrease started in the same region as DCI and preceded cortical infarction identification by at least one day (Tab.2, Fig.1 and 2). Brain imaging were done after aneurysm treatment ± EVD placement and at distance from the hemorrhagic event to evaluate the brain injury. MRI or perfusion CT were also performed if DCI was suspected based on intracranial monitoring (i.e. PbtO2 and ICP, continuous data), clinical examination (e.g. GCS every 4h), and TCD made every 12h and in case of clinical or intracranial monitoring worsening. Patient 1 had a first bilateral MCA proximal spasm more pronounced on the right side, and AT/D started to decrease in Fp2F4 and C4T4 derivations. Diffuse AT/D prolonged decrease was observed in case of intracranial hypertension (e.g. patient 11 and 12, Tab. 2 and 3), multiple lesions (e.g. Patient 9, Tab. 2 and 3), or could be a false positive (i.e. no DCI like in patient 14, Tab. 2 and 3). When intracranial monitoring data were available, the changes occurred after cEEG ones, except for patient 12 in whom an ICP increase lead to a global AT/D decrease (Tab2).

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4 Discussion 4.1 AT/D decrease duration threshold In this observational retrospective study 9/15 (60%) patients developed DCI, matching SAH severity (Fisher≥3, WFNS≥4). Indeed, all patients had consciousness impairment (GCS <8), and 7 of them died in the ICU (Tab. 3). On cEEG onset, normal alpha activity or anteroposterior differences were not present. Instead, background activity was between 5 and 8Hz reflecting an alpha-slowing to the theta band, with superimposed intermittent delta waves. Background or AT/D asymmetry was observed in case of intracranial hematoma, therefore we analyzed AT/D changes relative to a baseline. During qEEG monitoring, AT/D changes followed three different patterns: (1) prolonged decrease most likely linked to DCI, (2) transient decrease and (3) stability or increase. Since prolonged decrease was seen in patients with DCI we were interested in defining an AT/D decrease duration threshold beyond which the diagnosis of DCI would be probable. We found that AT/D decrease duration thresholds under 30% or 50% from baseline outlasting respectively 3.7h or 1.4h, best predicted DCI. Although 30% and 50% duration threshold’s AUC were not significantly different, an AT/D decrease under 30% tended to predict DCI with a better sensitivity with a lower number of patients in the grey zone, meaning that it would be more useful at the bedside with less diagnosis uncertainty. Since the consequences of false negative is high (i.e. missing DCI, leading to outcome worsening), a good sensitivity should be preferred over specificity: In other words, the threshold chosen to trigger usual first line treatment of DCI (induced hypertension, volume optimization (Francoeur and Mayer, 2016)) should have a high sensitivity like the lower grey zone’s limit (which privilege sensitivity, i.e. Se > 90%). If the AT/D decrease persists over the upper grey zone limit (which privilege specificity, i.e. Sp > 90%), this could trigger further brain imaging for the purpose of endovascular rescue therapy. Nevertheless, if a duration threshold is reached too often, as under 50% from baseline, it could lead to imaging overuse, and become less relevant for clinicians. Then, an AT/D decrease under 30% from baseline outlasting 3.7h seems to be the best biomarker as (1) it is the best threshold (2) it is also the lower limit of the grey zone which has a high sensitivity (3) less patients were in the grey zone of diagnosis uncertainty, and (4) it is not reach too often meaning that it would be relevant at the bedside to trigger first line therapy. Moreover, processes underlying DCI are progressively injuring brain parenchyma. Then they are likely to lead to long-lasting AT/D decrease starting in one cortical area and spreading to others. Indeed, most AT/D decreases occur in derivations close to cortical area that will evolve toward ischemia, and then spread to surrounding ones supporting this hypothesis of a progressive spreading process. Altogether, these data support that AT/D decrease beyond 30% for more than 3.7h distinguished prolonged decrease linked to DCI from transient changes. These results are consistent with high sensitivity and specificity also reported by other authors (Claassen, et al., 2004a; Gollwitzer et al., 2015; Rots et al., 2015). The selection of severe poor grade SAH with consciousness impairment can also account for the high sensitivity compared to prior study using similar duration threshold in moderate severity SAH patients (i.e. 40% decrease outlasting 5h (Gollwitzer et al., 2015)). Indeed, AT/D can also be influence by sleep feature and EEG fluctuation, which induce prolonged increase in delta activity and are more frequent in patients with greater outcome (Vespa et al., 1997; Claassen et al., 2006). The circadian rhythm is a physiological oscillation observed for several signals, either biological (such as melatonin/cortisol) or clinical (such as temperature). Neurophysiological assessment could also be dependent from this synchronization to the day-night time (approximately 24h+/-30min) which is genetically determined by a complex interdependency of genomic-proteomic system of oscillation at the cellular level in dedicated hypothalamic 10

neurons. This endogenous oscillation can however be altered in case of direct lesion of the hypothalamic generator of this rhythm (the Supra-Chiasmatic Nucleus) or in case of abnormal or absent information from the exogenous synchronizer (Zeitgeber such as light or social time, food, every factor the subject undergoing a constant routine protocol are deprived of (Czeisler and Gooley, 2007)). Thus, it is plausible that, in absence of functional circadian rhythm (with a near 24h oscillation), a repressed ultradian rhythm (with a less 24h oscillation) could be observed. Indeed, circadian rhythm are often lost in the ICU especially in acutely brain injured patients with only ultradian changes (4-8h (Paul and Lemmer, 2009)). It has also been described in comatose patient (either after lesional cause or functional transient dysfunction) to be a 70 min cycle for minimally conscious patients (Piarulli et al., 2016). In acute comatose state, the periods might be more heterogeneous because the mechanisms of wakefulness compensation are still at work. Despite the absence of sleep features, our patients presented transient AT/D changes with a median 160min period. Although these changes are slower than entropy or theta power oscillations reported in minimally conscious patients (i.e. 70min), they might also reflect ultradian awareness fluctuation (Piarulli et al., 2016). This pattern of transient ultradian changes was present outside of AT/D prolonged decrease or before neurological improvement. Then, in patients with a poor neurological examination, AT/D increase or transient changes (Fig 3), as well as other EEG features (e.g. alpha amplitude, or fronto-central coherence) can complement behavioral assessment at the bedside to evaluate neurological improvement (Claassen et al., 2016). 4.2 Study limitations This retrospective study included only very severe patients leading to a selection bias and can explain the high rate of DCI observed in this population (60%). Moreover, the small number of patients (n=15) can account for an overestimation of the diagnosis performance of this biomarker, as delineated by the large 95CI. Therefore, the diagnostic performance of the AT/D might not be as high in a larger population, especially if including less severe patients in whom sleep feature are preserved and movement artefact are more frequent. Finally, our small EEG montage reduce the amount of monitored cortical matter and did not allowed us to separate signal coming from close cortical areas. Instead we chose to analyze bipolar derivations covering large surfaces in the anterior (Frontal lobes), middle (parietotemporal lobes) or posterior (parieto-occipital lobes) vascular territories. These territories were overlapping and indeed the AT/D prolonged changes quickly involved several adjacent derivations. Therefore, a denser montage should lead to a better spatial resolution, although it could be more challenging for the staff to keep all electrodes working for a long period of time. 4.3 Other EEG biomarkers Patients with altered consciousness in the ICU are likely to have epileptic events that can be missed by a 30 min EEG (Claassen et al., 2006; Khan et al., 2014; André-Obadia et al., 2015). Indeed, non-convulsive seizures occur in 7-30% of SAH patients (Lindgren et al., 2012; Kondziella et al., 2015; Claassen et al., 2016). This incidence seems to be low in patients needing mechanical ventilation (Lindgren et al., 2012) and in the present cohort (2/12 patients), possibly due to the use of sedative drugs for intubation, pneumonia, or intracranial hypertension. 6/15 patients (4/9 with and 2/6 without DCI on CT) also had lateralized or generalized periodic discharges which are known to be predicative of worst outcome and DCI in SAH patients (Claassen et al., 2006; Kim et al., 2017). Spreading depolarization recorded on subdural electrodes are one of the main event underlying DCI (Dreier, 2011; Lauritzen et al., 2011). The concurrent depression of brain activity can be seen on scalp EEG, sometimes spreading from one electrode to adjacent ones 11

(Drenckhahn et al., 2012; Hartings et al., 2014). We did not see such spreading depression of brain activity but instead we observed the diffusion of the AT/D ratio from a focal phenomenon to a diffuse one over several derivations. However, one could argue that this simplified montage with bipolar derivations covering large cortical area did not allow us to evidence depression of brain activity spreading across restricted cortical area in a gyrencephalic brain (Santos et al., 2014). 4.4 DCI detection at the bedside The actual definition of DCI relies on clinical examination with a new focal or global impairment, and/or new cerebral infarction on CT or MRI (Francoeur and Mayer, 2016). Then, the detection of DCI before reaching the commitment point leading to irreversible cerebral ischemia is challenging in patients with consciousness impairment. TCD has been proposed as a non-invasive technique to detect proximal vasospasm with a 45-80% sensitivity and a 78-84% specificity, depending on the artery (Miller and Armonda, 2014). However, TCD cannot explore posterior as well as distal vasculature. Moreover, it is unclear whether DCI is the consequence of large artery vasospasm or if it is more underpinned by small vessels constriction as during spreading depolarizations (Woitzik et al., 2012; Dreier and Reiffurth, 2015). Likewise, patient in Fig 1 presented a right middle cerebral artery vasospasm on TCD while perfusion CT showed a left DCI. In this patient, cEEG showed four days earlier a left AT/D prolonged decrease. Unlike TCD, cEEG offers continuous real-time information of most superficial cortical areas, with reliable quantitative bio-markers of DCI. Nevertheless, we chose not to consider as DCI sub-cortical lesions (i.e. brainstem or white matter) revealed by MRI, but not seen on post aneurysm treatment CT. Indeed, those lesions did not lead to prolonged AT/D decrease, and it is possible that some small infarcts occurred during endovascular procedure prior to cEEG onset. Finally, multimodal monitoring (ICP, PbtO2, and cerebral micro-dialysis) also gives continuous information, and are often used in acutely brain injured patients to target optimal CBF, glycemic control of oxygen therapy, in order to prevent or limit secondary brain injuries (Carteron et al., 2017; Okonkwo et al., 2017). Even if it appears to change after cEEG, intracranial monitoring is useful to improve detection of physiological derangements and offers both a trigger and a target for intervention (Tholance et al., 2015, 2017; Francoeur and Mayer, 2016). Therefore, in addition to continuous EEG, intra-cranial monitoring should also be considered and implanted in brain area corresponding to aneurysm location: i.e. anterior cerebral arteries territory for anterior communicating artery aneurysms and ipsilateral middle cerebral artery territory for middle cerebral artery or internal carotid artery ones. However, it is unclear whether this monitoring is useful to detect DCI after posterior or vertebrobasilar arteries aneurysms rupture because its implantation is not possible in area most at risk of new infarction (Tholance et al., 2017). 5 Conclusion In poor grade SAH patient with impaired consciousness, a poor neurological exam can lead to use of cEEG as a surrogate marker to detect DCI and check for improvement at the bedside. A prolonged AT/D decrease appears to be the most reliable parameter associated with cerebral infarction. Most importantly, cEEG changes were preceding that of the multimodal monitoring or the confirmation of infarction on CT. Therefore, we propose that AT/D decrease duration threshold might be reached before the commitment point. Altogether, these findings pave the way for further clinical trials to figure out if a strategy aiming at detecting earlier DCI based on the AT/D could prevent irreversible cerebral damage.

12

Conflict of Interest Statement This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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References André-Obadia N, Parain D, Szurhaj W. Continuous EEG monitoring in adults in the intensive care unit (ICU). Neurophysiol Clin. 2015;45(1):39–46. Von Bornstädt D, Houben T, Seidel J, Zheng Y, Dilekoz E, Qin T, et al. Supply-Demand Mismatch Transients in Susceptible Peri-infarct Hot Zones Explain the Origins of Spreading Injury Depolarizations. Neuron. 2015;85(5):1117–31. Carteron L, Bouzat P, Oddo M. Cerebral Microdialysis Monitoring to Improve Individualized Neurointensive Care Therapy: An Update of Recent Clinical Data. Front Neurol. 2017;8:601. Charpentier C, Audibert G, Guillemin F, Civit T, Ducrocq X, Bracard S, et al. Multivariate analysis of predictors of cerebral vasospasm occurrence after aneurysmal subarachnoid hemorrhage. Stroke. 1999;30(7):1402–8. Claassen J, Hirsch L, Frontera J, Fernandez A, Schmidt M, Kapinos G, et al. Prognostic significance of continuous EEG monitoring in patients with poor-grade subarachnoid hemorrhage. Neurocrit care. 2006;4(2):103–12. Claassen J, Hirsch LJ, Kreiter KT, Du EY, Connolly SE, Emerson RG, et al. Quantitative continuous EEG for detecting delayed cerebral ischemia in patients with poor-grade subarachnoid hemorrhage. Clin Neurophysiol. 2004a;115(12):2699–710. Claassen J, Velazquez A, Meyers E, Witsch J, Falo C, Park S, et al. Bedside quantitative electroencephalography improves assessment of consciousness in comatose subarachnoid hemorrhage patients. Ann Neurol. 2016;80(4):541–53. Claassen J, Vu A, Kreiter K, Kowalski R, Du EY, Ostapkovich N, et al. Effect of acute physiologic derangements on outcome after subarachnoid hemorrhage. Crit Care Med. 2004b;32(3):832–8. Cohn R, Raines G. Cerebral vascular lesions; electroencephalographic and neuropathologic correlations. Archives of neurology and psychiatry. Arch Neurol Psychiatry. 1948;60(2):165– 81. Connolly S, Rabinstein A, Carhuapoma R, Derdeyn C, Dion J, Higashida R, et al. Guidelines for the Management of Aneurysmal Subarachnoid Hemorrhage A Guideline for Healthcare Professionals From the American Heart Association/American Stroke Association. Stroke. 2012;43(6):1711–37. Czeisler CA, Gooley JJ. Sleep and circadian rhythms in humans. Cold Spring Harb. Symp. Quant Biol. 2007;72:579–97. Dreier J. The role of spreading depression, spreading depolarization and spreading ischemia in neurological disease. Nat Med. 2011;17(4):439–47. Dreier JP, Reiffurth C. The stroke-migraine depolarization continuum. Neuron. 2015;86(4):902 922. Drenckhahn C, Winkler M, Major S, Scheel M, Kang E-J, Pinczolits A, et al. Correlates of spreading depolarization in human scalp electroencephalography. Brain. 2012;135(3):853–68. Finnigan S, van Putten MJ. EEG in ischaemic stroke: quantitative EEG can uniquely inform (sub-)acute prognoses and clinical management. Clin Neurophysiol. 2013;124(1):10–9. Francoeur C, Mayer S. Management of delayed cerebral ischemia after subarachnoid hemorrhage. Crit Care. 2016;20(1):277. Gollwitzer, Groemer, Rampp, Hagge, Olmes, Huttner HB, et al. Early prediction of delayed cerebral ischemia in subarachnoid hemorrhage based on quantitative EEG: A prospective study in adults. Clin Neurophysiol. 2015;126(8):1514–23. Hartings J, Wilson, Hinzman J, Pollandt S, Dreier J, DiNapoli V, et al. Spreading depression in continuous electroencephalography of brain trauma. Ann Neurol. 2014;76(5):681–94.

14

Helbok R, Madineni R, Schmidt M, Kurtz P, Fernandez L, Ko S-B, et al. Intracerebral monitoring of silent infarcts after subarachnoid hemorrhage. Neurocrit Care. 2011;14(2):162– 7. Hossmann K. Viability thresholds and the penumbra of focal ischemia. Ann Neurol. 1994;36(4):557 565. Khan O, Azevedo C, Hartshorn A, Montanye J, Gonzalez J, Natola M, et al. A comparison of continuous video-EEG monitoring and 30-minute EEG in an ICU. Epileptic Disord. 2014;16(4):439–48. Kim J.A., Rosenthal E.S., Biswal S, Zafar S, Shenoy A.V., O’Connor K.L., et al. Epileptiform abnormalities predict delayed cerebral ischemia in subarachnoid hemorrhage. Clin Neurophysiol. 2017;(6):1091–9. Kondziella D, Friberg C, Wellwood I, Reiffurth C, Fabricius M, Dreier J. Continuous EEG Monitoring in Aneurysmal Subarachnoid Hemorrhage: A Systematic Review. Neurocrit Care. 2015;22(3):450–61. Lauritzen M, Dreier J, Fabricius M, Hartings J, Graf R, Strong A. Clinical relevance of cortical spreading depression in neurological disorders: migraine, malignant stroke, subarachnoid and intracranial hemorrhage, and traumatic brain injury. J Cereb Blood Flow Metab. 2011;31(1):17–35. Lindgren C, Nordh E, Naredi S, Olivecrona M. Frequency of non-convulsive seizures and non-convulsive status epilepticus in subarachnoid hemorrhage patients in need of controlled ventilation and sedation. Neurocrit Care. 2012;17(3):367–73. De Manoel A, Goffi A, Marotta T, Schweizer T, Abrahamson S, Macdonald L. The critical care management of poor-grade subarachnoid haemorrhage. Crit Care. 2016;20(1):21. Memezawa H, Smith M.L., Siesjö B.K.. Penumbral tissues salvaged by reperfusion following middle cerebral artery occlusion in rats. Stroke. 1992;23(4):552–9. Miller C, Armonda R. Monitoring of Cerebral Blood Flow and Ischemia in the Critically Ill. Neurocritical Care. Neurocrit Care. 2014;21(S2):121–8. Nieuwkamp D, Setz L, Algra A, Linn F, de Rooij N, Rinkel G. Changes in case fatality of aneurysmal subarachnoid haemorrhage over time, according to age, sex, and region: a metaanalysis. Lancet Neurol. 2009;8(7):635–42. Okonkwo D, Shutter L, Moore C, Temkin N, Puccio A, Madden C, et al. Brain Oxygen Optimization in Severe Traumatic Brain Injury Phase-II: A Phase II Randomized Trial*. Crit Care Med. 2017;45(11):1907. Paul T, Lemmer B. Disturbance of Circadian Rhythms in Analgosedated Intensive Care Unit Patients with and without Craniocerebral Injury. Chronobiol Int. 2009;24(1):45–61. Piarulli A, Bergamasco M, Thibaut A, Cologan V, Gosseries O, Laureys S. EEG ultradian rhythmicity differences in disorders of consciousness during wakefulness. J. Neurol. 2016;263(9):1746–60. Pickard J, Murray G, Illingworth R, Shaw M. Effect of oral nimodipine on cerebral infarction and outcome after subarachnoid haemorrhage: British aneurysm nimodipine trial. Bmj. 1989;298(6):636–42. Quintard H, Leduc S, Ferrari P, Petit I, Ichai C. Early and persistent high level of PS 100β is associated with increased poor neurological outcome in patients with SAH: is there a PS 100β threshold for SAH prognosis? Crit Care. 2016;20(1):33. Ray P, Manach Y, Riou B, Houle T. Statistical Evaluation of a Biomarker. Anesthesiology. 2010;112(4):1023. Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez J-C, et al. pROC: an opensource package for R and S+ to analyze and compare ROC curves. Bmc Bioinformatics. 2011;12(1):1–8.

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Rots ML, van Putten MJ, Hoedemaekers CW, Horn J. Continuous EEG Monitoring for Early Detection of Delayed Cerebral Ischemia in Subarachnoid Hemorrhage: A Pilot Study. Neurocrit Care. 2016;24(2):207–16. Santos E, Schöll M, Sánchez-Porras R, Dahlem M, Silos H, Unterberg A, et al. Radial, spiral and reverberating waves of spreading depolarization occur in the gyrencephalic brain. NeuroImage. 2014;99:244–55. Schleiger E, Wong A, Read S, Rowland T, Finnigan S. Poststroke QEEG informs early prognostication of cognitive impairment. Psychophysiology. 2017;54(2):301–9. Somjen GG. Irreversible hypoxic (ischemic) neuron injury. In:Ions in the brain. OUP; 2004. Steiner T, Juvela S, Unterberg A, Jung C, Forsting M, Rinkel G, et al. European Stroke Organization Guidelines for the Management of Intracranial Aneurysms and Subarachnoid Haemorrhage. Cerebrovasc Dis. 2013;35(2):93–112. Sundt TJ, Sharbrough F, Piepgras D. Correlation of cerebral blood flow and electroencephalographic changes during carotid endarterectomy: with results of surgery and hemodynamics of cerebral ischemia. Survey of anesthesiology. 1982;56(9):533–43. Symon L, Branston N.M., Strong A.J., Hope T.D. The concepts of thresholds of ischaemia in relation to brain structure and function. J Clin Pathol Suppl. 1977;11:149–54. Tholance Y, Barcelos G, Dailler F, Renaud B, Marinesco S, Perret-Liaudet A. Biochemical neuromonitoring of poor-grade aneurysmal subarachnoid hemorrhage: comparative analysis of metabolic events detected by cerebral microdialysis and by retrograde jugular vein catheterization. Neurol Res. 2015;37(7):578–87. Tholance Y, Barcelos GK, Perret-Liaudet A, Omar E, Carrillon R, Grousson S, et al. Placing intracerebral probes to optimise detection of delayed cerebral ischemia and allow for the prediction of patient outcome in aneurysmal subarachnoid haemorrhage. J Cereb Blood Flow Metab. 2017;37(8):2820–32. Vergouwen M, Vermeulen M, van Gijn J, Rinkel G, Wijdicks E, Muizelaar, et al. Definition of delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage as an outcome event in clinical trials and observational studies: proposal of a multidisciplinary research group. Stroke. 2010;41(10):2391–5. Vespa P, Nuwer M, Juhász C, Alexander M, Nenov V, Martin N, et al. Early detection of vasospasm after acute subarachnoid hemorrhage using continuous EEG ICU monitoring. Electroencephalogr Clin Neurophysiol. 1997;103(6):607–15. Woitzik J, Dreier J, Hecht N, Fiss I, Sandow N, Major S, et al. Delayed cerebral ischemia and spreading depolarization in absence of angiographic vasospasm after subarachnoid hemorrhage. J Cereb Blood Flow Metab. 2012;32(2):203–12.

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Legends Table 1. Literature review of quantitative EEG study aiming at detecting DCI in SAH patients. A, Alpha power; D, Delta power; A/D, alpha/delta power ration; T, Theta power; Se, sensitivity; Sp, specificity. Severity is defined as moderate (i.e. most patients had Hunt and Hess < 4, or WFNS < 4) or severe (i.e. only patients with Hunt and Hess ≥ 4, or WFNS ≥ 4). *this study evaluated angiographic vasospasm instead of DCI, only 12/19 patients had ischemic lesions on final CT. Table 2. Patients characteristics. Patients 5, 7 and 14 underwent hematoma evacuation, 5 and 7 with decompressive craniectomy. Patient 5 had aneurysm clipping, while other patients had aneurysm coiling during endovascular procedure. ACoA, anterior communicating artery; LCA, left carotid artery; RCA, right carotid artery; RMCA, right middle cerebral artery; LMCA, left middle cerebral artery; LPCA, left posterior communicating artery; BA, basilar artery; IVB, intraventricular bleeding; IH, intracranial hypertension; ACA, anterior cerebral artery; LPD, lateralized periodic discharges; GPD, generalized periodic discharges; ICP, intracranial pressure; PbtO2, Brain tissue oxygen partial pressure. Table 3. cEGG background activity, qEEG patterns and outcome. ACA, anterior cerebral artery; DW, Intermittent Delta Waves; IH, intracranial hypertension; mRS, Modifier Rankin Scale; MCA, middle cerebral artery, TL: Collegial therapeutic limitation. AT/D patterns were T: Transient, P: prolonged (> 3.7h under 30% decrease), I: progressive increase. * DW of lower amplitude # DW more frequent on this derivation(s)

Table 4. ROC analyses. ROC analyses using 30% and 50% AT/D percentage of decrease, with best thresholds (i.e. least hours of AT/D decrease in at least one cEEG bipolar derivation). The grey zones of diagnostic uncertainty are presented with the percentage of patients within. On the right ROC curves are plotted with their respective best threshold and CI95. AUC, area under the curve; Se, sensitivity; Sp, specificity; PLR, positive likelihood ratio; NLR, negative likelihood ratio. Figure 1. Example of transient and prolonged AT/D changes. Data from Patient 3 (Tab2). (left CT image) CT on day 3 after bleeding with intraventricular blood an EVD. (middle panel) AT/D changes are plotted in black in the six different bipolar derivations facing vascular territories: right and left anterior (respectively Fp2F4, Fp1F3), central (C4T4, C3T3) and posterior (P4O2, P3O1). cEEG started on day 4 after bleeding. Blue: AT/D decrease under 30% from baseline. Red: AT/D decrease outlasting 3.7h under 30% from baseline. A zoom on the C3T3 AT/D highlight the transition from “blue” to “red”. (right CT images) CT performed on day 12 after bleeding (upper image), with the corresponding Mean Transit Time (MTT) image of the perfusion CT (lower image). Note the increased MTT (in blue) with a hypo density on the left hemisphere. (lower panel) EEG on day 7 after bleeding, before (EEG1, left) and during prolonged AT/D decrease (EEG2, right). 30s EEG pages, upper blue traces be electrodes on the right side (Fp2F4, C4T4 and P4O2) 17

and lower black traces on the left side (Fp1F3, C3T3, P3O1). ECG is plotted in red in the middle. Transient AT/D changes (with a 240min period) are present on all derivations before the prolonged AT/D decrease (in red) reached on C3T3 at day 8 after bleeding. At that time TCD did not showed proximal vasospasm, and the patient had fever attributed to a ventilatorassociated pneumonia. She opened her eyes to noxious stimulation and had bilateral spontaneous movements. On day 12 TCD showed a proximal spasm of the right middle cerebral artery, and a Perfusion CT was performed revealing DCI on the left hemisphere. Note that the large transient changes present on day 7 are hardly observed after day 8.

Figure 2. Example of prolonged AT/D changes due to cortical DCI. Data from Patient 13 (Tab2). (left images) CT on day 0 after SAH showing a hematoma in the right central sulcus with an ipsilateral subdural hematoma. Underneath is the postoperative CT on day1. (middle panel) AT/D changes are plotted in black in the six different bipolar derivations facing vascular territories: right and left anterior (respectively Fp2F4, Fp1F3), central (C4T4, C3T3) and posterior (P4O2, P3O1). Blue: AT/D decrease under 30% from baseline. Red: AT/D decrease outlasting 3.7h under 30% from baseline. (right images) CT performed on day 5 after bleeding (upper image), showing an hypodensity in the superior frontal gyrus. The perfusion images (not shown) showed a minor increase in MTT and reduction in CBF surrounding the central sulcus. The MRI performed on day 11 also showed a FLAIR hypersignal of the right frontal cortex. (lower panel) EEG on day 2 (EEG1, left) and 4 (EEG2, right) after bleeding, respectively before and during prolonged AT/D decrease: upper blue traces be electrodes on the right side (Fp2F4, C4T4 and P4O2) and lower black traces on the left side (Fp1F3, C3T3, P3O1). The perfusion CT was performed on day 5 because DCI was suspected based on a persistent increase in the right middle cerebral artery velocities on the TCD and high fever not related to sepsis. The angiography and perfusion images did not evidence significant vasospasm, but a small infarct on the frontal superior gyrus. Before clinical DCI suspicion, cEEG displayed from day 3 an AT/D decrease starting in Fp2F4 and spreading to C4T4, likely the consequence of the right frontal infarction onset. Figure 3. Example of AT/D progressive increase followed by transient changes. Data from patient 2 (Tab2). (upper left CT image) CT on the day of bleeding. (lower left CT image) CT on day 18 after bleeding with intraventricular blood an EVD. (upper middle panel) AT/D changes in left (grey) and right (purple) bipolar derivations. (lower middle panel) Glasgow Coma Scale (GCS) during the same period. The early AT/D global increase was followed by transient global AT/D changes with a 120min period (late day 4) and a GCS improvement on day 5.

18

19

20

Tables

Vespa et al. 1997

DCI/total number

SAH severity

Analysis window

19*/32

moderate

8-12h

A variability decrease

9/34

severe

15min

Post stimulation A/D >10% decrease > 1h30

Se=100%, Sp=76%

8/12

moderate

6h sliding window/30 min

combined A variability and power index

Se=67%, Sp=73%

9/18

moderate

5min/hour

A/D > 38% decrease

Se=100%, Sp=83.3%

Claassen et al. 2004

Rathakrishnan et al. 2011

EEG index

Rots et al. 2015

Gollwitzer 2015

et

al. 6/12

moderate

1min

A/D > 70% decrease > 2h T > 40% decrease > 6h

Table 1.

Predictive info Se=100%, Sp=50%

Se=55%, Sp=85% Se=89%, Sp=77%.

Fischer

Aneurysm location

F

67

ACoA

4

4

Pericallosal hematoma, IVB, Hydrocephalus

Refractory IH, MCA spasm.

NA

2

F

70

RMCA

4

5

IVB, Hydrocephalus

No DCI

Diffusion hypersignal in the corpus callosum, brainstem, left temporal lobe

3

F

38

LPCA

4

5

IVB, Hydrocephalus

Left MCA territory

NA

4

M

76

ACoA

4

5

Right subdural hematoma

Right hemisphere

NA

5

F

44

RMCA

4

4

Right Fronto-temporal hematoma. Surgical evaluation and craniectomy

Left hemisphere

NA

6

F

43

ACoA

4

4

No DCI

Stable hematoma

7

F

51

RMCA

4

5

No DCI

Right peri-hematoma ischemia

8

F

68

ACoA

4

5

9

M

54

LMCA

4

5

IVB, hydrocephalus

Left MCA territory, bifrontal

10

F

55

BA

4

5

IVB, hydrocephalus

No DCI

11

F

55

LPCA

4

4

IVB, hydrocephalus

Refractory IH

NA

12

F

50

LCA

3

4

-

Left ACA territory

13

F

48

RMCA

4

4

Right temporal hematoma and subdural hematoma

Right Frontal

14

F

53

ACoA

4

4

IVB, Right frontal hematoma

No DCI

Left fronto-parietal ischemia Right frontal ischemia, pontine ischemic lesion, stable temporal lesions post-surgical evacuation Stable hematoma, right punctiform insular ischemia

15

M

57

ACoA

4

4

Right frontal hematoma, bilateral frontal ischemia during coiling procedure

No DCI

Stable frontal lesions, right subdural hematoma 4mm.

-

Table

WFNS

Sex

1

Age

Patient

AT/D decrease under 30% > 3.7h first location (day after bleeding) and spatial evolution

Early brain injury

Frontal inter-hemispheric hematoma, IVB Right frontal hematoma, IVB, surgical evacuation and craniectomy Frontal inter-hemispheric hematoma, IVB, left parietooccipital subdural hematoma

DCI location

Left occipital

Delayed MRI

Frontal peri-hematoma ischemia, left occipital ischemia Widespread left cortical ischemia, right frontal ischemia Latero-pontine and cerebellar ischemia

EEG anomaly

AT/D max duration over 30%

First brain imaging with DCI evidence (day after bleeding)

Other monitoring data changes (day after bleeding, hours after AT/D decrease)

Fp2F4 (3d), stays focal

Right LPD

5.9h

Bilateral MCA spasm (5d)

-

Right LPD

1.4h

-

C3T3 & Fp1F3 (7d), only in C3T3 after 2min

Left frontocentral spikes

8.5h

-

26.8h

Left LPD

24.5h

Left MCA territory perfusion deficit (↑MTT, ↓CBF) (6d)

Left temporal spikes + LPD

1.7h

-

Clinical improvement

-

-

0

-

Clinical improvement

P3O1 (9d), stays focal

-

4h

Global (2d)

GPD

6.1h

-

-

0.7h

-

C3T3 (10d), spread to P3O1 within 45min and right hemisphere 6h later

-

12.8h

NA (death 12h post AT/D decrease)

Sceptic choc (10d, -20h)

global (5d)

Right anterior LPD

22.9h

Frontal and parietal infarct (5d)

ICP increase (5d, -4h)

Fp2F4 (3d), spread to C4T4 within 1h

-

7.9d

Right frontal perfusion deficit (↑MTT, ↓CBF, 5d)

Fever, RMCA increase doppler velocities (3d, +7h)

7.6h

-

3.3h

-

Fp2F4 (3d), spread to Fp1F3 within 6min Fp1F3 (5d), spread within 1min to Fp2F4 & 9h10 to C3T3 -

Global (11d) -

Left hemisphere ischemia, perfusion deficit (↑MTT, ↓CBF) (12d) Infarction in the RMCA superficial territory (9d)

Left parietooccipital ↑FLAIR ↓ADC (17d) Left MCA territory and bi-frontal infarcts (10d)

left paresis (3d, +22h), bilateral mydriasis (6d)

Clinical improvement

Recurrent hyperthermia

ICP increase (4d, +14h), and PbtO2 decrease (4d, +28h)

PbtO2 decrease (6d, +10h)

Transient ICP increase with stable PbtO2 (3d), stable on day 9 Stable PbtO2 No changes

Recurrent hyperthermia, clinical improvement LMCA doppler increase velocity (5d, +14h), clinical improvement

2.

PATIENT

BACKGROUND ACTIVITY AT CEEG ONSET C4-T4 P4-O2 Fp1-F3 C3-T3 6Hz 6Hz Beta 4Hz

P3-O1 5Hz

AT/D PATTERN T, P (Fp2F4)

1

Fp2-F4 Beta

2 3

5Hz + DW 6Hz + DW

5Hz + DW 6Hz + DW

5Hz + DW 6Hz + DW*

5Hz + DW 6Hz + DW

5Hz + DW 6Hz + DW

5Hz + DW 6Hz + DW*

T, I T, P (C3T3)

4

7Hz + Beta

7Hz + Beta

7Hz + Beta

7Hz + Beta

7Hz + Beta

7Hz + Beta

5

7Hz + DW

7Hz + DW

7Hz + DW

7Hz + DW#

7Hz + DW#

7Hz + DW#

6 7 8

7Hz + DW# 6Hz+DW Beta + DW

7Hz + DW# 6Hz+DW 8Hz + DW

7Hz + DW# 6Hz+DW* 8Hz + DW

7Hz + DW 1.5-2Hz 7Hz + DW#

7Hz + DW 2-4 Hz 7Hz + DW#

7Hz + DW 2-4Hz 8Hz + DW#

T, P (Fp2F4, Fp1-F3, then diffuse) T, P (Fp1F3, Fp2-F4, C3-T3) T, I T, P (P3-O1)

9

8Hz + Beta

8-9Hz

9Hz

8Hz

8-9Hz

9Hz

T, P (diffuse)

10 11

7Hz 7Hz + DW

7Hz 5-6Hz + DW

depressed depressed

6Hz + DW 6Hz + DW

6Hz + DW 6Hz + DW

depressed depressed

12

8Hz + DW

8Hz + DW

8Hz + DW

depressed

2-5 Hz

2-5Hz

T P (C3T3 then diffuse) T, P (diffuse)

13

Beta + 2Hz

7Hz + 2Hz

7Hz

Beta + DW

8Hz + DW

6-7Hz

14

Beta + DW

6Hz + DW

6Hz + DW

6Hz + DW

6Hz + DW

6Hz + DW#

15

4-5Hz

4Hz

4-5Hz

4-5Hz

3-4Hz

3-5Hz

Table 3.

Over 30% decrease 94.4 [82.3-100]

Over 50% decrease 92.6 [79.4-100]

3.7 [2.7-10.1]

1.4 [0.9-3.7]

83.3 [50-100] 100 4.4 [1.1-18.5] 0.064 [0.0040.975]

83.3 [50-100] 88.9 [66.7-100] 5.3 [0.9-32.4] 0.133 [0.020.876]

Grey zone (h)

3.7 - 8.04

0.9 - 2.6

% patient in grey zone

26.7%

33.3%

AUC (%) Best threshold (h) Sp (%) Se (%) PLR NLR

Table 4.

T, P (Fp2-F4 then C4T4 and P4O2) T, I, P (diffuse) T

DCI Refractory IH, MCA spasm. No DCI Left MCA territory Right hemisphere

MRS 6

5 (1month) 6 6

Left hemisphere

6

No DCI No DCI Left occipital Left MCA territory, bifrontal No DCI Refractory IH Left ACA territory Right Frontal

3 (2years) 2 (2 years) 5 (3month)

No DCI

4 (2month)

No DCI

4 (1month)

3 (3years)

6 (TL) 6 3 (2years) 6 (TL)