Oxygen-based autoregulation indices associated with clinical outcomes and spreading depolarization in aSAH

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In addition, it has previously been proposed that there may be an association of impaired autoregulation with the occurrence of spreading depolarization (SD) events. Methods Subjects with invasive multimodal monitoring and aSAH were enrolled in an observational study. Autoregulation indices were prospectively calculated from this database as a 10 second moving correlation coefficient between various cerebral blood flow (CBF) surrogates and mean arterial pressure (MAP). In subjects with subdural ECoG (electrocorticography) monitoring, SD was also scored. Associations between clinical outcomes using the mRS (modified Rankin Scale) and occurrence of either isolated or clustered SD was assessed. Results 320 subjects were included, 47 of whom also had ECoG SD monitoring. As expected, baseline severity factors such as mFS and WFNS (World Federation of Neurosurgical Societies scale) were strongly associated with the clinical outcome. SD probability was related to blood pressure in a triphasic pattern with a linear increase in probability below MAP of ~ 100mmHg. Autoregulation indices were available for intracranial pressure (ICP) measurements (PRx), PbtO2 from Licox (ORx), perfusion from the Bowman perfusion probe (CBFRx), and cerebral oxygen saturation measured by near infrared spectroscopy (OSRx). Only worse ORx and OSRx were associated with worse clinical outcomes. ORx and OSRx also were found to both increase in the hour prior to SD for both sporadic and clustered SD. Conclusions Impairment in autoregulation in aSAH is associated with worse clinical outcomes and occurrence of SD when using ORx and OSRx. Impaired autoregulation precedes SD occurrence. Targeting the optimal MAP or cerebral perfusion pressure in patients with aSAH should use ORx and/or OSRx as the input function rather than intracranial pressure. subarachnoid hemorrhage delayed cerebral ischemia spreading depolarization cerebral autoregulation cerebral ischemia Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Management of delayed cerebral ischemia 1 (DCI) after aneurysmal subarachnoid hemorrhage (aSAH) remains both a significant challenge and one of the major targetable secondary injury mechanisms in the neuro-intensive care unit. Evolving understanding of the mechanisms of DCI have opened new physiologically based therapeutic approaches to prevent and treat this problem. Two of the most relevant of these include the role of impaired autoregulation 2 – 9 and occurrence of spreading depolarization (SD) events 7 , 10 – 13 . Cerebral autoregulation (CA) is the adaptive mechanism by which the brain maintains constant cerebral blood flow (CBF) over a wide range of mean arterial pressure (MAP) 14 . In cases of injury, autoregulation can be impaired, shifted, or even lost such that moderately low blood pressure could lead to potentially ischemic levels of blood flow and moderate elevations could lead to hyperemia, elevated intracranial pressure, and secondary damage 3 , 15 . There is no perfect tool to measure autoregulation, especially in a condition such as aSAH where there can be significant temporal and regional changes in autoregulation the weeks following the initial bleed 16 . Continuous indices have been developed which use a rolling correlation coefficient between MAP and various surrogates for CBF to gain insight into the autoregulatory status and how it evolves over the course of admission 16 – 18 . A target that minimizes the index can then be hypothesized on a patient-specific basis and potentially be used to refine the patient-specific optimum MAP (MAPopt) or optimum cerebral perfusion pressure (CPPopt) 19 . SD are massive, non-synaptically mediated, slow-moving depolarizing events that are electrophysiologically very similar to terminal depolarization/ brain death, so can be considered a type of “near death event” 20 – 22 especially when occurring in metabolically compromised regions. When there is adequate metabolic substrate, (blood flow, glucose, oxygen) tissue can slowly recover over minutes to hours, beginning neuronal transmission after a transient period of minutes 23 . However, in extremely metabolically compromised tissue, SD can result in expansion of ischemia due to the large metabolic requirements for recovery 22 , 23 . This process is likely cyclical, where, in vulnerable tissue, metabolic transients such as hypoxia, hypotension, and even cortical excitation can trigger SD 24 – 26 and SD can in turn, further stress this metabolically compromised tissue, resulting in expansion of ischemia 27 . In aSAH, the occurrence of SD has been strongly associated with worse clinical outcomes and episodes of neurologic deficit 13 . An important factor contributing to initiation of SD is inadequate CBF 28 , 29 . In conditions of impaired autoregulation, where CBF may fall into ischemic zones and trigger SD, even in the normal MAP range, it would be expected that SD may occur more frequently 7 . Several previous studies demonstrated a triphasic probability curve of SD versus MAP where the probability of SD increased dramatically at the lower end of blood pressure, was flat at the middle range, and decreased further at the high end of blood pressure 7 , 26 . This relationship follows the characteristics of the autoregulatory curve 16 . We previously assessed a small group of patients with aSAH who all had simultaneous monitoring of multiple different autoregulation indices to assess both agreement between those indices and relative predictive value for clinical outcomes and occurrence of SD 7 . In that study, we found that PRx (derived from ICP), ORx (derived from PbtO2), and OSRx (derived from scalp NIRS) seemed to have the most consistent association with SD and possibly with clinical outcomes, though the study lacked power. In this current study, we expanded this cohort to a much larger data set and performed a more rigorous high-resolution assessment of each autoregulation index to determine whether these indices were associated with worse clinical outcomes and if SD occurrence was the cause or the result of impaired autoregulation. Methods Subjects Subjects enrolled in multiple studies related to multimodality monitoring at our institution over a period of 12 years were pooled for the current study (UNM IRB# 10-159, 17-297, 20-390, and 21-044) and data on multimodality monitoring were collected on these patients. All of these were observational studies without research interventions related to SD or multimodal monitoring parameters. Subjects with aSAH were included who had placement of multimodality monitoring. The clinical criteria for placement of multimodal monitoring were any patient with symptomatic hydrocephalus on admission or need for CSF (cerebrospinal fluid) diversion during a surgical procedure. The Hummingbird (IRRAS USA: San Diego, CA) system 30 was used in this entire cohort. This system consists of a single twist drill bolt with an external ventricular drain (EVD) with an integrated parenchymal monitor for continuous intracranial pressure (ICP) measurement built into the catheter. There are one or two additional side ports that allowed for the placement of additional monitors, typically a PbtO 2 monitor (Licox: Integra LifeSciences: Princeton, NJ) and a CBF monitor (Bowman Perfusion Monitor: Hemedex: Waltham MA). Most monitors are placed contralateral to the site of aneurysm rupture in case surgical treatment was needed. All patients also had bilateral frontal NIRS (INVOS: Medtronic: Minneapolis, MN) placed for clinical management. Some patients undergoing surgical treatment had additional placement of a 1x6 subdural strip electrode over the region deemed to be at the highest risk of ischemia (e.g. temporal for middle cerebral artery aneurysms or frontal for anterior communicating aneurysms). All these parameters, in addition to systemic parameters such as blood pressure, are monitored at the bedside in the Moberg CNS monitor (Natus: Middleton, WI). Scoring In our previous study, we retrospectively calculated low-resolution versions of the various autoregulation indices using one-minute binned data exported from the Moberg CNS as a 30-minute rolling average of the Pearson’s correlation coefficients between MAP and each potential CBF surrogate 7 . With recent upgrades to the CNS Envision software (Natus: Middleton, WI), the originally proposed method for calculating PRx 31 can be applied to each additional parameter using the original waveform data. Briefly, each index is calculated as the 10s rolling average of the Pearson’s correlation coefficients between the waveform level MAP and the input function of interest over a 5-minute moving window. We used ICP from the parenchymal monitor to calculate PRx-parenchymal, ICP from the EVD to calculate PRx-EVD, CBF from the Bowman probe to calculate CBFRx, PbtO 2 from the Licox to calculate ORx, and cerebral regional hemoglobin oxygen saturation (rSO2) from the INVOS to calculate OSRx. We calculated each of these continuous indices using the CNS Envision software for all the retrospective stored subject data. SD recordings were acquired and scored per international consensus guidelines 20 . Briefly, a standard platinum 1X6 electrode was placed in the subdural space at the time of surgery. ECoG was recorded using a DC amplifier (Moberg CNS) directly into the Moberg CNS system, linking these data to the other multimodal monitoring parameters 32 . DC recordings (high-pass filtered at 0.005Hz to stabilize drift) were overlayed on high-frequency bandpass filtered data (0.5-50Hz). A SD cluster includes all SDs in the same patient occurring within 1 hour of a consecutive pair. Data processing The steps for data processing to a workable format required extensive development and are summarized in Figure 1. Briefly, clinically recorded files were first copied from a clinical server to a research folder per IRB requirements. After calculation of autoregulation indices, data were then exported in text files in one-minute bins. Annotations including SD scoring and other events were exported as a separate file. Naming for variables was then standardized from various conventions that changed over the years. In cases where the label was changed during the recording (e.g. ART changed to ABP), these were manually reviewed and then consolidated if it was determined to be the same data stream in that subject. Results for complex cases were manually reviewed to ensure accuracy. In some subjects, there were multiple files which were re-linked into one contiguous file. The recordings were then linked to the date and the times of SD. Data were then filtered to include only physiologically plausible ranges (e.g. to remove times when arterial lines were being accessed or “zeroed”.) Clinical data Standard clinical variables were recorded either retrospectively with chart review or prospectively collected. Age, sex, admission diagnosis, admission Glasgow coma scale (GCS) score, admission world federation of neurosurgical societies grade (WFNS), and modified Fisher scale (mFS) were all collected. Angiographic vasospasm was recorded as the maximum severity recorded on the routine day 7 angiogram. Discharge and ~day 90 modified Rankin scale (mRS) were collected either from chart review or from prospective structured interview. Data analysis Summary values for each parameter of interest in addition to clinical and demographic and outcome data were then compiled. The primary clinical outcome was discharge mRS as this was the outcome parameter with the least missing data. Good outcome was defined as mRS 0-3 whereas poor outcome was defined as mRS 4-6. Two-tailed t-tests as well as Mann-Whitney non-parametric tests were used for continuous variables, and Fisher’s test were used for binary variables. Subjects with missing data for a given variable were excluded from that analysis. Autoregulation indices were derived on the basis of Pearson’s correlation and hence are bounded between -1 and 1. We applied Fisher’s transformation for correlation coefficients to the autoregulation indices before conducting t-tests for the difference between poor and good outcomes and pre-SD and post-SD trend analysis. Significance was considered at p-values <0.05. In order to assess the relationship between SD and blood pressure, probability curves were constructed using 20-minute bins containing SD compared to bins without SD in reference to MAP. To determine the temporal relationship between SD and various autoregulation indices, we investigated the time-trend of these indices pre-SD and post-SD. We first defined and identified SD clusters within a patient using the working definition described earlier. SDs that were more than one hour before the first SD or one hour later after the last SD in a cluster were defined as belonging to different clusters. For each SD cluster, the pre-SD time series begins at 60 minutes before and ends at the first SD of the cluster, with a total of 60 bins including the one for the first SD. The post-SD time series begins at the last SD of the cluster and ends 60 minutes later. These bins were non-overlapping. We applied Fisher’s transformation of correlation coefficients and then conducted trend analysis on the transformed data using linear mixed-effects model with random effects for individual patients as well as SD clusters. We conducted separate trend analysis for non-clusters of single SDs, clusters of multiple SDs, and the two pooled. We also explored other time boundaries such as 2 hour and 30 minutes as well as varying length of the serial data. All processing and analysis were performed using Matlab, R, and Graphpad Prism(v10.1.1). The STROBE reporting guideline tool was used for this observational study. Results We identified 320 subjects meeting the inclusion criteria between 2010 and 2023. Mean Age was 57 (StDev=14). The median admission GCS score was 12 and WFNS score was 3. The overall mean hospital length of stay was 24 days, consistent with these being a relatively poor grade group of patients with aSAH. More patients (n=198) underwent craniotomy or craniectomy and the remainder (n=122) underwent endovascular embolization. This trend has been changing with time as endovascular therapy has improved, however, surgical treatment was preferred especially early in the experience. Forty-seven of these subjects underwent SD monitoring. Older age, lower admission GCS, higher admission WFNS, and higher admission mFS were all associated with worse outcomes. Sex was not. Interestingly, angiographic vasospasm severity on routine day 7-11 angiogram was also not associated with outcomes. See Table 1. We hypothesized that impaired autoregulation (higher autoregulation index) would be associated with poor outcomes but found only ORx (p=0.0005) and OSRx average (p<0.0001) demonstrated a significant association with worse clinical outcomes (see Table 1 and Figure 2). Plotting the probability of SD versus blood pressure, a familiar triphasic curve characteristic of cerebral autoregulation was demonstrated 16 (Figure 3). Thus, below MAP of ~90mmHg there was a nearly linear association of increased SD probability with decreased MAP. As noted in the plot, between 90 and 150mmHg, the probability of SD was relatively stable and low. Above MAP of 150 no SD were observed. These three transitions have previously proposed to represent a reflection of the autoregulatory curves and the upper and lower limits of autoregulation. In this cohort of poor grade patients overall, a shift in the lower limit of autoregulation may be suggested. With regard to SD clusters, we found an increasing trend in ORx and OSRx average within 60-min just before SD clusters as seen in the positive slopes derived from the fitted linear mixed-effects model (0.0013, p=0.001 for ORx, and 0.0009, p=0.0001 for OSRx, respectively, Table 2) We also found a decreasing trend in ORx within 60 minutes just after SD clusters (-0.0008, p=0.0288), but no significant trend in OSRx post-SD clusters. Comparable and consistent results were seen when we analyzed single SD clusters and multiple SD clusters separately (See slopes in Table 2). Figure 4a and Figure 4b display this trend. This may suggest that worsening autoregulation measured by ORx and OSRx contributed to increased SD probability with potential improvement (e.g. in ORx) or stabilization (e.g. in OSRx) post-SD. Mean PRx from the parenchymal monitor was significantly higher prior to clustered SDs compared to isolated SD, but with a somewhat decreasing trend immediately before clustered SDs (slope=-0.0015, p<0.0001) and increasing trend post isolated SDs (slope=0.0008, p=0.002). We also found a decreasing trend in PRx EVD prior to SD, but an increasing trend post-SD with only isolated SD. Significant pre-SD and post-SD trends were found in CBFRx only with clustered SDs not isolated SDs. It is interesting to note that the time trends in these autoregulation indices were robust with respect to time boundary, length of index serials, or removing time bins immediately proximate to SD clusters. Discussion The importance of autoregulation in the management of aSAH has been previously explored as a strategy to develop individualized management strategies for patients at risk of ischemia related to DCI 33 , 34 . Current AHA guidelines for the management of aSAH 35 suggest that after ensuring appropriate euvolemia, permissive autoregulation strategies are reasonable, however, further validation of algorithms and real time application are needed. In the absence of such individualized approaches, blood pressure augmentation in response to neurologic changes related to DCI may be considered 35 . The use of autoregulation-derived approaches potentially offers the opportunity to better refine such augmentation to the physiology of a given patient at a given stage after injury, though evidence for such strategies remains limited 36 . This is likely in part due to multiple different, non-interchangeable methods to assess cerebral autoregulation and a lack of clear understanding of the exact physiology that is being measured by these indices 37 . The current study therefore fills several important missing links in the literature. First, we have compared several measurement approaches side by side in a relatively large cohort of patients and demonstrated consistent association with outcomes with ORx and OSRx, consistent with other reports where the PRx was less strongly associated with outcomes in aSAH 6 , 38 . Second, our data provides an important mechanistic link to outcomes based on the relationship of impaired autoregulation to SD. These data serve as a validation of the hypotheses developed in a smaller cohort of patients who were only assessed if each subject had all the autoregulation monitoring approaches (ICP, PBtO2, CBF, and NIRS) including SD monitoring 77 . In that study, we found that ORx, OSRx and PRx were the most reliable indices in identifying the risk of worse outcomes in patients with aSAH. Only one of the two PRx measurements (parenchymal ICP) was associated with clinical outcomes. This PRx measurement was also inconsistently associated with SD occurrence. In the current study, PRx was not associated with outcomes and there was a possible weak association with SD, though was not consistent when considering single versus clustered SD. On the other hand, both ORx and OSRx were associated with clinical outcomes and were found to increase prior to SD regardless of whether considering single or clustered events. A strength therefore of the current analysis is that this larger study replicates and strengthens the results of the exploratory study. Based on these data, we agree with current evolving management strategies that target optimal MAP or cerebral perfusion pressure in patients with aSAH 33 , 39 and also agree with the importance of tissue oxygenation as these studies have emphasized. The use of the ORx or OSRx as the input function for calculation of CPPopt or MAPopt may be a more effective strategy than using PRx and PbtO2 or rSO2 as separate measures to balance. Certainly, further practical studies are needed to determine the feasibility of incorporating such approaches at the bedside, however our work demonstrating this potential application of either invasive or non-invasive approaches may facilitate use in more patients. The second important link provided by our current analysis is in refining the relationship between autoregulation and SD. Specifically, SD triggered by decreasing CBF and impaired autoregulation may be one of the central mechanisms of secondary ischemia in aSAH. In a recent multicenter study from Europe, the peak total spreading depolarization-induced depression duration of a recording day (PTDDD) was found to predict ischemia and infarction with 60 and 180-minute duration and concluded SD to be an independent mechanistic biomarker for DCI and delayed infarction in aSAH 13 . This is consistent with previous literature linking SD and spreading ischemia to worse outcomes in aSAH 13 , 23 . In the current study, we once again identified a triphasic probability curve of SD which has now been demonstrated in patients with traumatic brain injury 40 and ischemic stroke 26 . Overall, it appears that there in increased risk of SD (lower limit of autoregulation) as high as a MAP of 90-100mmHg in this population, however we do not propose that this be used as in indiscriminate target. Instead, the use of the combination of autoregulation and SD monitoring could play an important role in better understanding the risk that a given patient may be at in terms of ischemia. For example, the occurrence of SD may be an indicator of metabolically compromised tissue (at risk of DCI) and progressively worsening duration of the ECoG depression could indicate the need to further optimize physiologic targets to avoid ischemia using MAPopt or other approaches 16 . Previous data on the relationship between SD and autoregulation was summarized in a recent comprehensive review 16 . In addition to the associations of ORx and OSRx that we previously reported 7 , another group found associations between SD and impaired autoregulation as measured by the PRx 10 , hypothesizing that SD may in fact be the source of impaired autoregulation. Interestingly, these findings were different depending on whether a parenchymal or ventricular source of ICP monitoring was used. In our data, PRx values from both the parenchymal and ventricular source demonstrated an inconsistent relationship with SD. Both tended to decrease prior to SD and generally tended to increase after SD with significant positive slopes for both sources. These PRx trends indicate variability in relationship to SD however could be consistent with the previous associations of SD leading to worsening PRx 10 . Since PRx was not consistently associated with outcome, it seems that the association between ORx and OSRx with SD may be more clinically relevant. The relationship between SD and autoregulation may also be more complex, as locally impaired autoregulation at the time of SD has also been observed 11 , 41 . Therefore, SD may both be triggered by globally impaired autoregulation and may further worsen autoregulation in the region of SD as one mechanism of ongoing tissue metabolic stress. This potentially cyclical relationship is therefore one explanation for these seemingly contradictory findings. Limitations While there are notable strengths to this study including high-resolution recordings over a long period of time in a large, relatively homogeneous cohort of aSAH patients, there are clearly some limitations. Not all subjects had all parameters measured due to practice and technology changes over the course of monitoring. A relatively small number of only surgical patients had SD monitoring, so it is unknown if our observations related to SD apply to non-surgical patients as well, however it seems plausible that similar pathophysiology is at play. In addition, there is a risk that the continuous indices do not reflect the tissue most at risk for SD since the monitors used to generate these indices are typically placed contralateral to the surgical site and therefore the SD monitoring electrode site. Finally, since this was primarily a cohort of more severely injured subjects requiring multimodality monitoring, the applicability to lower-grade patients is unknown. Conclusion Impairment in cerebral autoregulation in aSAH is most associated with worse clinical outcomes and occurrence of SD when using ORx and OSRx. Impaired autoregulation appears to precede SD occurrence rather than be a result of SD occurrence, though this could be a cyclical relationship. Targeting the optimal MAP or cerebral perfusion pressure in patients with aSAH should use ORx and/or OSRx as the input function rather than intracranial pressure. The combined use of continuous autoregulation monitoring to determine the optimum blood pressure target with monitoring of SD as a mechanism of ongoing metabolic stress and risk of ischemia may be a rational strategy to improve outcomes in patients with aSAH. Declarations Acknowledgements: none References Vergouwen MD, Vermeulen M, van Gijn J, 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. 10.1161/STROKEAHA.110.589275 . Budohoski KP, Czosnyka M, Smielewski P, et al. Impairment of cerebral autoregulation predicts delayed cerebral ischemia after subarachnoid hemorrhage: a prospective observational study. 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Individualized cerebral perfusion pressure in acute neurological injury: are we ready for clinical use? Curr Opin Crit Care. 2022;28(2):123–9. 10.1097/MCC.0000000000000919 . Czosnyka M, Brady K, Reinhard M, Smielewski P, Steiner LA. Monitoring of Cerebrovascular Autoregulation: Facts, Myths, and Missing Links. Neurocrit Care. 2009;10(3):373–86. 10.1007/s12028-008-9175-7 . (In English). Jaeger M, Soehle M, Schuhmann MU, Meixensberger J. Clinical significance of impaired cerebrovascular autoregulation after severe aneurysmal subarachnoid hemorrhage. Stroke. 2012;43(8):2097–101. 10.1161/STROKEAHA.112.659888 . Silverman A, Kodali S, Strander S, et al. Deviation From Personalized Blood Pressure Targets Is Associated With Worse Outcome After Subarachnoid Hemorrhage. Stroke. 2019;50(10):2729–37. 10.1161/STROKEAHA.119.026282 . Hartings JA, Strong AJ, Fabricius M, et al. Spreading depolarizations and late secondary insults after traumatic brain injury. J Neurotrauma. 2009;26(11):1857–66. 10.1089/neu.2009-0961 . (In eng). Hinzman JM, Andaluz N, Shutter LA, et al. Inverse neurovascular coupling to cortical spreading depolarizations in severe brain trauma. Brain. 2014;137(Pt 11):2960–72. 10.1093/brain/awu241 . Tables Table 1 - Associations of Patient Characteristics, Injury Severities, and Autoregulation Indices with Clinical Outcome at Discharge. Good outcome at DC (mRS 0-3) N=118 Poor outcome at DC (mRS 4-6) N=202 p-value test Age in years: Mean (StD) 51.81 (12.26) 60.46 (13.46) <0.0001 t-test Female Sex: N (%) 70 (59.3%) 139 (68.8%) 0.0898 Fisher’s exact test Admission GCS: Median 15 (n=29) 10 (n=94) <0.0001 Mann Whitney test Admission WFNS: Median 1 (n=28) 4 (n=87) <0.0001 Mann Whitney test Admission mFS: Median 4 (n=24) 4 (n=58) <0.0001 Mann Whitney test Vasospasm (score=0-3*): Median 0 (n=103) 0 (n=157) 0.2991 Fisher’s exact test PRx EVD: Mean (StD) 0.11 (0.10) (n=68) 0.09 (0.13)(n=122) 0.2546 t-test** PRx parenchymal: Mean(StD) 0.17 (0.12) (n=68) 0.18(0.14)(n=132) 0.6869 t-test** ORx: Mean (StD) 0.03 (n=95) 0.06 (n=173) 0.0005 t-test** OSRx (average R/L): Mean (StD) 0.05 (n=98) 0.12 (n=167) <0.0001 t-test** CBFRx: Mean (StD) 0.25 (n=66) 0.25 (n=103) 0.9551 t-test** * 0= no vasospasm, 3=severe vasospasm **For each autoregulation index Fisher’s transformation of correlation coefficient was applied to the data before two-sample t-test. DC= Discharge, mRS= modified Rankin Scale, StD= Standard deviation, GCS=Glasgow Coma Scale score, WFNS= World Federation of Neurosurgical Societies Grade, mFS= modified Fisher Scale, PRx= Pressure reactivity, EVD= external ventricular drain, ORx= Oxygen reactivity, OSRx= Oxygen saturation reactivity, CBFRx= Cerebral blood flow reactivity Table 2 Pre- and Post-SD Trend in Autoregulation Based on Linear Mixed Effects Models fit to serial 1-minute bin data up to 60 minutes before and after a SD cluster. Bold/italic values are significant. Grey boxes indicate consistent trends in direction of slope and significance. Autoregulation Index Cluster Pre-SD Post-Sd Slope (/min) p-value Slope (/min) p-value PRx EVD Single SD -0.00049 0.0349 0.00068 0.0014 Multiple SD -0.00058 0.0556 0.00017 0.4866 Pooled -0.00052 0.0043 0.00048 0.0025 PRx Parenchymal Single SD -0.00040 0.1354 0.00083 0.0021 Multiple SD -0.00152 <0.0001 -0.00026 0.4324 Pooled -0.00082 0.0001 0.00041 0.0510 ORx Single SD 0.00124 0.0070 -0.00009 0.8376 Multiple SD 0.00133 0.0463 -0.00217 0.0007 Pooled 0.00125 0.0010 -0.00080 0.0288 OSRx (average R/L) Single SD 0.00089 0.0017 -0.00003 0.9265 Multiple SD 0.00088 0.0221 0.00029 0.4199 Pooled 0.00089 0.0001 0.00008 0.7096 CBFRx Single SD 0.00049 0.4094 0.00017 0.7835 Multiple SD -0.00195 0.0249 -0.00202 0.0062 Pooled -0.00036 0.4683 -0.00058 0.2271 Cite Share Download PDF Status: Published Journal Publication published 27 Aug, 2024 Read the published version in Neurocritical Care → Version 1 posted Reviewers agreed at journal 29 May, 2024 Reviewers invited by journal 26 May, 2024 Editor invited by journal 26 May, 2024 Editor assigned by journal 23 May, 2024 First submitted to journal 23 May, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4451509","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":307007829,"identity":"fec8aeb5-70f1-4da3-85d8-58ac460e85b1","order_by":0,"name":"Andrew Phillip Carlson","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEElEQVRIiWNgGAWjYHACAwjFDiIqGBj44CIMCQS0MIOIMwwMbKRpYWwjQgv/7OZtHz622cmbN7M/fFw4r06eTSJ5AzPvjm0M/Ow5Bti0SNw5VjxzZluy4ZzDPMbGM7cdNmyTSCtg5j1zm0Gy5w1WLQw3coyZebcdYJzBzMMmDWK0SeQYMPO23WYwuIHdFnmQlr/bDtjPYGZ/Js07p84ersUehxYDkBbGbQcSZzAzmEnzNjAnImyRwK7F8EZaMWPvv+RkoMOMjXmOHU5u43lWcHBu220eiTPPCrBpkbuRvJnhxxk72xns7Q8f89TU2fazJ2988Lbtthx/e/IGrN7HCg4AMQ/xykfBKBgFo2AUoAMAsQlaJ0Zt10IAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0003-2189-3699","institution":"University of New Mexico","correspondingAuthor":true,"prefix":"","firstName":"Andrew","middleName":"Phillip","lastName":"Carlson","suffix":""},{"id":307007830,"identity":"6dad472e-5f1f-4283-8a5c-db8af981d645","order_by":1,"name":"Thomas Jones","email":"","orcid":"","institution":"University of New Mexico School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Thomas","middleName":"","lastName":"Jones","suffix":""},{"id":307007831,"identity":"fa97ecea-9a84-42cc-86f3-03cf021880ec","order_by":2,"name":"Yiliang Zhu","email":"","orcid":"","institution":"University of New Mexico School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yiliang","middleName":"","lastName":"Zhu","suffix":""},{"id":307007832,"identity":"17bcd031-2907-4951-bb70-e9d0dd8a7c78","order_by":3,"name":"Masoom Desai","email":"","orcid":"","institution":"University of New Mexico School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Masoom","middleName":"","lastName":"Desai","suffix":""},{"id":307007833,"identity":"902f8d35-7a79-4705-a0b7-c581732d34ad","order_by":4,"name":"Ali Alsarah","email":"","orcid":"","institution":"Harvard Medical School","correspondingAuthor":false,"prefix":"","firstName":"Ali","middleName":"","lastName":"Alsarah","suffix":""},{"id":307007834,"identity":"1e60506c-96b9-418a-aab0-214fe0447e73","order_by":5,"name":"C William Shuttleworth","email":"","orcid":"","institution":"University of New Mexico School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"C","middleName":"William","lastName":"Shuttleworth","suffix":""}],"badges":[],"createdAt":"2024-05-21 01:39:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4451509/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4451509/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12028-024-02088-x","type":"published","date":"2024-08-27T15:58:09+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":58154003,"identity":"84d3e9bb-f1ac-487b-9541-b9033b750a09","added_by":"auto","created_at":"2024-06-11 20:36:25","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":285224,"visible":true,"origin":"","legend":"\u003cp\u003eWorkflow for data cleaning and analysis\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4451509/v1/16e6c80525a5837e6755073f.png"},{"id":58154004,"identity":"41e99c7b-7472-4e3f-b685-a84dcd1e7a64","added_by":"auto","created_at":"2024-06-11 20:36:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":315092,"visible":true,"origin":"","legend":"\u003cp\u003eBox plots with raw data for each autoregulation index and clinical outcomes.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4451509/v1/d3a0a29533f2054250c3914f.png"},{"id":58154005,"identity":"789aebd8-1279-4da6-ab08-0c8fdf1e99ec","added_by":"auto","created_at":"2024-06-11 20:36:25","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":173000,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between probability of SD and MAP. This triphasic curve could plausibly be a reflection of a shifted autoregulation curve in this cohort.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4451509/v1/002c787ffcb49a47a08fdf6a.png"},{"id":58155175,"identity":"8fb5caae-dca9-4c7d-b15f-4d554cd2578f","added_by":"auto","created_at":"2024-06-11 20:44:25","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":218256,"visible":true,"origin":"","legend":"\u003cp\u003e4a: Trends in autoregulation index ORx in the 60 minutes pre-SD and 60 minutes post-SD. 4b: Trends in autoregulation index OSRx 60 minutes pre-SD and 60 minutes post-SD. Dots are 1-minute bin average across all SD clusters. Lines are predicted mean values using linear mixed-effects model based on serial data of individual SD clusters (See table 2 for intercept and slope). Individual index values were first transformed using Fisher’s transformation for correlation coefficient.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4451509/v1/3f42cb31b9e5570712b1db74.png"},{"id":63821134,"identity":"394c5025-58f8-4bd4-ba83-7dd149de029d","added_by":"auto","created_at":"2024-09-02 16:12:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1462651,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4451509/v1/b9f695a1-69f7-4b66-8ef2-2de66c94b532.pdf"}],"financialInterests":"","formattedTitle":"Oxygen-based autoregulation indices associated with clinical outcomes and spreading depolarization in aSAH","fulltext":[{"header":"Introduction","content":"\u003cp\u003eManagement of delayed cerebral ischemia\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e (DCI) after aneurysmal subarachnoid hemorrhage (aSAH) remains both a significant challenge and one of the major targetable secondary injury mechanisms in the neuro-intensive care unit. Evolving understanding of the mechanisms of DCI have opened new physiologically based therapeutic approaches to prevent and treat this problem. Two of the most relevant of these include the role of impaired autoregulation\u003csup\u003e\u003cspan additionalcitationids=\"CR3 CR4 CR5 CR6 CR7 CR8\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e and occurrence of spreading depolarization (SD) events\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCerebral autoregulation (CA) is the adaptive mechanism by which the brain maintains constant cerebral blood flow (CBF) over a wide range of mean arterial pressure (MAP)\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. In cases of injury, autoregulation can be impaired, shifted, or even lost such that moderately low blood pressure could lead to potentially ischemic levels of blood flow and moderate elevations could lead to hyperemia, elevated intracranial pressure, and secondary damage\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. There is no perfect tool to measure autoregulation, especially in a condition such as aSAH where there can be significant temporal and regional changes in autoregulation the weeks following the initial bleed\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Continuous indices have been developed which use a rolling correlation coefficient between MAP and various surrogates for CBF to gain insight into the autoregulatory status and how it evolves over the course of admission\u003csup\u003e\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. A target that minimizes the index can then be hypothesized on a patient-specific basis and potentially be used to refine the patient-specific optimum MAP (MAPopt) or optimum cerebral perfusion pressure (CPPopt)\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSD are massive, non-synaptically mediated, slow-moving depolarizing events that are electrophysiologically very similar to terminal depolarization/ brain death, so can be considered a type of \u0026ldquo;near death event\u0026rdquo;\u003csup\u003e\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e especially when occurring in metabolically compromised regions. When there is adequate metabolic substrate, (blood flow, glucose, oxygen) tissue can slowly recover over minutes to hours, beginning neuronal transmission after a transient period of minutes\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. However, in extremely metabolically compromised tissue, SD can result in expansion of ischemia due to the large metabolic requirements for recovery\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. This process is likely cyclical, where, in vulnerable tissue, metabolic transients such as hypoxia, hypotension, and even cortical excitation can trigger SD\u003csup\u003e\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e and SD can in turn, further stress this metabolically compromised tissue, resulting in expansion of ischemia\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. In aSAH, the occurrence of SD has been strongly associated with worse clinical outcomes and episodes of neurologic deficit\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAn important factor contributing to initiation of SD is inadequate CBF\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. In conditions of impaired autoregulation, where CBF may fall into ischemic zones and trigger SD, even in the normal MAP range, it would be expected that SD may occur more frequently\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Several previous studies demonstrated a triphasic probability curve of SD versus MAP where the probability of SD increased dramatically at the lower end of blood pressure, was flat at the middle range, and decreased further at the high end of blood pressure\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. This relationship follows the characteristics of the autoregulatory curve\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe previously assessed a small group of patients with aSAH who all had simultaneous monitoring of multiple different autoregulation indices to assess both agreement between those indices and relative predictive value for clinical outcomes and occurrence of SD\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. In that study, we found that PRx (derived from ICP), ORx (derived from PbtO2), and OSRx (derived from scalp NIRS) seemed to have the most consistent association with SD and possibly with clinical outcomes, though the study lacked power. In this current study, we expanded this cohort to a much larger data set and performed a more rigorous high-resolution assessment of each autoregulation index to determine whether these indices were associated with worse clinical outcomes and if SD occurrence was the cause or the result of impaired autoregulation.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cem\u003eSubjects\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSubjects enrolled in multiple studies related to multimodality monitoring at our institution over a period of 12 years were pooled for the current study (UNM IRB# 10-159, 17-297, 20-390, and 21-044) and data on multimodality monitoring were collected on these patients. All of these were observational studies without research interventions related to SD or multimodal monitoring parameters. Subjects with aSAH were included who had placement of multimodality monitoring. The clinical criteria for placement of multimodal monitoring were any patient with symptomatic hydrocephalus on admission or need for CSF (cerebrospinal fluid) diversion during a surgical procedure. The Hummingbird (IRRAS USA: San Diego, CA) system\u003csup\u003e30\u003c/sup\u003e was used in this entire cohort. This system consists of a single twist drill bolt with an external ventricular drain (EVD) with an integrated parenchymal monitor for continuous intracranial pressure (ICP) measurement built into the catheter. There are one or two additional side ports that allowed for the placement of additional monitors, typically a PbtO\u003csub\u003e2\u003c/sub\u003e monitor (Licox: Integra LifeSciences: Princeton, NJ) and a CBF monitor (Bowman Perfusion Monitor: Hemedex: Waltham MA). Most monitors are placed contralateral to the site of aneurysm rupture in case surgical treatment was needed. All patients also had bilateral frontal NIRS (INVOS: Medtronic: Minneapolis, MN) placed for clinical management. Some patients undergoing surgical treatment had additional placement of a 1x6 subdural strip electrode over the region deemed to be at the highest risk of ischemia (e.g. temporal for middle cerebral artery aneurysms or frontal for anterior communicating aneurysms). All these parameters, in addition to systemic parameters such as blood pressure, are monitored at the bedside in the Moberg CNS monitor (Natus: Middleton, WI). \u003c/p\u003e\n\n\u003cp\u003e\u003cem\u003eScoring\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn our previous study, we retrospectively calculated low-resolution versions of the various autoregulation indices using one-minute binned data exported from the Moberg CNS as a 30-minute rolling average of the Pearson\u0026rsquo;s correlation coefficients between MAP and each potential CBF surrogate\u003csup\u003e7\u003c/sup\u003e. With recent upgrades to the CNS Envision software (Natus: Middleton, WI), the originally proposed method for calculating PRx\u003csup\u003e31\u003c/sup\u003e can be applied to each additional parameter using the original waveform data. Briefly, each index is calculated as the 10s rolling average of the Pearson\u0026rsquo;s correlation coefficients between the waveform level MAP and the input function of interest over a 5-minute moving window. We used ICP from the parenchymal monitor to calculate PRx-parenchymal, ICP from the EVD to calculate PRx-EVD, CBF from the Bowman probe to calculate CBFRx, PbtO\u003csub\u003e2\u003c/sub\u003e from the Licox to calculate ORx, and cerebral regional hemoglobin oxygen saturation (rSO2) from the INVOS to calculate OSRx. We calculated each of these continuous indices using the CNS Envision software for all the retrospective stored subject data. \u003c/p\u003e\n\u003cp\u003eSD recordings were acquired and scored per international consensus guidelines\u003csup\u003e20\u003c/sup\u003e. Briefly, a standard platinum 1X6 electrode was placed in the subdural space at the time of surgery. ECoG was recorded using a DC amplifier (Moberg CNS) directly into the Moberg CNS system, linking these data to the other multimodal monitoring parameters\u003csup\u003e32\u003c/sup\u003e. DC recordings (high-pass filtered at 0.005Hz to stabilize drift) were overlayed on high-frequency bandpass filtered data (0.5-50Hz). A SD cluster includes all SDs in the same patient occurring within 1 hour of a consecutive pair. \u003c/p\u003e\n\n\u003cp\u003e\u003cem\u003eData processing\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe steps for data processing to a workable format required extensive development and are summarized in Figure 1. Briefly, clinically recorded files were first copied from a clinical server to a research folder per IRB requirements. After calculation of autoregulation indices, data were then exported in text files in one-minute bins. Annotations including SD scoring and other events were exported as a separate file. Naming for variables was then standardized from various conventions that changed over the years. In cases where the label was changed during the recording (e.g. ART changed to ABP), these were manually reviewed and then consolidated if it was determined to be the same data stream in that subject. Results for complex cases were manually reviewed to ensure accuracy. In some subjects, there were multiple files which were re-linked into one contiguous file. The recordings were then linked to the date and the times of SD. Data were then filtered to include only physiologically plausible ranges (e.g. to remove times when arterial lines were being accessed or \u0026ldquo;zeroed\u0026rdquo;.)\u003c/p\u003e\n\n\u003cp\u003e\u003cem\u003eClinical data\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eStandard clinical variables were recorded either retrospectively with chart review or prospectively collected. Age, sex, admission diagnosis, admission Glasgow coma scale (GCS) score, admission world federation of neurosurgical societies grade (WFNS), and modified Fisher scale (mFS) were all collected. Angiographic vasospasm was recorded as the maximum severity recorded on the routine day 7 angiogram. Discharge and ~day 90 modified Rankin scale (mRS) were collected either from chart review or from prospective structured interview.\u003c/p\u003e\n\n\u003cp\u003e\u003cem\u003eData analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSummary values for each parameter of interest in addition to clinical and demographic and outcome data were then compiled. The primary clinical outcome was discharge mRS as this was the outcome parameter with the least missing data. Good outcome was defined as mRS 0-3 whereas poor outcome was defined as mRS 4-6. Two-tailed t-tests as well as Mann-Whitney non-parametric tests were used for continuous variables, and Fisher\u0026rsquo;s test were used for binary variables. Subjects with missing data for a given variable were excluded from that analysis. Autoregulation indices were derived on the basis of Pearson\u0026rsquo;s correlation and hence are bounded between -1 and 1. We applied Fisher\u0026rsquo;s transformation for correlation coefficients to the autoregulation indices before conducting t-tests for the difference between poor and good outcomes and pre-SD and post-SD trend analysis. Significance was considered at p-values \u0026lt;0.05.\u003c/p\u003e\n\u003cp\u003eIn order to assess the relationship between SD and blood pressure, probability curves were constructed using 20-minute bins containing SD compared to bins without SD in reference to MAP. To determine the temporal relationship between SD and various autoregulation indices, we investigated the time-trend of these indices pre-SD and post-SD. We first defined and identified SD clusters within a patient using the working definition described earlier. SDs that were more than one hour before the first SD or one hour later after the last SD in a cluster were defined as belonging to different clusters. For each SD cluster, the pre-SD time series begins at 60 minutes before and ends at the first SD of the cluster, with a total of 60 bins including the one for the first SD. The post-SD time series begins at the last SD of the cluster and ends 60 minutes later. These bins were non-overlapping. We applied Fisher\u0026rsquo;s transformation of correlation coefficients and then conducted trend analysis on the transformed data using linear mixed-effects model with random effects for individual patients as well as SD clusters. We conducted separate trend analysis for non-clusters of single SDs, clusters of multiple SDs, and the two pooled. We also explored other time boundaries such as 2 hour and 30 minutes as well as varying length of the serial data. All processing and analysis were performed using Matlab, R, and Graphpad Prism(v10.1.1). The STROBE reporting guideline tool was used for this observational study. \u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eWe identified 320 subjects meeting the inclusion criteria between 2010 and 2023. Mean Age was 57 (StDev=14). The median admission GCS score was 12 and WFNS score was 3. The overall mean hospital length of stay was 24 days, consistent with these being a relatively poor grade group of patients with aSAH. More patients (n=198) underwent craniotomy or craniectomy and the remainder (n=122) underwent endovascular embolization. This trend has been changing with time as endovascular therapy has improved, however, surgical treatment was preferred especially early in the experience. Forty-seven of these subjects underwent SD monitoring. \u003c/p\u003e\n\u003cp\u003eOlder age, lower admission GCS, higher admission WFNS, and higher admission mFS were all associated with worse outcomes. Sex was not. Interestingly, angiographic vasospasm severity on routine day 7-11 angiogram was also not associated with outcomes. See Table 1. \u003c/p\u003e\n\u003cp\u003eWe hypothesized that impaired autoregulation (higher autoregulation index) would be associated with poor outcomes but found only ORx (p=0.0005) and OSRx average (p\u0026lt;0.0001) demonstrated a significant association with worse clinical outcomes (see Table 1 and Figure 2).\u003c/p\u003e\n\u003cp\u003ePlotting the probability of SD versus blood pressure, a familiar triphasic curve characteristic of cerebral autoregulation was demonstrated\u003csup\u003e16\u003c/sup\u003e (Figure 3). Thus, below MAP of ~90mmHg there was a nearly linear association of increased SD probability with decreased MAP. As noted in the plot, between 90 and 150mmHg, the probability of SD was relatively stable and low. Above MAP of 150 no SD were observed. These three transitions have previously proposed to represent a reflection of the autoregulatory curves and the upper and lower limits of autoregulation. In this cohort of poor grade patients overall, a shift in the lower limit of autoregulation may be suggested. \u003c/p\u003e\n\u003cp\u003eWith regard to SD clusters, we found an increasing trend in ORx and OSRx average within 60-min just before SD clusters as seen in the positive slopes derived from the fitted linear mixed-effects model (0.0013, p=0.001 for ORx, and 0.0009, p=0.0001 for OSRx, respectively, Table 2) We also found a decreasing trend in ORx within 60 minutes just after SD clusters (-0.0008, p=0.0288), but no significant trend in OSRx post-SD clusters. Comparable and consistent results were seen when we analyzed single SD clusters and multiple SD clusters separately (See slopes in Table 2). Figure 4a and Figure 4b display this trend. This may suggest that worsening autoregulation measured by ORx and OSRx contributed to increased SD probability with potential improvement (e.g. in ORx) or stabilization (e.g. in OSRx) post-SD. Mean PRx from the parenchymal monitor was significantly higher prior to clustered SDs compared to isolated SD, but with a somewhat decreasing trend immediately before clustered SDs (slope=-0.0015, p\u0026lt;0.0001) and increasing trend post isolated SDs (slope=0.0008, p=0.002). We also found a decreasing trend in PRx EVD prior to SD, but an increasing trend post-SD with only isolated SD. Significant pre-SD and post-SD trends were found in CBFRx only with clustered SDs not isolated SDs. It is interesting to note that the time trends in these autoregulation indices were robust with respect to time boundary, length of index serials, or removing time bins immediately proximate to SD clusters. \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe importance of autoregulation in the management of aSAH has been previously explored as a strategy to develop individualized management strategies for patients at risk of ischemia related to DCI\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Current AHA guidelines for the management of aSAH\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e suggest that after ensuring appropriate euvolemia, permissive autoregulation strategies are reasonable, however, further validation of algorithms and real time application are needed. In the absence of such individualized approaches, blood pressure augmentation in response to neurologic changes related to DCI may be considered\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. The use of autoregulation-derived approaches potentially offers the opportunity to better refine such augmentation to the physiology of a given patient at a given stage after injury, though evidence for such strategies remains limited\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. This is likely in part due to multiple different, non-interchangeable methods to assess cerebral autoregulation and a lack of clear understanding of the exact physiology that is being measured by these indices\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. The current study therefore fills several important missing links in the literature. First, we have compared several measurement approaches side by side in a relatively large cohort of patients and demonstrated consistent association with outcomes with ORx and OSRx, consistent with other reports where the PRx was less strongly associated with outcomes in aSAH\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Second, our data provides an important mechanistic link to outcomes based on the relationship of impaired autoregulation to SD.\u003c/p\u003e \u003cp\u003eThese data serve as a validation of the hypotheses developed in a smaller cohort of patients who were only assessed if each subject had all the autoregulation monitoring approaches (ICP, PBtO2, CBF, and NIRS) including SD monitoring\u003csup\u003e77\u003c/sup\u003e. In that study, we found that ORx, OSRx and PRx were the most reliable indices in identifying the risk of worse outcomes in patients with aSAH. Only one of the two PRx measurements (parenchymal ICP) was associated with clinical outcomes. This PRx measurement was also inconsistently associated with SD occurrence. In the current study, PRx was not associated with outcomes and there was a possible weak association with SD, though was not consistent when considering single versus clustered SD. On the other hand, both ORx and OSRx were associated with clinical outcomes and were found to increase prior to SD regardless of whether considering single or clustered events. A strength therefore of the current analysis is that this larger study replicates and strengthens the results of the exploratory study.\u003c/p\u003e \u003cp\u003eBased on these data, we agree with current evolving management strategies that target optimal MAP or cerebral perfusion pressure in patients with aSAH\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e and also agree with the importance of tissue oxygenation as these studies have emphasized. The use of the ORx or OSRx as the input function for calculation of CPPopt or MAPopt may be a more effective strategy than using PRx and PbtO2 or rSO2 as separate measures to balance. Certainly, further practical studies are needed to determine the feasibility of incorporating such approaches at the bedside, however our work demonstrating this potential application of either invasive or non-invasive approaches may facilitate use in more patients.\u003c/p\u003e \u003cp\u003eThe second important link provided by our current analysis is in refining the relationship between autoregulation and SD. Specifically, SD triggered by decreasing CBF and impaired autoregulation may be one of the central mechanisms of secondary ischemia in aSAH. In a recent multicenter study from Europe, the peak total spreading depolarization-induced depression duration of a recording day (PTDDD) was found to predict ischemia and infarction with 60 and 180-minute duration and concluded SD to be an independent mechanistic biomarker for DCI and delayed infarction in aSAH\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. This is consistent with previous literature linking SD and spreading ischemia to worse outcomes in aSAH\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. In the current study, we once again identified a triphasic probability curve of SD which has now been demonstrated in patients with traumatic brain injury\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e and ischemic stroke\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Overall, it appears that there in increased risk of SD (lower limit of autoregulation) as high as a MAP of 90-100mmHg in this population, however we do not propose that this be used as in indiscriminate target. Instead, the use of the combination of autoregulation and SD monitoring could play an important role in better understanding the risk that a given patient may be at in terms of ischemia. For example, the occurrence of SD may be an indicator of metabolically compromised tissue (at risk of DCI) and progressively worsening duration of the ECoG depression could indicate the need to further optimize physiologic targets to avoid ischemia using MAPopt or other approaches\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePrevious data on the relationship between SD and autoregulation was summarized in a recent comprehensive review\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. In addition to the associations of ORx and OSRx that we previously reported\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, another group found associations between SD and impaired autoregulation as measured by the PRx\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, hypothesizing that SD may in fact be the source of impaired autoregulation. Interestingly, these findings were different depending on whether a parenchymal or ventricular source of ICP monitoring was used. In our data, PRx values from both the parenchymal and ventricular source demonstrated an inconsistent relationship with SD. Both tended to decrease prior to SD and generally tended to increase after SD with significant positive slopes for both sources. These PRx trends indicate variability in relationship to SD however could be consistent with the previous associations of SD leading to worsening PRx\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Since PRx was not consistently associated with outcome, it seems that the association between ORx and OSRx with SD may be more clinically relevant.\u003c/p\u003e \u003cp\u003eThe relationship between SD and autoregulation may also be more complex, as locally impaired autoregulation at the time of SD has also been observed\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Therefore, SD may both be triggered by globally impaired autoregulation and may further worsen autoregulation in the region of SD as one mechanism of ongoing tissue metabolic stress. This potentially cyclical relationship is therefore one explanation for these seemingly contradictory findings.\u003c/p\u003e\n\u003ch3\u003eLimitations\u003c/h3\u003e\n\u003cp\u003eWhile there are notable strengths to this study including high-resolution recordings over a long period of time in a large, relatively homogeneous cohort of aSAH patients, there are clearly some limitations. Not all subjects had all parameters measured due to practice and technology changes over the course of monitoring. A relatively small number of only surgical patients had SD monitoring, so it is unknown if our observations related to SD apply to non-surgical patients as well, however it seems plausible that similar pathophysiology is at play. In addition, there is a risk that the continuous indices do not reflect the tissue most at risk for SD since the monitors used to generate these indices are typically placed contralateral to the surgical site and therefore the SD monitoring electrode site. Finally, since this was primarily a cohort of more severely injured subjects requiring multimodality monitoring, the applicability to lower-grade patients is unknown.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eImpairment in cerebral autoregulation in aSAH is most associated with worse clinical outcomes and occurrence of SD when using ORx and OSRx. Impaired autoregulation appears to precede SD occurrence rather than be a result of SD occurrence, though this could be a cyclical relationship. Targeting the optimal MAP or cerebral perfusion pressure in patients with aSAH should use ORx and/or OSRx as the input function rather than intracranial pressure. The combined use of continuous autoregulation monitoring to determine the optimum blood pressure target with monitoring of SD as a mechanism of ongoing metabolic stress and risk of ischemia may be a rational strategy to improve outcomes in patients with aSAH.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgements:\u003c/h2\u003e \u003cp\u003enone\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eVergouwen MD, Vermeulen M, van Gijn J, 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. 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Brain. 2014;137(Pt 11):2960\u0026ndash;72. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/brain/awu241\u003c/span\u003e\u003cspan address=\"10.1093/brain/awu241\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cu\u003eTable 1\u003c/u\u003e- Associations of Patient Characteristics, Injury Severities, and Autoregulation Indices with Clinical Outcome at Discharge.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"743\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.129205921938087%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.611036339165544%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGood outcome at DC\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(mRS 0-3) N=118\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.380888290713326%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePoor outcome at DC\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(mRS 4-6) N=202\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.113055181695827%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.765814266487215%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003etest\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.129205921938087%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge in years: Mean (StD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.611036339165544%\" valign=\"top\"\u003e\n \u003cp\u003e51.81 (12.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.380888290713326%\" valign=\"top\"\u003e\n \u003cp\u003e60.46 (13.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.113055181695827%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;0.0001\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.765814266487215%\" valign=\"top\"\u003e\n \u003cp\u003et-test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.129205921938087%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale Sex: N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.611036339165544%\" valign=\"top\"\u003e\n \u003cp\u003e70 (59.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.380888290713326%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;139 (68.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.113055181695827%\" valign=\"top\"\u003e\n \u003cp\u003e0.0898\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.765814266487215%\" valign=\"top\"\u003e\n \u003cp\u003eFisher\u0026rsquo;s exact test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.129205921938087%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdmission GCS: Median\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.611036339165544%\" valign=\"top\"\u003e\n \u003cp\u003e15 (n=29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.380888290713326%\" valign=\"top\"\u003e\n \u003cp\u003e10 (n=94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.113055181695827%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;0.0001\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.765814266487215%\" valign=\"top\"\u003e\n \u003cp\u003eMann Whitney test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.129205921938087%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdmission WFNS: Median\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.611036339165544%\" valign=\"top\"\u003e\n \u003cp\u003e1 (n=28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.380888290713326%\" valign=\"top\"\u003e\n \u003cp\u003e4 (n=87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.113055181695827%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;0.0001\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.765814266487215%\" valign=\"top\"\u003e\n \u003cp\u003eMann Whitney test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.129205921938087%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdmission mFS: Median\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.611036339165544%\" valign=\"top\"\u003e\n \u003cp\u003e4 (n=24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.380888290713326%\" valign=\"top\"\u003e\n \u003cp\u003e4 (n=58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.113055181695827%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;0.0001\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.765814266487215%\" valign=\"top\"\u003e\n \u003cp\u003eMann Whitney test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.129205921938087%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVasospasm (score=0-3*): Median\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.611036339165544%\" valign=\"top\"\u003e\n \u003cp\u003e0 (n=103)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.380888290713326%\" valign=\"top\"\u003e\n \u003cp\u003e0 (n=157)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.113055181695827%\" valign=\"top\"\u003e\n \u003cp\u003e0.2991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.765814266487215%\" valign=\"top\"\u003e\n \u003cp\u003eFisher\u0026rsquo;s exact test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.129205921938087%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.611036339165544%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.380888290713326%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.113055181695827%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.765814266487215%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.129205921938087%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePRx EVD:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMean (StD)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.611036339165544%\" valign=\"top\"\u003e\n \u003cp\u003e0.11 (0.10) (n=68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.380888290713326%\" valign=\"top\"\u003e\n \u003cp\u003e0.09 (0.13)(n=122)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.113055181695827%\" valign=\"top\"\u003e\n \u003cp\u003e0.2546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.765814266487215%\" valign=\"top\"\u003e\n \u003cp\u003et-test**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.129205921938087%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePRx parenchymal:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMean(StD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.611036339165544%\" valign=\"top\"\u003e\n \u003cp\u003e0.17 (0.12) (n=68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.380888290713326%\" valign=\"top\"\u003e\n \u003cp\u003e0.18(0.14)(n=132)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.113055181695827%\" valign=\"top\"\u003e\n \u003cp\u003e0.6869\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.765814266487215%\" valign=\"top\"\u003e\n \u003cp\u003et-test**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.129205921938087%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eORx:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMean (StD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.611036339165544%\" valign=\"top\"\u003e\n \u003cp\u003e0.03 (n=95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.380888290713326%\" valign=\"top\"\u003e\n \u003cp\u003e0.06 (n=173)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.113055181695827%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.0005\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.765814266487215%\" valign=\"top\"\u003e\n \u003cp\u003et-test**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.129205921938087%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOSRx (average R/L):\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMean (StD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.611036339165544%\" valign=\"top\"\u003e\n \u003cp\u003e0.05 (n=98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.380888290713326%\" valign=\"top\"\u003e\n \u003cp\u003e0.12 (n=167)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.113055181695827%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;0.0001\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.765814266487215%\" valign=\"top\"\u003e\n \u003cp\u003et-test**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.129205921938087%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCBFRx:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMean (StD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.611036339165544%\" valign=\"top\"\u003e\n \u003cp\u003e0.25 (n=66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.380888290713326%\" valign=\"top\"\u003e\n \u003cp\u003e0.25 (n=103)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.113055181695827%\" valign=\"top\"\u003e\n \u003cp\u003e0.9551\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.765814266487215%\" valign=\"top\"\u003e\n \u003cp\u003et-test**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e* 0= no vasospasm, 3=severe vasospasm\u003c/p\u003e\n\u003cp\u003e**For each autoregulation index Fisher\u0026rsquo;s transformation of correlation coefficient was applied to the data before two-sample t-test.\u003c/p\u003e\n\u003cp\u003eDC= Discharge, mRS= modified Rankin Scale, StD= Standard deviation, GCS=Glasgow Coma Scale score, WFNS= World Federation of Neurosurgical Societies Grade, mFS= modified Fisher Scale, PRx= Pressure reactivity, EVD= external ventricular drain, ORx= Oxygen reactivity, OSRx= Oxygen saturation reactivity, CBFRx= Cerebral blood flow reactivity\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eTable 2\u003c/u\u003e Pre- and Post-SD Trend in Autoregulation Based on Linear Mixed Effects Models fit to serial 1-minute bin data up to 60 minutes before and after a SD cluster. Bold/italic values are significant. Grey boxes indicate consistent trends in direction of slope and significance.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"568\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.887323943661972%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAutoregulation\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eIndex \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.95774647887324%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.633802816901408%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePre-SD\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.52112676056338%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePost-Sd\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.357142857142858%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSlope (/min)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.571428571428573%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSlope (/min)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.642857142857142%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.887323943661972%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePRx EVD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.95774647887324%\" valign=\"top\"\u003e\n \u003cp\u003eSingle SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.95774647887324%\" valign=\"top\"\u003e\n \u003cp\u003e-0.00049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.67605633802817%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.0349\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.901408450704224%\" valign=\"top\"\u003e\n \u003cp\u003e0.00068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.619718309859154%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.0014\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.28767123287671%\" valign=\"top\"\u003e\n \u003cp\u003eMultiple SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.28767123287671%\" valign=\"top\"\u003e\n \u003cp\u003e-0.00058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.438356164383563%\" valign=\"top\"\u003e\n \u003cp\u003e0.0556\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.91780821917808%\" valign=\"top\"\u003e\n \u003cp\u003e0.00017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.068493150684931%\" valign=\"top\"\u003e\n \u003cp\u003e0.4866\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.28767123287671%\" valign=\"top\"\u003e\n \u003cp\u003ePooled\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.28767123287671%\" valign=\"top\"\u003e\n \u003cp\u003e-0.00052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.438356164383563%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.0043\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.91780821917808%\" valign=\"top\"\u003e\n \u003cp\u003e0.00048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.068493150684931%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.0025\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.887323943661972%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePRx Parenchymal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.95774647887324%\" valign=\"top\"\u003e\n \u003cp\u003eSingle SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.95774647887324%\" valign=\"top\"\u003e\n \u003cp\u003e-0.00040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.67605633802817%\" valign=\"top\"\u003e\n \u003cp\u003e0.1354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.901408450704224%\" valign=\"top\"\u003e\n \u003cp\u003e0.00083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.619718309859154%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.0021\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.28767123287671%\" valign=\"top\"\u003e\n \u003cp\u003eMultiple SD\u003c/p\u003e\n \u003c/td\u003e\n 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\u003c/td\u003e\n \u003ctd width=\"21.91780821917808%\" valign=\"top\"\u003e\n \u003cp\u003e-0.00058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.068493150684931%\" valign=\"top\"\u003e\n \u003cp\u003e0.2271\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"neurocritical-care","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"neca","sideBox":"Learn more about [Neurocritical Care](http://link.springer.com/journal/12028)","snPcode":"12028","submissionUrl":"https://www.editorialmanager.com/neca/default2.aspx","title":"Neurocritical Care","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"subarachnoid hemorrhage, delayed cerebral ischemia, spreading depolarization, cerebral autoregulation, cerebral ischemia","lastPublishedDoi":"10.21203/rs.3.rs-4451509/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4451509/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eImpairment in cerebral autoregulation has been proposed as a potentially targetable factor in patients with aneurysmal subarachnoid hemorrhage (aSAH), however there are different continuous measures that can be used to calculate the state of autoregulation. In addition, it has previously been proposed that there may be an association of impaired autoregulation with the occurrence of spreading depolarization (SD) events.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eSubjects with invasive multimodal monitoring and aSAH were enrolled in an observational study. Autoregulation indices were prospectively calculated from this database as a 10 second moving correlation coefficient between various cerebral blood flow (CBF) surrogates and mean arterial pressure (MAP). In subjects with subdural ECoG (electrocorticography) monitoring, SD was also scored. Associations between clinical outcomes using the mRS (modified Rankin Scale) and occurrence of either isolated or clustered SD was assessed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003e320 subjects were included, 47 of whom also had ECoG SD monitoring. As expected, baseline severity factors such as mFS and WFNS (World Federation of Neurosurgical Societies scale) were strongly associated with the clinical outcome. SD probability was related to blood pressure in a triphasic pattern with a linear increase in probability below MAP of ~\u0026thinsp;100mmHg. Autoregulation indices were available for intracranial pressure (ICP) measurements (PRx), PbtO2 from Licox (ORx), perfusion from the Bowman perfusion probe (CBFRx), and cerebral oxygen saturation measured by near infrared spectroscopy (OSRx). Only worse ORx and OSRx were associated with worse clinical outcomes. ORx and OSRx also were found to both increase in the hour prior to SD for both sporadic and clustered SD.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eImpairment in autoregulation in aSAH is associated with worse clinical outcomes and occurrence of SD when using ORx and OSRx. Impaired autoregulation precedes SD occurrence. Targeting the optimal MAP or cerebral perfusion pressure in patients with aSAH should use ORx and/or OSRx as the input function rather than intracranial pressure.\u003c/p\u003e","manuscriptTitle":"Oxygen-based autoregulation indices associated with clinical outcomes and spreading depolarization in aSAH","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-11 20:36:20","doi":"10.21203/rs.3.rs-4451509/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2024-05-29T10:19:28+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-27T01:55:39+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Neurocritical Care","date":"2024-05-27T01:50:18+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-23T18:19:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"Neurocritical Care","date":"2024-05-23T11:35:50+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"neurocritical-care","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"neca","sideBox":"Learn more about [Neurocritical Care](http://link.springer.com/journal/12028)","snPcode":"12028","submissionUrl":"https://www.editorialmanager.com/neca/default2.aspx","title":"Neurocritical Care","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"e3e6990e-0f4e-4409-8f71-fb89c04ca009","owner":[],"postedDate":"June 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-09-02T16:05:16+00:00","versionOfRecord":{"articleIdentity":"rs-4451509","link":"https://doi.org/10.1007/s12028-024-02088-x","journal":{"identity":"neurocritical-care","isVorOnly":false,"title":"Neurocritical Care"},"publishedOn":"2024-08-27 15:58:09","publishedOnDateReadable":"August 27th, 2024"},"versionCreatedAt":"2024-06-11 20:36:20","video":"","vorDoi":"10.1007/s12028-024-02088-x","vorDoiUrl":"https://doi.org/10.1007/s12028-024-02088-x","workflowStages":[]},"version":"v1","identity":"rs-4451509","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4451509","identity":"rs-4451509","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}