Predictor Of Delayed Cerebral Ischemia Research Articles

A Longitudinal Model for a Dynamic Risk Score to Predict Delayed Cerebral Ischemia after Subarachnoid Hemorrhage.

Accurate longitudinal risk prediction for DCI (delayed cerebral ischemia) occurrence after subarachnoid hemorrhage (SAH) is essential for clinicians to administer appropriate and timely diagnostics, thereby improving treatment planning and outcome. This study aimed to develop an improved longitudinal DCI prediction model and evaluate its performance in predicting DCI between day 4 and 14 after aneurysm rupture. Two DCI classification models were trained: (1) a static model based on routinely collected demographics and SAH grading scores and (2) a dynamic model based on results from laboratory and blood gas analysis anchored at the time of DCI. A combined model was derived from these two using a voting approach. Multiple classifiers, including Logistic Regression, Support Vector Machines, Random Forests, Histogram-based Gradient Boosting, and Extremely Randomized Trees, were evaluated through cross-validation using anchored data. A leave-one-out simulation was then performed on the best-performing models to evaluate their longitudinal performance using time-dependent Receiver Operating Characteristic (ROC) analysis. The training dataset included 218 patients, with 89 of them developing DCI (41%). In the anchored ROC analysis, the combined model achieved a ROC AUC of 0.73 ± 0.05 in predicting DCI onset, the static and the dynamic model achieved a ROC AUC of 0.69 ± 0.08 and 0.66 ± 0.08, respectively. In the leave-one-out simulation experiments, the dynamic and voting model showed a highly dynamic risk score (intra-patient score range was 0.25 [0.24, 0.49] and 0.17 [0.12, 0.25] for the dynamic and the voting model, respectively, for DCI occurrence over the course of disease. In the time-dependent ROC analysis, the dynamic model performed best until day 5.4, and afterwards the voting model showed the best performance. A machine learning model for longitudinal DCI risk assessment was developed comprising a static and a dynamic sub-model. The longitudinal performance evaluation highlighted substantial time dependence in model performance, underscoring the need for a longitudinal assessment of prediction models in intensive care settings. Moreover, clinicians need to be aware of these performance variations when performing a risk assessment and weight the different model outputs correspondingly.

Predicting who has delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage using machine learning approach: a multicenter, retrospective cohort study.

Early prediction of delayed cerebral ischemia (DCI) is critical to improving the prognosis of aneurysmal subarachnoid hemorrhage (aSAH). Machine learning (ML) algorithms can learn from intricate information unbiasedly and facilitate the early identification of clinical outcomes. This study aimed to construct and compare the ability of different ML models to predict DCI after aSAH. Then, we identified and analyzed the essential risk of DCI occurrence by preoperative clinical scores and postoperative laboratory test results. This was a multicenter, retrospective cohort study. A total of 1039 post-operation patients with aSAH were finally included from three hospitals in China. The training group contained 919 patients, and the test group comprised 120 patients. We used five popular machine-learning algorithms to construct the models. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, precision, and f1 score were used to evaluate and compare the five models. Finally, we performed a Shapley Additive exPlanations analysis for the model with the best performance and significance analysis for each feature. A total of 239 patients with aSAH (23.003%) developed DCI after the operation. Our results showed that in the test cohort, Random Forest (RF) had an AUC of 0.79, which was better than other models. The five most important features for predicting DCI in the RF model were the admitted modified Rankin Scale, D-Dimer, intracranial parenchymal hematoma, neutrophil/lymphocyte ratio, and Fisher score. Interestingly, clamping or embolization for the aneurysm treatment was the fourth button-down risk factor in the ML model. In this multicenter study, we compared five ML methods, among which RF performed the best in DCI prediction. In addition, the essential risks were identified to help clinicians monitor the patients at high risk for DCI more precisely and facilitate timely intervention.

Early localization of tissue at risk for delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage: blood distribution on initial imaging vs early CT perfusion.

Delayed cerebral ischemia (DCI) is a potentially reversible adverse event after aneurysmal subarachnoid hemorrhage (aSAH), when early detected and treated. Computer tomography perfusion (CTP) is used to identify the tissue at risk for DCI. In this study, the predictive power of early CTP was compared with that of blood distribution on initial CT for localization of tissue at risk for DCI. A consecutive patient cohort with aSAH treated between 2012 and 2020 was retrospectively analyzed. Blood distribution on CT was semi-quantitatively assessed with the Hijdra-score. The vessel territory with the most surrounding blood and the one with perfusion deficits on CTP performed on day 3 after ictus were considered to be at risk for DCI, respectively. A total of 324 patients were included. Delayed infarction occurred in 17% (56/324) of patients. Early perfusion deficits were detected in 82% (46/56) of patients, 85% (39/46) of them developed infarction within the predicted vessel territory at risk. In 46% (25/56) a vessel territory at risk was reliably determined by the blood distribution. For the prediction of DCI, blood amount/distribution was inferior to CTP. Concerning the identification of "tissue at risk" for DCI, a combination of both methods resulted in an increase of sensitivity to 64%, positive predictive value to 58%, and negative predictive value to 92%. Regarding the DCI-prediction, early CTP was superior to blood amount/distribution, while a consideration of subarachnoid blood distribution may help identify the vessel territories at risk for DCI in patients without early perfusion deficits.

Evaluation of CSF 8-iso-prostaglandin F2α and erythrocyte anisocytosis as prognostic biomarkers for delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage

Delayed cerebral ischemia (DCI) is a serious, life-threatening, complication affecting patients who have survived the initial bleeding from a ruptured intracranial aneurysm. Due to the challenging diagnosis, potential DCI prognostic markers should be of value in clinical practice. According to recent reports isoprostanes and red blood cell distribution (RDW) showed to be promising in this respect. We conducted a prospective study of 27 aSAH patients and control group (n = 8). All patients from the study group were treated within the first day of the initial bleeding. We collected data regarding clinical status and results of biochemical, and radiological examinations. We measured cerebrospinal fluid (CSF) concentration of 8-iso-prostaglandin F2α (F2-IsoP) and RDW on days 1, 3, and 5. Both CSF F2-IsoP level and RDW-SD measured on day 1 were significant predictors of DCI. The receiver operating characteristics curve for DCI prediction based on the multivariate model yielded an area under the curve of 0.924 (95% CI 0.824–1.000, p < 0.001). In our study, the model based on the combination of RDW and the level of isoprostanes in CSF on the first day after the initial bleeding showed a prognostic value for DCI prediction. Further studies are required to validate this observation.

Cerebrospinal fluid volume as an early radiological factor for clinical course prediction after aneurysmal subarachnoid hemorrhage. A pilot study

BackgroundThe pathological mechanisms following aneurysmal subarachnoid hemorrhage (SAH) are poorly understood. Limited clinical evidence exists on the association between cerebrospinal fluid (CSF) volume and the risk of delayed cerebral ischemia (DCI) or cerebral vasospasm (CV). In this study, we raised the hypothesis that the amount of CSF or its ratio to hemorrhage blood volume, as determined from non-contrast Computed Tomography (NCCT) images taken on admission, could be a significant predictor for CV and DCI. MethodsThe pilot study included a retrospective analysis of NCCT scans of 49 SAH patients taken shortly after an aneurysm rupture (33 males, 16 females, mean age 56.4 ± 15 years). The SynthStrip and Slicer3D software tools were used to extract radiological factors – CSF, brain, and hemorrhage volumes from the NCCT images. The “pure” CSF volume (VCSF) was estimated in the range of [−15, 15] Hounsfield units (HU). ResultsVCSF was negatively associated with the risk of CV occurrence (p = 0.0049) and DCI (p = 0.0069), but was not associated with patients’ outcomes. The hemorrhage volume (VSAH) was positively associated with an unfavorable outcome (p = 0.0032) but was not associated with CV/DCI. The ratio VSAH/VCSF was positively associated with, both, DCI (p = 0.031) and unfavorable outcome (p = 0.002). The CSF volume normalized by the brain volume showed the highest characteristics for DCI prediction (AUC = 0.791, sensitivity = 0.80, specificity = 0.812) and CV prediction (AUC = 0.769, sensitivity = 0.812, specificity = 0.70). ConclusionIt was demonstrated that “pure” CSF volume retrieved from the initial NCCT images of SAH patients (including CV, Non-CV, DCI, Non-DCI groups) is a more significant predictor of DCI and CV compared to other routinely used radiological biomarkers. VCSF could be used to predict clinical course as well as to personalize the management of SAH patients. Larger multicenter clinical trials should be performed to test the added value of the proposed methodology.

Combined Transcranial Doppler and Melatonin Levels to Predict Delayed Cerebral Ischemia After Subarachnoid Hemorrhage.

To investigate the early prediction value of transcranial Doppler ultrasound (TCD) combined with serum melatonin level for delayed cerebral ischemia (DCI) caused by subarachnoid hemorrhage (SAH). This paper is a prospective study. A total of 120 patients with SAH treated were included. The patients were divided into the DCI group (40 cases) and non-DCI group (80 cases) according to whether DCI occurred 14 days after SAH (DCI usually occurs 4 to 14d after bleeding). Baseline data, serum melatonin level, and TCD test results within 24hours after admission were compared between the 2 groups. Multivariate logistic analysis was used to analyze the factors affecting the occurrence of DCI after SAH. The value of serum melatonin level, middle cerebral artery mean blood flow velocity (MBFV) and their combination in predicting DCI in SAH patients was evaluated. Univariate analysis showed that there were statistically significant differences in the proportion of Fisher grade, Hunt-Hess grade, serum melatonin level, middle cerebral artery systolic blood flow velocity (Vs), MBFV and pulse index (PI) between the 2 groups ( P <0.05). Serum melatonin levels, middle cerebral artery Vs, MBFV, and PI in the DCI group were higher than those in non-DCI group. Logistic regression (LR) analysis showed that serum melatonin level (OR=1.796, 95% CI: 1.575-4.123) and middle cerebral artery MBFV (OR=3.279, 95% CI: 2.112-4.720] were the influencing factors for DCI in SAH patients ( P <0.05). Middle cerebral artery MBFV and serum melatonin levels were higher in patients with SAH complicated with DCI, and the combination of the 2 could provide a reference for early clinical prediction of DCI in patients with aneurysmal subarachnoid hemorrhage (aSAH).

Predictive Value of Quantitative Electroencephalogram Combined with Transcranial Doppler Ultrasound in Delayed Cerebral Ischemia after Subarachnoid Hemorrhage

ObjectiveTo explore the predictive value of transcranial Doppler ultrasound (TCD) combined with quantitative electroencephalogram (QEEG) in delayed cerebral ischemia (DCI) caused by aneurysmal subarachnoid hemorrhage (aSAH). MethodsThe subjects were 105 patients with aSAH from June 2020 to December 2022. Patients were divided into DCI group (n=34) and non-DCI group (n=71) according to the presence of DCI 14d after onset. Further comparison was conducted on the baseline data as well as the parameters of QEEG and TCD within 24h after admission. Multivariate logistic analysis was performed to investigate risk factors related to DCI within 14 days of admission in aSAH patients. ResultsThere were significant differences in the comparison of the proportion of Hunt-Hess grading, relative δ power (RDP), relative α power (RAP), relative α/β power ratio (ADR), as well as peak systolic velocity (Vs), mean blood flow velocity (MBFV) and pulsatility index (PI) of middle cerebral artery between the two groups (P<0.05). Furthermore, Logistic regression analysis revealed that ADR [OR=1.668, 95%CI (1.369, 4.345)] and MBFV of middle cerebral artery [OR=3.279, 95%CI (2.332, 6.720)] were risk factors for the occurrence of DCI in aSAH patients (P<0.05). In addition, evaluation of the predictive value revealed that combined use of the two indicators showed the highest predictive value (AUC: 0.959, 95%CI: 0.896∼0.993). ConclusionPatients with aSAH complicated by DCI have relatively higher MBFV of middle cerebral artery and ADR. Combined use of the two indicators can provide reference for early prediction of DCI in aSAH patients.

Clinical Value of Serum Secretoneurin Levels in Prediction of Delayed Cerebral Ischemia and Prognostic Analysis of Aneurysmal Subarachnoid Hemorrhage: A Prospective Cohort Study.

Secretoneurin is a neuropeptide with several neuroprotective properties. Here, we discuss the importance of serum secretoneurin in assessing severity and predicting delayed cerebral ischemia (DCI) and functional outcomes following aneurysmal subarachnoid hemorrhage (aSAH). A prospective cohort study of 167 patients with aSAH and 100 controls was performed to determine serum secretoneurin levels. Severity was reflected by the Hunt-Hess and modified Fisher scores. Prognostic parameters included DCI and poor 6-month prognosis (extended Glasgow outcome scale scores of 1-4). Univariate analysis followed by multivariate analysis was performed to determine the correlation between severity and prognosis. Compared to controls, patients exhibited a marked elevation in serum secretoneurin levels. Serum secretoneurin levels, which were independently correlated with Hunt-Hess scores and modified Fisher scores, independently predicted DCI and bad 6-month prognosis. Serum secretoneurin levels, which were linearly related to the risk of DCI and poor prognosis under a restricted cubic spline, effectively distinguished the risks under the receiver operating characteristic (ROC) curve. Subgroup analysis for prognosis or DCI prediction revealed no substantial interactions between serum secretoneurin levels and other variables, such as age, sex, hypertension, diabetes, alcohol consumption, and cigarette consumption. In addition, the prognosis model, in which serum secretoneurin, Hunt-Hess scale, and modified Fisher scale were merged, was graphically represented by a nomogram and performed well under the calibration, decision, and ROC curves. Serum secretoneurin levels significantly increased after aSAH, which was intimately correlated with disease severity and independently associated with DCI and worse outcomes, indicating that serum secretoneurin may be a potential prognostic biomarker of aSAH.

External Validation of an Extreme Gradient Boosting Model for Prediction of Delayed Cerebral Ischemia After Aneurysmal Subarachnoid Hemorrhage

Delayed cerebral ischemia (DCI) may significantly worsen the functional status of patients with aneurysmal subarachnoid hemorrhage (aSAH). Several authors have designed predictive models for early identification of patients at risk of post-aSAH DCI. In this study, we externally validate an extreme gradient boosting (EGB) forecasting model for post-aSAH DCI prediction. A 9-year institutional retrospective review of patients with aSAH was performed. Patients were included if they underwent surgical or endovascular treatment and had available follow-up data. DCI was diagnosed as new-onset neurologic deficits at 4-12 days after aneurysm rupture, defined as worsening Glasgow Coma Scale score for ≥2 points, and new ischemic infarcts at imaging. We collected 267 patients with aSAH. At admission, median Hunt-Hess score was 2 (range, 1-5), median Fisher score 3 (range, 1-4), and median modified Fisher score 3 (range, 1-4). One-hundred and forty-five patients underwent external ventricular drainage placement for hydrocephalus (54.3%). The ruptured aneurysms were treated with clipping (64%), coiling (34.8%), and stent-assisted coiling (1.1%). Fifty-eight patients (21.7%) were diagnosed with clinical DCI and 82 (30.7%) with asymptomatic imaging vasospasm. The EGB classifier correctly predicted 19 cases of DCI (7.1%) and 154 cases of no-DCI (57.7%), achieving sensitivity of 32.76% and specificity of 73.68%. The calculated F1 score and accuracy were 0.288% and 64.8%, respectively. We validated that the EGB model is a potential assistant tool to predict post-aSAH DCI in clinical practice, finding moderate-high specificity but low sensitivity. Future research should investigate the underlying pathophysiology of DCI to allow the development of high-performing forecasting models.

Comparison of Conventional Logistic Regression and Machine Learning Methods for Predicting Delayed Cerebral Ischemia After Aneurysmal Subarachnoid Hemorrhage: A Multicentric Observational Cohort Study.

BackgroundTimely and accurate prediction of delayed cerebral ischemia is critical for improving the prognosis of patients with aneurysmal subarachnoid hemorrhage. Machine learning (ML) algorithms are increasingly regarded as having a higher prediction power than conventional logistic regression (LR). This study aims to construct LR and ML models and compare their prediction power on delayed cerebral ischemia (DCI) after aneurysmal subarachnoid hemorrhage (aSAH).MethodsThis was a multicenter, retrospective, observational cohort study that enrolled patients with aneurysmal subarachnoid hemorrhage from five hospitals in China. A total of 404 aSAH patients were prospectively enrolled. We randomly divided the patients into training (N = 303) and validation cohorts (N = 101) according to a ratio of 75–25%. One LR and six popular ML algorithms were used to construct models. The area under the receiver operating characteristic curve (AUC), accuracy, balanced accuracy, confusion matrix, sensitivity, specificity, calibration curve, and Hosmer–Lemeshow test were used to assess and compare the model performance. Finally, we calculated each feature of importance.ResultsA total of 112 (27.7%) patients developed DCI. Our results showed that conventional LR with an AUC value of 0.824 (95%CI: 0.73–0.91) in the validation cohort outperformed k-nearest neighbor, decision tree, support vector machine, and extreme gradient boosting model with the AUCs of 0.792 (95%CI: 0.68–0.9, P = 0.46), 0.675 (95%CI: 0.56–0.79, P < 0.01), 0.677 (95%CI: 0.57–0.77, P < 0.01), and 0.78 (95%CI: 0.68–0.87, P = 0.50). However, random forest (RF) and artificial neural network model with the same AUC (0.858, 95%CI: 0.78–0.93, P = 0.26) were better than the LR. The accuracy and the balanced accuracy of the RF were 20.8% and 11% higher than the latter, and the RF also showed good calibration in the validation cohort (Hosmer-Lemeshow: P = 0.203). We found that the CT value of subarachnoid hemorrhage, WBC count, neutrophil count, CT value of cerebral edema, and monocyte count were the five most important features for DCI prediction in the RF model. We then developed an online prediction tool (https://dynamic-nomogram.shinyapps.io/DynNomapp-DCI/) based on important features to calculate DCI risk precisely.ConclusionsIn this multicenter study, we found that several ML methods, particularly RF, outperformed conventional LR. Furthermore, an online prediction tool based on the RF model was developed to identify patients at high risk for DCI after SAH and facilitate timely interventions.Clinical Trial Registrationhttp://www.chictr.org.cn, Unique identifier: ChiCTR2100044448.

Low serum albumin as a risk factor for delayed cerebral ischemia following aneurysmal subarachnoid hemorrhage: eICU collaborative research database analysis.

Delayed cerebral ischemia (DCI) represents a devastating complication of aneurysmal subarachnoid hemorrhage (aSAH) and is a significant predictor of morbidity and mortality. Recent studies have implicated inflammatory processes in the pathogenesis of DCI. aSAH patient data were retrospectively obtained from the eICU Collaborative Research Database (eICU CRD). Multivariable logistic regression models and receiver operating characteristic (ROC) curve analyses were employed to assess the association between low serum albumin (<3.4 g/dL) and clinical endpoints: DCI and in-hospital mortality. Among 276 aSAH patients included in the analysis, 35.5% (N.=98) presented with low serum albumin levels and demonstrated a higher incidence of DCI (18.4% vs. 8.4%, OR=2.45, 95% CI=1.17, 5.10; P=0.017) and in-hospital mortality (27.6% vs. 16.3%, OR=1.95, 95% CI=1.08, 3.54; P=0.027) compared to patients with normal admission albumin values. In a multivariable model controlling for age and World Federation of Neurosurgical Societies grade, low serum albumin remained significantly associated with DCI (OR=2.52, 95% CI=1.18, 5.36; P=0.017), but not with in-hospital mortality. A combined model for prediction of DCI, encompassing known risk factors in addition to low serum albumin, achieved an area under the curve of 0.65 (sensitivity = 0.55, specificity = 0.75). Serum albumin, a routine and inexpensive laboratory measurement, may potentially aid in the identification of patients with aSAH at risk for the development of DCI.

Day 2 neutrophil-to-lymphocyte and platelet-to-lymphocyte ratios for prediction of delayed cerebral ischemia in subarachnoid hemorrhage.

Recent evidence has suggested that an admission neutrophil-to-lymphocyte ratio (NLR) of ≥ 5.9 predicts delayed cerebral ischemia (DCI) in aneurysmal subarachnoid hemorrhage (aSAH). The primary aims of this study were to assess reproducibility and to ascertain the predictive ability of NLR on subsequent days postictus. Secondary aims included identification of additional inflammatory markers. A single-center, retrospective study of all patients aged ≥ 18 years with aSAH between May 2014 and July 2018 was performed. Patient characteristics, DCI incidence, operative features, and outcomes (on discharge and at 3 months postictus) were recorded. C-reactive protein (CRP) and full blood count differentials were recorded on admission and through day 8 postictus or at discharge. In total, 403 patients were included in the final analysis. Ninety-six patients (23.8%) developed DCI with a median time from ictus of 6 days (IQR 3.25-8 days). A platelet-to-lymphocyte ratio (PLR) cutoff ≥ 157 and CRP cutoff ≥ 27 was used in our cohort. In a multiple binary logistic regression model, after controlling for known DCI predictors, day 2 NLR ≥ 5.9 (OR 2.194, 95% CI 1.099-4.372; p = 0.026), day 1 PLR ≥ 157 (OR 2.398, 95% CI 1.1072-5.361; p = 0.033), day 2 PLR ≥ 157 (OR 2.676, 95% CI 1.344-5.329; p = 0.005), and CRP ≥ 27 on days 3, 4, and 5 were predictive of DCI. The results of this study have confirmed the association between NLR and DCI and have demonstrated the predictive potential of PLR and CRP, suggesting that NLR and PLR at day 2, and CRP from day 3 onward, may be better predictors of DCI than those measurements at the time of ictus.

Abstract TMP12: Artificial Intelligence Prediction Of Delayed Cerebral Ischemia After Cerebral Aneurysm Rupture

Background: Aneurysmal subarachnoid hemorrhage (aSAH) results in significant mortality and disability, which is worsened by the development of Delayed Cerebral Ischemia (DCI). Tests to identify patients with DCI prospectively are needed. Objective: We created a machine learning (ML) system based on clinical variables to predict DCI in aSAH patients and to determine which variables have the most impact on DCI prediction. Methods: We performed a retrospective cohort study of aSAH patients from January 2006 to September 2014. The ML algorithm was trained on age, sex, HTN, diabetes, hyperlipidemia, CHF, CAD, smoking history, family history of aneurysm, Fisher Grade, Hunt and Hess score, and external ventricular drain (EVD) placement. Prediction outcome of the ML algorithm was DCI+, which was defined as new neurologic deterioration that could not be attributed to aneurysm re-bleeding, hydrocephalus, infection, seizure, hyponatremia or other metabolic abnormality. SHAP was used to explain and visualize the role of each feature’s contribution to the model prediction. Results: 500 aSAH patients were identified and 369 met inclusion criteria: 70 patients developed DCI (DCI+) and 299 did not (DCI-). Random Forest was selected for this project after a 5-fold cross validation. 276 cases (222 DCI- and 54 DCI+) were used for training and 93 cases (77 DCI- and 16 DCI+) were used for testing the algorithm. The Random Forest ML algorithm predicted DCI: Accuracy: 81.7%, Sensitivity: 12.5%, Specificity: 96.1%, PPV: 40%, and NPV: 84.1%. SHAP value demonstrated Age, EVD placement, Fisher Grade, and Hunt and Hess score, and HTN had the highest predictive values for DCI. Lower age, absence of hypertension, higher Hunt and Hess score, higher Fisher Grade, and EVD placement increased risk of DCI. Conclusion: ML models based upon clinical variable predict DCI with high specificity and modest accuracy. The addition of imaging or other biomarkers may improve the sensitivity of the ML algorithm.

Whole-Brain Permeability Analysis on Admission Improves Prediction of Delayed Cerebral Ischemia Following Aneurysmal Subarachnoid Hemorrhage

PurposeTo evaluate the changes of blood-brain barrier permeability (BBBP) after aneurysmal subarachnoid hemorrhage (aSAH) and find out whether BBBP within 24 h after onset can further improve prediction of delayed cerebral ischemia (DCI). MethodsCT perfusion (CTP) was performed within 24 h after onset and in the DCI time window (DCITW). Whole brain average values of flow extraction product (mKtrans), qualitative and quantitative CTP parameters, and clinical data were compared between DCI and non-DCI groups. The changes of mKtrans were analysed using a Paired t test. Multivariate logistic regression analysis and ROC analyses were performed to identify predictors of DCI and evaluate the predictive performance. ResultsOne hundred and forty of 179 consecutive patients were included, 45 of whom (32%) developed DCI. mKtrans was higher in the DCI group both on admission and in the DCITW (P<0.001). mKtrans decreased significantly in the non-DCI group (P=0.003), but not in DCI group (P=0.285). Multivariate logistic regression analysis showed that mKtrans (OR=1.07, 95%CI: 1.03-1.11, P<0.001), World Federation of Neurosurgery Scale (OR=6.73, 95%CI: 1.09-41.41, P=0.040), Hunt-Hess grade (OR=0.16, 95%CI: 0.02-1.19, P=0.073), modified Fisher Score (OR=3.74, 95%CI: 1.30-10.75, P=0.014), and qualitative CTP (OR=4.31, 95%CI: 1.49-12.47, P=0.007) were independent predictors of DCI. The model with Ktrans produced a larger AUC of 0.88 (95%CI: 0.81-0.95), with corresponding sensitivity and specificity of 84% and 86%, respectively. ConclusionBBBP measurement within 24 h after onset can improve the prediction of DCI. Early moderate BBB disruption may be reversible, whereas severe BBBP disruption indicates the risk of DCI.

Combining Transcranial Doppler and EEG Data to Predict Delayed Cerebral Ischemia After Subarachnoid Hemorrhage.

Delayed cerebral ischemia (DCI) is the leading complication of subarachnoid hemorrhage (SAH). Because DCI was traditionally thought to be caused by large vessel vasospasm, transcranial Doppler ultrasounds (TCDs) have been the standard of care. Continuous EEG has emerged as a promising complementary monitoring modality and predicts increased DCI risk. Our objective was to determine whether combining EEG and TCD data improves prediction of DCI after SAH. We hypothesize that integrating these diagnostic modalities improves DCI prediction. We retrospectively assessed patients with moderate to severe SAH (2011-2015; Fisher 3-4 or Hunt-Hess 4-5) who had both prospective TCD and EEG acquisition during hospitalization. Middle cerebral artery (MCA) peak systolic velocities (PSVs) and the presence or absence of epileptiform abnormalities (EAs), defined as seizures, epileptiform discharges, and rhythmic/periodic activity, were recorded daily. Logistic regressions were used to identify significant covariates of EAs and TCD to predict DCI. Group-based trajectory modeling (GBTM) was used to account for changes over time by identifying distinct group trajectories of MCA PSV and EAs associated with DCI risk. We assessed 107 patients; DCI developed in 56 (51.9%). Univariate predictors of DCI are presence of high-MCA velocity (PSV ≥200 cm/s, sensitivity 27%, specificity 89%) and EAs (sensitivity 66%, specificity 62%) on or before day 3. Two univariate GBTM trajectories of EAs predicted DCI (sensitivity 64%, specificity 62.75%). Logistic regression and GBTM models using both TCD and EEG monitoring performed better. The best logistic regression and GBTM models used both TCD and EEG data, Hunt-Hess score at admission, and aneurysm treatment as predictors of DCI (logistic regression: sensitivity 90%, specificity 70%; GBTM: sensitivity 89%, specificity 67%). EEG and TCD biomarkers combined provide the best prediction of DCI. The conjunction of clinical variables with the timing of EAs and high MCA velocities improved model performance. These results suggest that TCD and cEEG are promising complementary monitoring modalities for DCI prediction. Our model has potential to serve as a decision support tool in SAH management. This study provides Class II evidence that combined TCD and EEG monitoring can identify delayed cerebral ischemia after SAH.

The Role of the Glycocalyx in the Pathophysiology of Subarachnoid Hemorrhage-Induced Delayed Cerebral Ischemia

The glycocalyx is an important constituent of blood vessels located between the bloodstream and the endothelium. It plays a pivotal role in intercellular interactions in neuroinflammation, reduction of vascular oxidative stress, and provides a barrier regulating vascular permeability. In the brain, the glycocalyx is closely related to functions of the blood-brain barrier and neurovascular unit, both responsible for adequate neurovascular responses to potential threats to cerebral homeostasis. An aneurysmal subarachnoid hemorrhage (aSAH) occurs following rupture of an intracranial aneurysm and leads to immediate brain damage (early brain injury). In some cases, this can result in secondary brain damage, also known as delayed cerebral ischemia (DCI). DCI is a life-threatening condition that affects up to 30% of all aSAH patients. As such, it is associated with substantial societal and healthcare-related costs. Causes of DCI are multifactorial and thought to involve neuroinflammation, oxidative stress, neuroinflammation, thrombosis, and neurovascular uncoupling. To date, prediction of DCI is limited, and preventive and effective treatment strategies of DCI are scarce. There is increasing evidence that the glycocalyx is disrupted following an aSAH, and that glycocalyx disruption could precipitate or aggravate DCI. This review explores the potential role of the glycocalyx in the pathophysiological mechanisms contributing to DCI following aSAH. Understanding the role of the glycocalyx in DCI could advance the development of improved methods to predict DCI or identify patients at risk for DCI. This knowledge may also alter the methods and timing of preventive and treatment strategies of DCI. To this end, we review the potential and limitations of methods currently used to evaluate the glycocalyx, and strategies to restore or prevent glycocalyx shedding.

Cerebral circulation time on DSA during endovascular treatment in WFNS grade I aneurysmal SAH patients-a predictor of DCI?

Delayed cerebral ischemia (DCI) remains a contributor to poor outcome following aneurysmal subarachnoid hemorrhage (aSAH). We evaluated cerebral circulation time (CCT) on digital subtraction angiography (DSA) during endovascular treatment (EVT) in WFNS grade I aSAH patients as a predictor of DCI. Of 135 consecutive WNFS grade I aSAH patients, 90 were included. Age, gender, time of DSA from ictus (< 72h or > 72h), Fisher scale, severe vasospasm, development of DCI, EVD-dependent hydrocephalus, re-bleeding, and procedural complications were recorded. CCT was calculated retrospectively from multiphase DSA. Association with DCI was established through univariate and, subsequently, multivariable logistic regression. An optimal threshold value was identified using ROC curve analysis. Patient groups defined by threshold CCT value, DCI, and, subsequently, time of DSA from ictus were analyzed using χ2 and Fisher's exact test. CCT was the only significant factor in the multivariable logistic regression for the outcome development of DCI (OR/second increase in CCT = 1.46 [95% CI 1.14-1.86, p = .003]). When CCT was dichotomized at 8.5s, the odds ratio for developing DCI was 7.12 (95% CI 1.93-26.34, p = .003) for CCT > 8.5s compared with < 8.5s. There was a significant difference for DCI in all patient groups dichotomized by CCT < 8.5s and > 8.5s (all patients, p = .001; patients imaged before and after 72h of ictus, p = .024 and p = .034, respectively). A CCT > 8.5s on DSA during EVT in WFNS grade I aSAH patients is associated with an increased risk of developing DCI and may aid in the management of high-risk patients.

Abstract P41: Integrating Demographics, TCD and EEG Diagnostic Modalities Improves Delayed Cerebral Ischemia Prediction After Subarachnoid Hemorrhage

Introduction: Delayed cerebral ischemia (DCI) is the leading complication of subarachnoid hemorrhage (SAH). Because DCI was traditionally thought to be caused by large vessel vasospasm, transcranial Doppler ultrasounds (TCDs) have been the standard of care. Continuous EEG has emerged as a promising complementary monitoring modality and predicts increased DCI risk. Clinical variables have also been used in DCI prediction. We hypothesize integrating these diagnostic modalities improves DCI prediction. Methods: We assessed 107 patients with moderate-severe SAH (2011-2015) who had both TCD and EEG monitoring during hospitalization. Clinical demographics, including Hunt-Hess and aneurysm treatment (clipping/coiling), were collected via retrospective chart review. Middle cerebral artery (MCA) peak systolic velocities (PSV) and the presence or absence of epileptiform abnormalities (EA), defined as seizures, epileptiform discharges, and rhythmic/periodic activity, were recorded daily. Logistic regressions were used to identify EEG, TCD, and clinical variables associated with DCI. Group-Based Trajectory Modeling (GBTM) was used to account for changes over time by identifying distinct group trajectories of MCA and EA associated with DCI risk. Results: Independent predictors of DCI in logistic regressions are: presence of high MCA velocity (PSV≥200cm/s) and presence of EA on or before day 3. There are 2 univariate GBTM trajectories of EA (%DCI in group 1=32.1, group 2=70.4) significantly associated with DCI, but MCA velocity trajectories are not significant. Logistic regression and GBTM models using both TCD and EEG monitoring improve upon models using either modality alone. Hunt-Hess score at admission and aneurysm treatment as covariates further improved model performance. The best models used both TCD and EEG monitoring modalities and clinical variables as predictors (logistic regression: Se=90%, Sp=70%; GBTM: Se=89%, Sp=67%). Conclusions: EEG and TCD biomarkers combined provide the best prediction of DCI, compared to either alone. Models that considered the timing of EA and high MCA velocities plus clinical variables improved model performance.

Increased level of LIGHT/TNFSF14 is associated with survival in aneurysmal subarachnoid hemorrhage.

Multiple cytokines have been implicated in aneurysmal subarachnoid hemorrhage (aSAH), but tumor necrosis factor superfamily 14 (LIGHT/TNFSF14) and oncostatin-M (OSM) have not been previously explored. The primary objective of this study was to examine the relationship between TNFSF14 and OSM levels and survival. Our secondary goal was to investigate a potential association between these markers and the incidence of delayed cerebral ischemia (DCI). We consecutively recruited 60 patients with a clinical diagnosis of aSAH. LIGHT/TNFSF14 and OSM serum concentrations were determined by ELISA. The primary endpoint was survival at Day 30, while development of DCI was assessed as secondary outcome. Patients had significantly higher levels of both markers than the control group (median of LIGHT: 18.1pg/ml vs. 7pg/ml; p=0.01; median of OSM: 10.3pg/ml vs. 2.8pg/ml, p<0.001). Significantly lower serum level of LIGHT/TNFSF14 was found in nonsurviving patients (n=9) compared with survivors (n=51; p=0.011). Based on ROC analysis, serum LIGHT/TNFSF14 with a cutoff value of >7.95pg/ml predicted 30-day survival with a sensitivity of 71% and specificity of 78% (Area: 0.763; 95% CI: 0.604-0.921, p=0.013). In addition, it was also a predictor of DCI with a sensitivity of 72.7% and a specificity of 62.5% (AUC: 0.702; 95% CI: 0.555-0.849, p=0.018). Based on binary logistic regression analysis, LIGHT/TNFSF14 was found to be independently associated with 30-day mortality, but not with DCI. In this cohort, a higher serum level of LIGHT/TNFSF14 was associated with increased survival of patients with aSAH.

Double hemispheric Microdialysis study in poor-grade SAH patients

Delayed cerebral ischemia (DCI) is a dreadful complication present in 30% of subarachnoid hemorrhage (SAH) patients. DCI prediction and prevention are burdensome in poor grade SAH patients (WFNS 4–5). Therefore, defining an optimal neuromonitoring strategy might be cumbersome. Cerebral microdialysis (CMD) offers near-real-time regional metabolic data of the surrounding brain. However, unilateral neuromonitoring strategies obviate the diffuse repercussions of SAH. To assess the utility, indications and therapeutic implications of bilateral CMD in poor grade SAH patients. Poor grade SAH patients eligible for multimodal neuromonitoring were prospectively collected. Aneurysm location and blood volume were assessed on initial Angio-CT scans. CMD probes were bilaterally implanted and maintained, at least, for 48 hours (h). Ischemic events were defined as a Lactate/Pyruvate ratio >40 and Glucose concentration <0.7 mmol/L. 16 patients were monitored for 1725 h, observing ischemic events during 260 h (15.1%). Simultaneous bilateral ischemic events were rare (5 h, 1.9%). The established threshold of ≥7 ischemic events displayed a specificity and sensitivity for DCI of 96.2% and 83.3%, respectively. Bilateral CMD is a safe and useful strategy to evaluate areas at risk of suffering DCI in SAH patients. Metabolic crises occur bilaterally but rarely simultaneously. Hence, unilateral neuromonitoring strategies underestimate the risk of infarction and the possibility to offset its consequences.