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

Stroke ◽  
2021 ◽  
Vol 52 (Suppl_1) ◽  
Author(s):  
Hsin Yi Chen ◽  
Jonathan Elmer ◽  
Manohar Ghanta ◽  
Junior Valdery-Moura ◽  
Sahar F Zahar ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Subarachnoid Hemorrhage ◽  
Cerebral Ischemia ◽  
Delayed Cerebral Ischemia ◽  
Model Performance ◽  
Eeg Monitoring ◽  
Clinical Variables ◽  
Logistic Regressions ◽  
Diagnostic Modalities ◽  
Improved Model

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.

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

Neurology ◽  
2021 ◽  
pp. 10.1212/WNL.0000000000013126
Author(s):  
Hsin Yi Chen ◽  
Jonathan Elmer ◽  
Sahar F. Zafar ◽  
Manohar Ghanta ◽  
Valdery Moura Junior ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Subarachnoid Hemorrhage ◽  
Cerebral Ischemia ◽  
Transcranial Doppler ◽  
Delayed Cerebral Ischemia ◽  
Eeg Monitoring ◽  
Support Tool ◽  
Epileptiform Discharges ◽  
Eeg Data ◽  
Continuous Eeg

Background and Objectives:Delayed cerebral ischemia (DCI) is the leading complication of subarachnoid hemorrhage (SAH). Because DCI was traditionally thought to be caused by large vessel vasospasm, transcranial Doppler ultrasounds (TCDs) have been the standard of care. Continuous EEG has emerged as a promising complementary monitoring modality and predicts increased DCI risk. Our objective was to determine whether combining EEG and TCD data improves prediction of DCI after SAH. We hypothesize that integrating these diagnostic modalities improves DCI prediction.Methods:We retrospectively assessed patients with moderate-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 (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 significant covariates of EA 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 EA associated with DCI risk.Results:We assessed 107 patients, and DCI developed in 56 (51.9%). Univariate predictors of DCI are presence of high-MCA velocity (PSV≥200cm/s, Se=27%, Sp=89%) and EA (Se=66%, Sp=62%) both on or before day 3. Two univariate GBTM trajectories of EA predicted DCI (Se=64%, Sp=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: Se=90%, Sp=70%; GBTM: Se=89%, Sp=67%).Discussion:EEG and TCD biomarkers combined provide the best prediction of DCI. The conjunction of clinical variables with the timing of EA and high-MCA velocities improved model performance. These results suggest that TCD and cEEG are promising complementary monitoring modalities for DCI prediction. Our model has potential to serve as a decision support tool in SAH management.Classification of Evidence:This study provides Class II evidence that combined TCD and EEG monitoring can identify delayed cerebral ischemia after subarachnoid hemorrhage.

scholarly journals Ultra-early angiographic vasospasm associated with delayed cerebral ischemia and infarction following aneurysmal subarachnoid hemorrhage

2017 ◽  
Vol 126 (5) ◽  
pp. 1545-1551 ◽  
Author(s):  
Fawaz Al-Mufti ◽  
David Roh ◽  
Shouri Lahiri ◽  
Emma Meyers ◽  
Jens Witsch ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Subarachnoid Hemorrhage ◽  
Cerebral Ischemia ◽  
Cerebral Infarction ◽  
Functional Outcome ◽  
Aneurysmal Subarachnoid Hemorrhage ◽  
Delayed Cerebral Ischemia ◽  
Angiographic Vasospasm ◽  
Increased Risk ◽  
Address Data

OBJECTIVEThe clinical significance of cerebral ultra-early angiographic vasospasm (UEAV), defined as cerebral arterial narrowing within the first 48 hours of aneurysmal subarachnoid hemorrhage (aSAH), remains poorly characterized. The authors sought to determine its frequency, predictors, and impact on functional outcome.METHODSThe authors prospectively studied UEAV in a cohort of 1286 consecutively admitted patients with aSAH between August 1996 and June 2013. Admission clinical, radiographic, and acute clinical course information was documented during patient hospitalization. Functional outcome was assessed at 3 months using the modified Rankin Scale. Logistic regression and Cox proportional hazards models were generated to assess predictors of UEAV and its relationship to delayed cerebral ischemia (DCI) and outcome. Multiple imputation methods were used to address data lost to follow-up.RESULTSThe cohort incidence rate of UEAV was 4.6%. Multivariable logistic regression analysis revealed that younger age, sentinel bleed, and poor admission clinical grade were significantly associated with UEAV. Patients with UEAV had a 2-fold increased risk of DCI (odds ratio [OR] 2.3, 95% confidence interval [CI] 1.4–3.9, p = 0.002) and cerebral infarction (OR 2.0, 95% CI 1.0–3.9, p = 0.04), after adjusting for known predictors. Excluding patients who experienced sentinel bleeding did not change this effect. Patients with UEAV also had a significantly higher hazard for DCI in a multivariable model. UEAV was not found to be significantly associated with poor functional outcome (OR 0.8, 95% CI 0.4–1.6, p = 0.5).CONCLUSIONSUEAV may be less frequent than has been reported previously. Patients who exhibit UEAV are at higher risk for refractory DCI that results in cerebral infarction. These patients may benefit from earlier monitoring for signs of DCI and more aggressive treatment. Further study is needed to determine the long-term functional significance of UEAV.

scholarly journals Hyperoxemia during the hyperacute phase of aneurysmal subarachnoid hemorrhage is associated with delayed cerebral ischemia and poor outcome: a retrospective observational study

2019 ◽  
pp. 1-8 ◽  
Author(s):  
Shinya Fukuda ◽  
Yasutaka Koga ◽  
Motoki Fujita ◽  
Eiichi Suehiro ◽  
Kotaro Kaneda ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Subarachnoid Hemorrhage ◽  
Cerebral Ischemia ◽  
Aneurysmal Subarachnoid Hemorrhage ◽  
Delayed Cerebral Ischemia ◽  
Favorable Outcome ◽  
Regression Analyses ◽  
Poor Outcome ◽  
Poor Outcome Group ◽  
Outcome Group

OBJECTIVEThe harmful effects of hyperoxemia have been reported in critically ill patients with various disorders, including those with brain injuries. However, the effect of hyperoxemia on aneurysmal subarachnoid hemorrhage (aSAH) patients is unclear. In this study the authors aimed to determine whether hyperoxemia during the hyperacute or acute phase in patients with aSAH is associated with delayed cerebral ischemia (DCI) and poor neurological outcome.METHODSIn this single-center retrospective study, data from patients with aSAH treated between January 2011 and June 2017 were reviewed. The patients were classified into groups according to whether they experienced DCI (DCI group and non-DCI group) and whether they had a poor outcome at discharge (poor outcome group and favorable outcome group). The background characteristics and time-weighted average (TWA) PaO2 during the first 24 hours after arrival at the treatment facility (TWA24h-PaO2) and between the first 24 hours after arrival and day 6 (TWA6d-PaO2), the hyperacute and acute phases, respectively, were compared between the groups. Factors related to DCI and poor outcome were evaluated with logistic regression analyses.RESULTSOf 197 patients with aSAH, 42 patients experienced DCI and 82 patients had a poor outcome at discharge. TWA24h-PaO2 was significantly higher in the DCI group than in the non-DCI group (186 [141–213] vs 161 [138–192] mm Hg, p = 0.029) and in the poor outcome group than in the favorable outcome group (176 [154–205] vs 156 [136–188] mm Hg, p = 0.004). TWA6d-PaO2 did not differ significantly between the groups. Logistic regression analyses revealed that higher TWA24h-PaO2 was an independent risk factor for DCI (OR 1.09, 95% CI 1.01–1.17, p = 0.037) and poor outcome (OR 1.17, 95% CI 1.06–1.29, p = 0.002).CONCLUSIONSHyperoxemia during the first 24 hours was associated with DCI and a poor outcome in patients with aSAH. Excessive oxygen therapy might have an adverse effect in the hyperacute phase of aSAH.

scholarly journals Serum glucose as an important predictor of delayed cerebral ischemia in angiogram-negative subarachnoid hemorrhage

2021 ◽  
Author(s):  
Zeyu Zhang ◽  
Anke Zhang ◽  
Xiaoyu Wang ◽  
Yuanjian Fang ◽  
Yibo Liu ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Subarachnoid Hemorrhage ◽  
Cerebral Ischemia ◽  
Logistic Regression Analysis ◽  
Delayed Cerebral Ischemia ◽  
Multivariate Logistic Regression Analysis ◽  
Serum Glucose ◽  
World Federation ◽  
Symptomatic Vasospasm

Abstract Background Despite benign overall course, angiogram-negative subarachnoid hemorrhage (AN-SAH) still companies with risk of delayed cerebral ischemia (DCI). Serum glucose was previously found to be related to DCI occurrence in aneurysmal subarachnoid hemorrhage (aSAH), but this has not been confirmed in AN-SAH. The aim of this study was to clarify the significance of serum glucose in DCI prediction in AN-SAH patients. Methods We included patients with AN-SAH admitted to our hospital between January 2013 and December 2018. According to different bleeding patterns, patients were divided into perimesencephalic AN-SAH (PAN-SAH) and non-perimesencephalic AN-SAH (NPAH-SAH) patients. DCI was defined as symptomatic vasospasm or/and delayed cerebral infarction. A statistical analysis of the clinical, radiological, and laboratory risk factors of DCI was conducted. Logistic regression analysis was performed to identify the independent predictors of DCI. Results A total of 244 AN-SAH patients (mean age 55.7 years, 55.7% men) were included with 164 (67.2%) PAN-SAH patients and 80 (32.8%) NPAN-SAH patients. There were significant correlations between high DCI incidence and high serum glucose levels in the first five days after admission in both PAN-SAH patients and NPAN-SAH patients (p < 0.05). High admission serum glucose was significantly related to higher World Federation of Neurosurgeons Scale (WFNS) (p < 0.05). Multivariate logistic regression analysis showed that admission serum glucose (p = 0.001, OR 1.705, 95% CI 1.232–2.360) and WFNS (p = 0.008, OR 2.889, 95% CI 1.322–6.311) were both significant and independent predictors for DCI occurrence in PAN-SAH patients. Admission serum glucose (p = 0.016, OR 2.307, 95% CI 1.167–4.562), standard deviation (SD) of the serum glucose in the first three days after admission (p = 0.049, OR 5.684, 95% CI 1.006–32.114) and modified Fisher scale (mFS) (p = 0.033, OR 1.859, 95% CI 1.051–3.288) were significant and independent predictors for DCI occurrence in NPAN-SAH patients. Conclusions Serum glucose is an early biomarker to predict DCI risk in both PAN-SAH and NPAN-SAH patients, which has an important value in guiding intensive care in AN-SAH patients.

Machine learning improves prediction of delayed cerebral ischemia in patients with subarachnoid hemorrhage

2018 ◽  
Vol 11 (5) ◽  
pp. 497-502 ◽  
Author(s):  
Lucas Alexandre Ramos ◽  
Wessel E van der Steen ◽  
Renan Sales Barros ◽  
Charles B L M Majoie ◽  
Rene van den Berg ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Subarachnoid Hemorrhage ◽  
Cerebral Ischemia ◽  
Regression Models ◽  
Aneurysmal Subarachnoid Hemorrhage ◽  
Delayed Cerebral Ischemia ◽  
Image Data ◽  
Image Features ◽  
Logistic Regression Models

Background and purposeDelayed cerebral ischemia (DCI) is a severe complication in patients with aneurysmal subarachnoid hemorrhage. Several associated predictors have been previously identified. However, their predictive value is generally low. We hypothesize that Machine Learning (ML) algorithms for the prediction of DCI using a combination of clinical and image data lead to higher predictive accuracy than previously applied logistic regressions.Materials and methodsClinical and baseline CT image data from 317 patients with aneurysmal subarachnoid hemorrhage were included. Three types of analysis were performed to predict DCI. First, the prognostic value of known predictors was assessed with logistic regression models. Second, ML models were created using all clinical variables. Third, image features were extracted from the CT images using an auto-encoder and combined with clinical data to create ML models. Accuracy was evaluated based on the area under the curve (AUC), sensitivity and specificity with 95% CI.ResultsThe best AUC of the logistic regression models for known predictors was 0.63 (95% CI 0.62 to 0.63). For the ML algorithms with clinical data there was a small but statistically significant improvement in the AUC to 0.68 (95% CI 0.65 to 0.69). Notably, aneurysm width and height were included in many of the ML models. The AUC was highest for ML models that also included image features: 0.74 (95% CI 0.72 to 0.75).ConclusionML algorithms significantly improve the prediction of DCI in patients with aneurysmal subarachnoid hemorrhage, particularly when image features are also included. Our experiments suggest that aneurysm characteristics are also associated with the development of DCI.

scholarly journals Conditioning Effect of Inhalational Anesthetics on Delayed Cerebral Ischemia After Aneurysmal Subarachnoid Hemorrhage

Neurosurgery ◽  
2020 ◽  
Author(s):  
Umeshkumar Athiraman ◽  
Rajat Dhar ◽  
Keshav Jayaraman ◽  
Menelaos Karanikolas ◽  
Daniel Helsten ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Subarachnoid Hemorrhage ◽  
Cerebral Ischemia ◽  
Protective Effect ◽  
Neurological Outcome ◽  
Aneurysmal Subarachnoid Hemorrhage ◽  
Delayed Cerebral Ischemia ◽  
Angiographic Vasospasm ◽  
Inhalational Anesthetic

Abstract BACKGROUND Delayed cerebral ischemia (DCI) after aneurysmal subarachnoid hemorrhage (SAH) has been identified as an independent predictor of poor outcome in numerous studies. OBJECTIVE To investigate the potential protective role of inhalational anesthetics against angiographic vasospasm, DCI, and neurologic outcome in SAH patients. METHODS After Institutional Review Board approval, data were collected retrospectively for SAH patients who received general anesthesia for aneurysm repair between January 1st, 2010 and May 31st, 2018. Primary outcomes were angiographic vasospasm, DCI, and neurologic outcome as measured by modified Rankin scale at hospital discharge. Univariate and logistic regression analysis were performed to identify independent predictors of these outcomes. RESULTS The cohort included 390 SAH patients with an average age of 56 ± 15 (mean ± SD). Multivariate logistic regression analysis identified inhalational anesthetic only technique, Hunt-Hess grade, age, anterior circulation aneurysm and average intraoperative mean blood pressure as independent predictors of angiographic vasospasm. Inhalational anesthetic only technique and modified Fishers grade were identified as independent predictors of DCI. No impact on neurological outcome at time of discharge was noted. CONCLUSION Our data provide additional evidence that inhalational anesthetic conditioning in SAH patients affords protection against angiographic vasospasm and new evidence that it exerts a protective effect against DCI. When coupled with similar results from preclinical studies, our data suggest further investigation into the impact of inhalational anesthetic conditioning on SAH patients, including elucidating the most effective dosing regimen, defining the therapeutic window, determining whether a similar protective effect against early brain injury, and on long-term neurological outcome exists.

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