Association of Automatically Quantified Total Blood Volume after Aneurysmal Subarachnoid Hemorrhage with Delayed Cerebral Ischemia

Contribution of an Automatic Algorithm for Quantifying the Volume of Aneurysmal Subarachnoid Hemorrhage to the Evaluation of the Risk of Occurrence of Delayed Cerebral Ischemia: A Cohort Study

BACKGROUND

This study focuses on aneurysmal subarachnoid hemorrhage (aSAH) with a high risk of delayed cerebral ischemia (DCI) and acute hydrocephalus (AH). The aim was to compare the performance of an automatic algorithm for quantifying the volume of intracranial blood with the reference radiological scales to predict DCI, AH, and neurological outcome.

METHODS

This was a single-center retrospective observational study of a cohort of patients with aSAH. We developed an automated blood detection algorithm based on the specific density of the blood clot. The blood clot was segmented on the first brain scan (total, supratentorial, cisternal, intraventricular). The predictive value of our model was compared, using the area under the receiver operating characteristic curve (ROCAUC), to eight radiological scales: Fisher, modified Fisher, Claassen, Barrow Neurological Institute, Hijdra, Graeb, LeRoux scales, and intraventricular hemorrhage score.

RESULTS

We analyzed the scans of 145 patients with aSAH. In our cohort, 51 patients (43%) had DCI and 70 patients (54%) had AH. At 3 months, 22% of patients had died and 19% had poor outcome (Glasgow Outcome Scale extended 2-4). Cisternal blood volume was significantly correlated with cisternal Hijdra scale (R2 = 0.79; P < 0.001). The ROCAUC of cisternal blood volume was comparable to the ROCAUC of the Hijdra scale in predicting the occurrence of DCI (ROCAUC = 0.83 [95% confidence interval {CI} 0.75-0.89] vs. 0.86 [95% CI 0.79-0.9]; P = 0.23). The ROCAUC of intraventricular blood volume was not significantly different from the intraventricular hemorrhage score in predicting the occurrence of AH (ROCAUC = 0.78 [95% CI 0.70-0.84] vs. 0.79 [95% CI 0.72-0.85]; P = 0.28). The ROCAUC and supratentorial blood volumes were not significantly different from the Simplified Acute Physiology Score II in predicting the occurrence of poor neurological outcome at 3 months (ROCAUC = 0.75 [95% CI 0.67-0.82] vs. 0.81 [95% CI 0.74-0.87]; P = 0.073).

CONCLUSIONS

With no manual intervention, our algorithm performed as well as the best radiological scores in predicting the occurrence of DCI, AH, and neurological outcome.

Semiautomated Hemorrhage Volume Quantification in Aneurysmal Subarachnoid Hemorrhage

BACKGROUND

The volume of hemorrhage is a crucial factor in predicting outcomes following aneurysmal subarachnoid hemorrhage (aSAH). Although grading scales such as the Fisher score are widely used, they can lead to inaccuracies in quantifying the total blood volume because of their reliance on visual assessment. We analyzed a large cohort of patients with aSAH with a semiautomated software for the precise quantification of hemorrhage volume. The primary aim is to identify clear thresholds that correlate with the likelihood of complications after aSAH, thereby enhancing the predictive accuracy and improving patient management strategies.

METHODS

A semiautomated algorithm was developed to analyze noncontrast computed tomography scans of patients with aSAH. The algorithm categorized tissues into blood, gray matter, white matter, and cerebrospinal fluid, isolating the blood for volume quantification. Receiver operating curve analysis was done to establish thresholds for vasospasm, acute hydrocephalus, shunt-dependent hydrocephalus (SDHC), and death within 7 days.

RESULTS

A total of 500 patients with aSAH and their respective aneurysms were analyzed. Hemorrhage volume was significantly higher in patients with vasospasm (21.7 [10.9-41.4] vs. 10.7 [4.2-26.9], p < 0.001), acute hydrocephalus (22.7 [9.2-41.8] vs. 5.1 [2.1-13.5], p < 0.001), SDHC (23.8 [11.3-40.7] vs. 11.7 [4.1-28.2], p < 0.001), and those who died before 7 days (52.8 [34.6-90.6] mL vs. 14.8 [5.0-32.4] mL, p < 0.001) compared with their counterparts. Notably, specific hemorrhage thresholds were identified for each complication: 15.16 mL for vasospasm (65% sensitivity and 60% specificity), 9.95 mL for acute hydrocephalus (74% sensitivity and 69% specificity), 16.76 mL for SDHC (63% sensitivity and 60% specificity), and 33.84 mL for death within 7 days (79% sensitivity and 77% specificity).

CONCLUSIONS

Semiautomated blood volume quantification tools could aid in stratifying complication risk after aSAH. Established thresholds for hemorrhage volume related to complications could be used in clinical practice to aid in management decisions.

Integrating Clinical Data and Radiomics and Deep Learning Features for End-to-End Delayed Cerebral Ischemia Prediction on Noncontrast CT

BACKGROUND AND PURPOSE

Delayed cerebral ischemia is hard to diagnose early due to gradual, symptomless development. This study aimed to develop an automated model for predicting delayed cerebral ischemia following aneurysmal SAH on NCCT.

MATERIALS AND METHODS

This retrospective study included 400 patients with aneurysmal SAH (156 with delayed cerebral ischemia) who underwent NCCT. The study used ATT-Deeplabv3+ for automatically segmenting hemorrhagic regions using semisupervised learning. Principal component analysis was used for reducing the dimensionality of deep learning features extracted from the average pooling layer of ATT-DeepLabv3+. The classification model integrated clinical data, radiomics, and deep learning features to predict delayed cerebral ischemia. Feature selection involved Pearson correlation coefficients, least absolute shrinkage, and selection operator regression. We developed models based on clinical features, clinical-radiomics, and a combination of clinical, radiomics, and deep learning. The study selected logistic regression, Naive Bayes, Adaptive Boosting (AdaBoost), and multilayer perceptron as classifiers. The performance of segmentation and classification models was evaluated on their testing sets using the Dice similarity coefficient for segmentation, and the area under the receiver operating characteristic curve (AUC) and calibration curves for classification.

RESULTS

The segmentation process achieved a Dice similarity coefficient of 0.91 and the average time of 0.037 s/image. Seventeen features were selected to calculate the radiomics score. The clinical-radiomics-deep learning model with multilayer perceptron achieved the highest AUC of 0.84 (95% CI, 0.72-0.97), which outperformed the clinical-radiomics model (P = .002) and the clinical features model (P = .001) with multilayer perceptron. The performance of clinical-radiomics-deep learning model using AdaBoost was significantly superior to its clinical-radiomics model (P = .027). The performance of the clinical-radiomics-deep learning model and the clinical-radiomics model with logistic regression notably exceeded that of the model based solely on clinical features (P = .028; P = .046). The AUC of the clinical-radiomics-deep learning model with multilayer perceptron (P < .001) and the clinical-radiomics model with logistic regression (P = .046) were significantly higher than the clinical model with logistic regression. Of all models, the clinical-radiomics-deep learning model with multilayer perceptron showed best calibration.

CONCLUSIONS

The proposed 2-stage end-to-end model not only achieves rapid and accurate segmentation but also demonstrates superior diagnostic performance with high AUC values and good calibration in the clinical-radiomics-deep learning model, suggesting its potential to enhance delayed cerebral ischemia detection and treatment strategies.

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

BACKGROUND

The 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.

METHODS

The 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).

RESULTS

VCSF 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).

CONCLUSION

It 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.

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

OBJECTIVE

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.

METHODS

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.

RESULTS

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%.

CONCLUSIONS

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.

All Three Supersystems-Nervous, Vascular, and Immune-Contribute to the Cortical Infarcts After Subarachnoid Hemorrhage

The recently published DISCHARGE-1 trial supports the observations of earlier autopsy and neuroimaging studies that almost 70% of all focal brain damage after aneurysmal subarachnoid hemorrhage are anemic infarcts of the cortex, often also affecting the white matter immediately below. The infarcts are not limited by the usual vascular territories. About two-fifths of the ischemic damage occurs within ~ 48 h; the remaining three-fifths are delayed (within ~ 3 weeks). Using neuromonitoring technology in combination with longitudinal neuroimaging, the entire sequence of both early and delayed cortical infarct development after subarachnoid hemorrhage has recently been recorded in patients. Characteristically, cortical infarcts are caused by acute severe vasospastic events, so-called spreading ischemia, triggered by spontaneously occurring spreading depolarization. In locations where a spreading depolarization passes through, cerebral blood flow can drastically drop within a few seconds and remain suppressed for minutes or even hours, often followed by high-amplitude, sustained hyperemia. In spreading depolarization, neurons lead the event, and the other cells of the neurovascular unit (endothelium, vascular smooth muscle, pericytes, astrocytes, microglia, oligodendrocytes) follow. However, dysregulation in cells of all three supersystems-nervous, vascular, and immune-is very likely involved in the dysfunction of the neurovascular unit underlying spreading ischemia. It is assumed that subarachnoid blood, which lies directly on the cortex and enters the parenchyma via glymphatic channels, triggers these dysregulations. This review discusses the neuroglial, neurovascular, and neuroimmunological dysregulations in the context of spreading depolarization and spreading ischemia as critical elements in the pathogenesis of cortical infarcts after subarachnoid hemorrhage.

Proposed imaging assessment score for aneurysmal subarachnoid hemorrhage correlated with prognosis: Shinshu Aneurysmal subarachnoid hemorrhage score

BACKGROUND

Aneurysmal subarachnoid hemorrhage (aSAH) imaging has been shown to correlate with prognosis. However, no numerical index of bleeding severity has been established. This study aimed to propose a new simple scoring system for computed tomography imaging of aSAH and to confirm its effectiveness in retrospective and prospective studies.

METHODS

We devised an image evaluation system as an objective index. This system was established by scoring six items, with a maximum total of 19 points. Using this score, named the Shinshu Aneurysmal Subarachnoid Hemorrhage Score (S-score), we performed a retrospective study of 210 patients with aSAH at a single institution to confirm its efficacy. Age and World Federation of Neurosurgical Societies grades were adopted as other verification items, and the modified Rankin Scale was used for prognostic evaluation. A multicenter prospective study was then conducted to examine the function of the score by examining 214 patients with aSAH.

RESULTS

In the retrospective study, the threshold of the S-score between good and poor prognoses was 9/19 points. The area under the curve by receiver operating characteristic analysis of the S-score was 0.819, suggesting efficacy, with an odds ratio (OR) of 1.291 (1.077-1.547). In the prospective study, the judgment capability of the S-score was evaluated with a sensitivity of 0.674, specificity of 0.881, positive predictive value of 0.789, negative predictive value of 0.804, false-positive ratio of 0.119, false-negative ratio of 0.325, positive likelihood ratio of 6.072, and negative likelihood ratio of 1.369. S-score showed a significant difference in prognosis. The OR was 1.183 (1.009-1.388).

CONCLUSIONS

The scoring system could contribute to patient prognosis assessment. S-score and its prognostic formulas may serve as an objective source of information in the development of clinical medicine.

Deep learning-assisted identification and quantification of aneurysmal subarachnoid hemorrhage in non-contrast CT scans: Development and external validation of Hybrid 2D/3D UNet

Accurate stroke assessment and consequent favorable clinical outcomes rely on the early identification and quantification of aneurysmal subarachnoid hemorrhage (aSAH) in non-contrast computed tomography (NCCT) images. However, hemorrhagic lesions can be complex and difficult to distinguish manually. To solve these problems, here we propose a novel Hybrid 2D/3D UNet deep-learning framework for automatic aSAH identification and quantification in NCCT images. We evaluated 1824 consecutive patients admitted with aSAH to four hospitals in China between June 2018 and May 2022. Accuracy and precision, Dice scores and intersection over union (IoU), and interclass correlation coefficients (ICC) were calculated to assess model performance, segmentation performance, and correlations between automatic and manual segmentation, respectively. A total of 1355 patients with aSAH were enrolled: 931, 101, 179, and 144 in four datasets, of whom 326 were scanned with Siemens, 640 with Philips, and 389 with GE Medical Systems scanners. Our proposed deep-learning method accurately identified (accuracies 0.993-0.999) and segmented (Dice scores 0.550-0.897) hemorrhage in both the internal and external datasets, even combinations of hemorrhage subtypes. We further developed a convenient AI-assisted platform based on our algorithm to assist clinical workflows, whose performance was comparable to manual measurements by experienced neurosurgeons (ICCs 0.815-0.957) but with greater efficiency and reduced cost. While this tool has not yet been prospectively tested in clinical practice, our innovative hybrid network algorithm and platform can accurately identify and quantify aSAH, paving the way for fast and cheap NCCT interpretation and a reliable AI-based approach to expedite clinical decision-making for aSAH patients.

Predicting vasospasm risk using first presentation aneurysmal subarachnoid hemorrhage volume: A semi-automated CT image segmentation analysis using ITK-SNAP

PURPOSE

Cerebral vasospasm following aneurysmal subarachnoid hemorrhage (aSAH) is a significant complication associated with poor neurological outcomes. We present a novel, semi-automated pipeline, implemented in the open-source medical imaging analysis software ITK-SNAP, to segment subarachnoid blood volume from initial CT head (CTH) scans and use this to predict future radiological vasospasm.

METHODS

42 patients were admitted between February 2020 and December 2021 to our tertiary neurosciences center, and whose initial referral CTH scan was used for this retrospective cohort study. Blood load was segmented using a semi-automated random forest classifier and active contour evolution implemented in ITK-SNAP. Clinical data were extracted from electronic healthcare records in order to fit models aimed at predicting radiological vasospasm risk.

RESULTS

Semi-automated segmentations demonstrated excellent agreement with manual, expert-derived volumes (mean Dice coefficient = 0.92). Total normalized blood volume, extracted from CTH images at first presentation, was significantly associated with greater odds of later radiological vasospasm, increasing by approximately 7% for each additional cm3 of blood (OR = 1.069, 95% CI: 1.021-1.120; p < .005). Greater blood volume was also significantly associated with vasospasm of a higher Lindegaard ratio, of longer duration, and a greater number of discrete episodes. Total blood volume predicted radiological vasospasm with a greater accuracy as compared to the modified Fisher scale (AUC = 0.86 vs 0.70), and was of independent predictive value.

CONCLUSION

Semi-automated methods provide a plausible pipeline for the segmentation of blood from CT head images in aSAH, and total blood volume is a robust, extendable predictor of radiological vasospasm, outperforming the modified Fisher scale. Greater subarachnoid blood volume significantly increases the odds of subsequent vasospasm, its time course and its severity.

Whole-brain CT Perfusion at Admission and During Delayed Time-window Detects the Delayed Cerebral Ischemia in Patients with Aneurysmal Subarachnoid Hemorrhage

OBJECTIVE

To evaluate the utility of computed tomography perfusion (CTP) both at admission and during delayed cerebral ischemia time-window (DCITW) in the detection of delayed cerebral ischemia (DCI) and the change in CTP parameters from admission to DCITW following aneurysmal subarachnoid hemorrhage.

METHODS

Eighty patients underwent CTP at admission and during DCITW. The mean and extreme values of all CTP parameters at admission and during DCITW were compared between the DCI group and non-DCI group, and comparisons were also made between admission and DCITW within each group. The qualitative color-coded perfusion maps were recorded. Finally, the relationship between CTP parameters and DCI was assessed by receiver operating characteristic (ROC) analyses.

RESULTS

With the exception of cerebral blood volume (P=0.295, admission; P=0.682, DCITW), there were significant differences in the mean quantitative CTP parameters between DCI and non-DCI patients both at admission and during DCITW. In the DCI group, the extreme parameters were significantly different between admission and DCITW. The DCI group also showed a deteriorative trend in the qualitative color-coded perfusion maps. For the detection of DCI, mean transit time to the center of the impulse response function (Tmax) at admission and mean time to start (TTS) during DCITW had the largest area under curve (AUC), 0.698 and 0.789, respectively.

CONCLUSION

Whole-brain CTP can predict the occurrence of DCI at admission and diagnose DCI during DCITW. The extreme quantitative parameters and qualitative color-coded perfusion maps can better reflect the perfusion changes of patients with DCI from admission to DCITW.

Automated Quantification of Compartmental Blood Volumes Enables Prediction of Delayed Cerebral Ischemia and Outcomes After Aneurysmal Subarachnoid Hemorrhage

OBJECTIVE

The role of hemorrhage volume in risk of vasospasm, delayed cerebral ischemia (DCI), and poor outcomes after aneurysmal subarachnoid hemorrhage (SAH) is well established. However, the relative contribution of blood within individual compartments is unclear. We present an automated technique for measuring not only total but also volumes of blood in each major compartment after SAH.

METHODS

We trained convolutional neural networks to identify compartmental blood (cisterns, sulci, and ventricles) from baseline computed tomography scans of patients with SAH. We compared automated blood volumes against traditional markers of bleeding (modified Fisher score [mFS], Hijdra sum score [HSS]) in 190 SAH patients for prediction of vasospasm, DCI, and functional status (modified Rankin Scale) at hospital discharge.

RESULTS

Combined cisternal and sulcal volume was better correlated with mFS and HSS than cisternal volume alone (ρ = 0.63 vs. 0.58 and 0.75 vs. 0.70, P < 0.001). Only blood volume in combined cisternal plus sulcal compartments was independently associated with DCI (OR 1.023 per mL, 95% CI 1.002-1.048), after adjusting for clinical factors while ventricular blood volume was not. Total and specifically sulcal blood volume was strongly associated with poor outcome (OR 1.03 per mL, 1.01-1.06, P = 0.006 and OR 1.04, 1.00-1.08 for sulcal) as was HSS (OR 1.06 per point, 1.00-1.12, P = 0.04), while mFS was not (P = 0.24).

CONCLUSIONS

An automated imaging algorithm can measure the volume of bleeding after SAH within individual compartments, demonstrating cisternal plus sulcal (and not ventricular) blood contributes to risk of DCI/vasospasm. Automated blood volume was independently associated with outcome, while qualitative grading was not.

The influence of aneurysm morphology on the volume of hemorrhage after rupture

OBJECTIVE

Factors determining the risk of rupture of intracranial aneurysms have been extensively studied; however, little attention is paid to variables influencing the volume of bleeding after rupture. In this study the authors aimed to evaluate the impact of aneurysm morphological variables on the amount of hemorrhage.

METHODS

This was a retrospective cohort analysis of a prospectively collected data set of 116 patients presenting at a single center with subarachnoid hemorrhage due to aneurysmal rupture. A volumetric assessment of the total hemorrhage volume was performed from the initial noncontrast CT. Aneurysms were segmented and reproduced from the initial CT angiography study, and morphology indexes were calculated with a computer-assisted approach. Clinical and demographic characteristics of the patients were included in the study. Factors influencing the volume of hemorrhage were explored with univariate correlations, multiple linear regression analysis, and graphical probabilistic modeling.

RESULTS

The univariate analysis demonstrated that several of the morphological variables but only the patient's age from the clinical-demographic variables correlated (p < 0.05) with the volume of bleeding. Nine morphological variables correlated positively (absolute height, perpendicular height, maximum width, sac surface area, sac volume, size ratio, bottleneck factor, neck-to-vessel ratio, and width-to-vessel ratio) and two correlated negatively (parent vessel average diameter and the aneurysm angle). After multivariate analysis, only the aneurysm size ratio (p < 0.001) and the patient's age (p = 0.023) remained statistically significant. The graphical probabilistic model confirmed the size ratio and the patient's age as the variables most related to the total hemorrhage volume.

CONCLUSIONS

A greater aneurysm size ratio and an older patient age are likely to entail a greater volume of bleeding after subarachnoid hemorrhage.

When the Blood Hits Your Brain: The Neurotoxicity of Extravasated Blood

Hemorrhage in the central nervous system (CNS), including intracerebral hemorrhage (ICH), intraventricular hemorrhage (IVH), and aneurysmal subarachnoid hemorrhage (aSAH), remains highly morbid. Trials of medical management for these conditions over recent decades have been largely unsuccessful in improving outcome and reducing mortality. Beyond its role in creating mass effect, the presence of extravasated blood in patients with CNS hemorrhage is generally overlooked. Since trials of surgical intervention to remove CNS hemorrhage have been generally unsuccessful, the potent neurotoxicity of blood is generally viewed as a basic scientific curiosity rather than a clinically meaningful factor. In this review, we evaluate the direct role of blood as a neurotoxin and its subsequent clinical relevance. We first describe the molecular mechanisms of blood neurotoxicity. We then evaluate the clinical literature that directly relates to the evacuation of CNS hemorrhage. We posit that the efficacy of clot removal is a critical factor in outcome following surgical intervention. Future interventions for CNS hemorrhage should be guided by the principle that blood is exquisitely toxic to the brain.

Association of Cerebrospinal Fluid Volume with Cerebral Vasospasm After Aneurysmal Subarachnoid Hemorrhage: A Retrospective Volumetric Analysis

BACKGROUND

In aneurysmal subarachnoid hemorrhage (SAH), clot volume has been shown to correlate with the development of radiographic vasospasm (VS), while the role of cerebrospinal fluid (CSF) volume remains largely elusive in the literature. We evaluated CSF volume as a potential surrogate for VS in addition to SAH volume in this retrospective series.

PATIENTS AND METHODS

From a consecutive cohort of aneurysmal SAH (n= 320), cases were included when angiographic evaluation for VS was performed (n= 125). SAH and CSF volumes were volumetrically quantified using an algorithm-assisted segmentation approach on initial computed tomography after ictus. Association with VS was analyzed using regression analysis. Receiver operating characteristic (ROC) curves were used to evaluate predictive accuracy of volumetric measures for VS and to identify cutoffs for risk stratification.

RESULTS

Among 125 included cases, angiography showed VS in 101 (VS+), while no VS was observed in 24 (VS-) cases. In volumetric analysis, mean SAH volume was significantly larger (26.8 ± 21.1 ml vs. 12.6 ± 12.2 ml, p= 0.001), while mean CSF volume was significantly smaller (63.0 ± 31.2 ml vs. 85.7 ± 62.8, p= 0.03) in VS+ compared to VS- cases, respectively. The absence of correlation for SAH and CSF volumes (Pearson R - 0.05, p= 0.58) indicated independence of both measures of the subarachnoid compartment, which was a prerequisite for CSF to act as a new surrogate for VS not related to SAH. Regression analysis confirmed an increased risk of VS with increasing SAH (OR 1.06, 95% CI 1.02-1.11, p= 0.006), while CSF had a protective effect toward VS (OR 0.99, 95% CI 0.98-0.99, p= 0.02). SAH/CSF ratio was also associated with VS (OR 1.03, 95% CI 1.01-1.05, p= 0.015). ROC curves suggested cutoffs at 120 ml CSF and 20 ml SAH for VS stratification. Combination of variables improved stratification accuracy compared to use of SAH alone.

CONCLUSION

This study provides a proof of concept for CSF correlating with angiographic VS after aneurysmal SAH. Quantification of CSF in conjunction with SAH might enhance risk stratification and exhibit advantages over traditional scores. The association of CSF has to be corroborated for delayed cerebral ischemia to further establish CSF as a surrogate parameter.

Predictive Value of Cerebral Autoregulation Impairment for Delayed Cerebral Ischemia in Aneurysmal Subarachnoid Hemorrhage: A Meta-Analysis

OBJECTIVE

Delayed cerebral ischemia (DCI) happens in about 30% of patients with aneurysmal subarachnoid hemorrhage (SAH) and is related to higher mortality and disability. Some studies have shown cerebral autoregulation impairment can be a predictor of DCI in aneurysmal SAH. We conducted this meta-analysis to evaluate the predictive value of cerebral autoregulation impairment for DCI based on the current literature.

METHODS

A systematic literature search was performed in PubMed and Embase. According to inclusion and exclusion criteria, 2 authors screened the records and extracted data from the included studies. Pooled sensitivity, specificity, and their 95% confidence intervals (CIs) were obtained. To investigate the overall accuracy, a summary receiver operating characteristic (SROC) curve was built and the area under SROC curve was calculated. Deeks' linear regression was used to assess the publication bias. All statistical analyses were performed with Stata 14.0.

RESULTS

A total of 7 studies were finally included in this meta-analysis. The pooled sensitivity and specificity values of impaired cerebral autoregulation for DCI prediction were 0.79 (95% CI, 0.65-0.88) and 0.85 (95% CI, 0.615-0.96). Moreover, the area under the SROC curve of cerebral autoregulation impairment for DCI prediction was 0.87 (95% CI, 0.835-0.89). No obvious publication bias was found in Deeks' linear regression (P = 0.99).

CONCLUSIONS

Cerebral autoregulation impairment can be a helpful predictor of DCI in aneurysmal SAH. Its accuracy for DCI prediction should be verified by more studies in the future.

Radiological scales predicting delayed cerebral ischemia in subarachnoid hemorrhage: systematic review and meta-analysis

PURPOSE

Delayed cerebral ischemia (DCI) is a severe complication of aneurysmal subarachnoid hemorrhage (aSAH). The extent of subarachnoid blood is a strong predictor of DCI and is frequently estimated with the Fisher scale, modified Fisher scale, or Hijdra sum score. It is unclear which scale has the strongest association with clinical DCI. To evaluate this, we performed a systematic review of the literature.

METHODS

We performed a MEDLINE and EMBASE search from 1980 to 20th of June 2017. Radiological grade and occurrence of clinical DCI were extracted along with odds ratios (ORs) for DCI. When possible, pooled ORs with 95% confidence intervals were calculated per grade increase on the radiological scale.

RESULTS

Fifty-three studies were included. The Fisher scale was significantly associated with DCI in 62% of the studies compared to 88-100% for the other scales. In studies using the Fisher scale, Fisher 3 had the strongest association with DCI (pooled OR 3.21 (1.87-5.49)). In studies using the modified Fisher score, DCI occurred most frequently (42%) in modified Fisher 4. No pooled OR could be calculated for the other scales.

CONCLUSION

The Fisher scale, modified Fisher scale, and Hijdra sum score are all associated with clinical DCI. The risk of DCI, however, does not increase with increasing Fisher grade as opposed to the modified Fisher scale. Furthermore, the modified Fisher scale was more commonly significantly associated with DCI than the Fisher scale, which may advocate using the modified Fisher in future SAH-related studies.

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

Background and purpose

Delayed 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 methods

Clinical 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.

Results

The 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).

Conclusion

ML 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.

Prediction of outcome after aneurysmal subarachnoid haemorrhage using data from patient admission

OBJECTIVES

The pathogenesis leading to poor functional outcome after aneurysmal subarachnoid haemorrhage (aSAH) is multifactorial and not fully understood. We evaluated a machine learning approach based on easily determinable clinical and CT perfusion (CTP) features in the course of patient admission to predict the functional outcome 6 months after ictus.

METHODS

Out of 630 consecutive subarachnoid haemorrhage patients (2008-2015), 147 (mean age 54.3, 66.7% women) were retrospectively included (Inclusion: aSAH, admission within 24 h of ictus, CTP within 24 h of admission, documented modified Rankin scale (mRS) grades after 6 months. Exclusion: occlusive therapy before first CTP, previous aSAH, CTP not evaluable). A random forests model with conditional inference trees was optimised and trained on sex, age, World Federation of Neurosurgical Societies (WFNS) and modified Fisher grades, aneurysm in anterior vs. posterior circulation, early external ventricular drainage (EVD), as well as MTT and T_max maximum, mean, standard deviation (SD), range, 75th quartile and interquartile range to predict dichotomised mRS (≤ 2; > 2). Performance was assessed using the balanced accuracy over the training and validation folds using 20 repeats of 10-fold cross-validation.

RESULTS

In the final model, using 200 trees and the synthetic minority oversampling technique, median balanced accuracy was 84.4% (SD 0.7) over the training folds and 70.9% (SD 1.2) over the validation folds. The five most important features were the modified Fisher grade, age, MTT range, WFNS and early EVD.

CONCLUSIONS

A random forests model trained on easily determinable features in the course of patient admission can predict the functional outcome 6 months after aSAH with considerable accuracy.

KEY POINTS

• Features determinable in the course of admission of a patient with aneurysmal subarachnoid haemorrhage (aSAH) can predict the functional outcome 6 months after the occurrence of aSAH. • The top five predictive features were the modified Fisher grade, age, the mean transit time (MTT) range from computed tomography perfusion (CTP), the WFNS grade and the early necessity for an external ventricular drainage (EVD). • The range between the minimum and the maximum MTT may prove to be a valuable biomarker for detrimental functional outcome.

Association of Quantified Location-Specific Blood Volumes with Delayed Cerebral Ischemia after Aneurysmal Subarachnoid Hemorrhage

BACKGROUND AND PURPOSE

Delayed cerebral ischemia is a severe complication of aneurysmal SAH and is associated with a high case morbidity and fatality. The total blood volume and the presence of intraventricular blood on CT after aneurysmal SAH are associated with delayed cerebral ischemia. Whether quantified location-specific (cisternal, intraventricular, parenchymal, and subdural) blood volumes are associated with delayed cerebral ischemia has been infrequently researched. This study aimed to associate quantified location-specific blood volumes with delayed cerebral ischemia.

MATERIALS AND METHODS

Clinical and radiologic data were collected retrospectively from consecutive patients with aneurysmal SAH with available CT scans within 24 hours after ictus admitted to 2 academic centers between January 2009 and December 2011. Total blood volume was quantified using an automatic hemorrhage-segmentation algorithm. Segmented blood was manually classified as cisternal, intraventricular, intraparenchymal, or subdural. Adjusted ORs with 95% confidence intervals for delayed cerebral ischemia per milliliter of location-specific blood were calculated using multivariable logistic regression analysis.

RESULTS

We included 282 patients. Per milliliter increase in blood volume, the adjusted OR for delayed cerebral ischemia was 1.02 (95% CI, 1.01-1.04) for cisternal, 1.02 (95% CI, 1.00-1.04) for intraventricular, 0.99 (95% CI, 0.97-1.02) for intraparenchymal, and 0.96 (95% CI, 0.86-1.07) for subdural blood.

CONCLUSIONS

Our findings suggest that in patients with aneurysmal subarachnoid hemorrhage, the cisternal blood volume has a stronger relation with delayed cerebral ischemia than the blood volumes at other locations in the brain.

Ruptured middle cerebral artery aneurysms with a concomitant intraparenchymal hematoma: the role of hematoma volume

PURPOSE

To study whether clinical outcome data from our patient cohort could give support to the new recommendation in the AHA/ASA guidelines for the management of aneurysmal subarachnoid hemorrhage that states "that microsurgical clipping may receive increased consideration in patients with ruptured middle cerebral artery (MCA) aneurysms and large (>50 mL) intraparenchymal hematomas", while clinical outcome data supporting this recommendation are sparse.

METHODS

We reviewed the clinical and radiological data of 81 consecutive patients with MCA aneurysms and concomitant hematomas admitted between January 2006 and December 2015. The relation between (semi-automatically quantified) hematoma volume (< or > 50 ml), neurological condition on admission (poor: GCS < 8 or non-reactive pupils), treatment strategies (no treatment, coiling, or clipping with or without decompression and/or clot removal), and outcome (favorable: mRS score 0-3) was evaluated.

RESULTS

Clinical outcome data were available for 76 patients. A significant difference in favorable outcome (17 vs 68%) was seen when comparing patients with poor and good neurological condition on admission (p < 0.01). Patients with hematomas > 50 ml had similar outcomes for coiling and clipping, all underwent decompression. Patients with hematomas < 50 ml did not show differences in favorable outcome when comparing coiling and clipping with (33 and 31%) or without decompression (90 and 88%).

CONCLUSION

Poor neurological condition on admission, and not large intraparenchymal hematoma volume, was associated with poor clinical outcome. Therefore, even in patients with large hematomas, the neurological condition on admission and the aneurysm configuration seem to be equally important factors to determine the most appropriate treatment strategy.