Predictive Value of CTA Spot Sign on Hematoma Expansion in Intracerebral Hemorrhage Patients

Machine learning for predicting hematoma expansion in spontaneous intracerebral hemorrhage: a systematic review and meta-analysis

PURPOSE

Early identification of hematoma enlargement and persistent hematoma expansion (HE) in patients with cerebral hemorrhage is increasingly crucial for determining clinical treatments. However, due to the lack of clinically effective tools, radiomics has been gradually introduced into the early identification of hematoma enlargement. Though, radiomics has limited predictive accuracy due to variations in procedures. Therefore, we conducted a systematic review and meta-analysis to explore the value of radiomics in the early detection of HE in patients with cerebral hemorrhage.

METHODS

Eligible studies were systematically searched in PubMed, Embase, Cochrane and Web of Science from inception to April 8, 2024. English articles are considered eligible. The radiomics quality scoring (RQS) tool was used to evaluate included studies.

RESULTS

A total of 34 studies were identified with sample sizes ranging from 108 to 3016. Eleven types of models were involved, and the types of modeling contained mainly clinical, radiomic, and radiomic plus clinical features. The radiomics models seem to have better performance (0.77 and 0.73 C-index in the training cohort and validation cohort, respectively) than the clinical models (0.69 C-index in the training cohort and 0.70 C-index in the validation cohort) in discriminating HE. However, the C-index was the highest for the combined model in both the training (0.82) and validation (0.79) cohorts.

CONCLUSIONS

Machine learning based on radiomic plus clinical features has the best predictive performance for HE, followed by machine learning based on radiomic features, and can be used as a potential tool to assist clinicians in early judgment.

Imaging predictors of hemorrhagic progression of a contusion after traumatic brain injury: a systematic review and meta-analysis

The hemorrhagic progression of a contusion (HPC) after Traumatic brain injury (TBI) is one of the important causes of death in trauma patients. The purpose of this meta-analysis was to evaluate the predictive effect of imaging features of Computed tomography (CT) on HPC after TBI. A comprehensive systematic search was performed using PubMed, EMBASE, and WEB OF SCIENCE databases to identify all relevant literature. A total of 8 studies involving 2543 patients were included in this meta-analysis. Meta-analysis showed that subarachnoid hemorrhage (OR 3.28; 95% CI 2.57-4.20), subdural hemorrhage (OR 4.35; 95% CI 3.29-5.75), epidural hemorrhage (OR 1.47;95% CI 1.15-1.89), contrast extravasation (OR 11.81; 95% CI 4.86-28.71) had a predictive effect on the occurrence of HPC. Skull fracture (OR 1.64; 95% CI 0.84-3.19) showed no statistical significance, and midline displacement > 5 mm (OR 4.66; 95% CI 1.87-11.62) showed high heterogeneity. The results of this meta-analysis showed that some imaging features were effective predictors of HPC after TBI. Well-designed prospective studies are needed to more accurately assess the effective predictors of HPC after TBI.

Absolute and relative iodine concentrations in the spot sign and haematoma for prediction of haematoma expansion in spontaneous intracerebral haemorrhage

AIM

To explore the predictive value of absolute and relative iodine concentrations in the spot sign (SS) and haematoma on gemstone spectral imaging (GSI) for haematoma expansion (HE).

MATERIALS AND METHODS

Patients with spontaneous intracerebral haemorrhage (ICH) who underwent computed tomography (CT) angiography using GSI were divided into an SS-positive group and an SS-negative group. In the SS-positive group, absolute and relative iodine concentrations in the SS (aICIS and rICIS, respectively) were measured. In the SS-negative group, absolute and relative iodine concentrations in haematoma (aICIH and rICIH, respectively) were measured. The area under the receiver operating characteristic curve (AUC-ROC) was used to investigate the HE predictive performance of aICIS, rICIS, and their combination in the SS-positive group, as well as the HE predictive performance of aICIH, rICIH, and their combination in the SS-negative group. The risk variables for HE in the two groups were investigated separately using logistic regression.

RESULTS

A total of 123 spontaneous ICH patients were enrolled. In the SS-positive group, the AUC of aICIS, rICIS, and their combination for predicting HE were 0.853, 0.893, and 0.922, respectively. rICIS was demonstrated to be a standalone predictor of HE via logistic regression. In the SS-negative group, aICIH, rICIH, and their combination had AUC-ROC values of 0.552, 0.783, and 0.851, respectively, to predict HE. According to multivariate analysis, rICIH was a reliable predictor of HE.

CONCLUSION

Absolute and relative iodine concentrations in the SS and haematoma can predict HE.

Ferroptosis and mitochondrial dysfunction in acute central nervous system injury

Acute central nervous system injuries (ACNSI), encompassing traumatic brain injury (TBI), non-traumatic brain injury like stroke and encephalomeningitis, as well as spinal cord injuries, are linked to significant rates of disability and mortality globally. Nevertheless, effective and feasible treatment plans are still to be formulated. There are primary and secondary injuries occurred after ACNSI. Most ACNSIs exhibit comparable secondary injuries, which offer numerous potential therapeutic targets for enhancing clinical outcomes. Ferroptosis, a newly discovered form of cell death, is characterized as a lipid peroxidation process that is dependent on iron and oxidative conditions, which is also indispensable to mitochondria. Ferroptosis play a vital role in many neuropathological pathways, and ACNSIs may induce mitochondrial dysfunction, thereby indicating the essentiality of the mitochondrial connection to ferroptosis in ACNSIs. Nevertheless, there remains a lack of clarity regarding the involvement of mitochondria in the occurrence of ferroptosis as a secondary injuries of ACNSIs. In recent studies, anti-ferroptosis agents such as the ferroptosis inhibitor Ferrostain-1 and iron chelation therapy have shown potential in ameliorating the deleterious effects of ferroptosis in cases of traumatic ACNSI. The importance of this evidence is extremely significant in relation to the research and control of ACNSIs. Therefore, our review aims to provide researchers focusing on enhancing the therapeutic outcomes of ACNSIs with valuable insights by summarizing the physiopathological mechanisms of ACNSIs and exploring the correlation between ferroptosis, mitochondrial dysfunction, and ACNSIs.

Prediction of Hematoma Expansion in Hypertensive Intracerebral Hemorrhage by a Radiomics Nomogram

Objective

To develop and validate a radiomics-based nomogram model which aimed to predict hematoma expansion (HE) in hypertensive intracerebral hemorrhage (HICH).

Methods

Patients with HICH (n=187) were included from October 2017 to March 2022 in the Yongchuan Affiliated Hospital of Chongqing Medical University. Patients were randomly divided into a training set (n=130) and a validation set (n=57) in a ratio of 7:3. The radiomic features were extracted from the regions of interest (including main hematoma, the surrounding small hematoma(s) and perihematomal edema) in the first CT scan images. The variance threshold, SelectKBest and LASSO (least absolute shrinkage and selection operator), features were selected and the radiomics signature was built. Multivariate logistic regression was used to establish a nomogram based on clinical risk factors and the Rad-score. A receiver operating characteristic (ROC) curve was used to evaluate the generalization of the models' performance. The calibration curve and the Hosmer-Lemeshow test were used to assess the calibration of the predictive nomogram. And decision curve analysis (DCA) was used to evaluate the prediction model.

Results

Thirteen radiomics features were selected to construct the radiomics signature, which has a robust association with HE. The radiomics model found that blend sign was a predictive factor of HE. The radiomics model ROC in the training set was 0.89 (95%CI 0.82-0.96) and was 0.82 (95%CI 0.60-0.93) in the validation set. The nomogram model was built using the combined prediction model based on radiomics and blend sign, and worked well in both the training set (ROC: 0.90[95%CI 0.83-0.96]) and the validation set (ROC: 0.88[95%CI 0.71-0.93]).

Conclusion

The radiomic signature based on CT of HICH has high accuracy for predicting HE. The combined prediction model of radiomics and blend sign improves the prediction performance.

Quamdiu? Time to proficiency in endoscope-assisted minimally invasive clot evacuation

INTRODUCTION

Spontaneous intraparenchymal brain hemorrhages are a devastating disease associated with significant disability or death. Minimally invasive clot evacuation (MICE) techniques can reduce mortality. We reviewed our experience with learning endoscope-assisted MICE to determine whether adequate results could be obtained in less than 10 cases.

METHODS

We performed a retrospective chart review of patients undergoing endoscope-assisted MICE at a single institution by a single surgeon from January 1, 2018 to January 1, 2023 using a neuro-endoscope, a commercial clot evacuation device, and frameless stereotaxis. Demographic data was collected along with surgical results and complications. Image analysis using software determined the degree of clot removal. Hospital length of stay and functional outcomes were assessed using the Glasgow Coma Scale score (GCS) and Glasgow Outcome Score (extended) (GOS-E).

RESULTS

Eleven patients were identified: average age 60.82 years old, 64 % male, all had hypertension. There was a clear improvement in IPH evacuation over the series. By case #7, greater than 80 % of clot volume was evacuated consistently. All patients remained neurologically stable or improved following surgery. In long-term follow-up, four patients (36.4 %) had good outcomes (GOS-E ≥ 6) and 2 patients had fair outcomes (GOS-E = 4) (18 %). There were no surgical mortalities, re-hemorrhages, or infections.

CONCLUSIONS

With an experience of less than 10 cases, it is possible to obtain results comparable to most published series of endoscope-assisted MICE. Benchmarks such as greater than 80 % volume removal, less than 15 mL residual, and 40 % good functional outcomes can be obtained.

A predictive nomogram for intracerebral hematoma expansion based on non-contrast computed tomography and clinical features

PURPOSE

To develop and validate a new nomogram utilizing non-contrast computed tomography (NCCT) signs and clinical factors for predicting hematoma expansion (HE) in patients with spontaneous intracerebral hemorrhage (ICH).

METHODS

HE was defined as > 6 mL or 33% increase in baseline hematoma volume. Multivariable logistic regression analysis was performed to identify the predictors of HE. The discriminatory performance of the proposed model was evaluated via receiver operation characteristic (ROC) analysis, and the predictive accuracy was assessed by a calibration curve. The nomogram was established by R programming language. The decision curve analysis and clinical impact curve were drawn according to the related risk factors.

RESULTS

A total of 506 patients with spontaneous ICH were recruited in the development cohort, and 103 patients were registered as the external validation cohort. Among the development cohort, 132 (26.09%) experienced HE. Glasgow coma scale (GCS) (P < 0.001), neutrophil to lymphocyte ratio (NLR) (P < 0.001), blend sign (P < 0.001), swirl sign (P < 0.001), and hypodensities (P = 0.003) were significant predictors of HE, by which were used to establish the nomogram. The model demonstrated good performance with high area under the curve both in the development (AUC = 0.908; 95% confidence interval, 0.880-0.936) and the external validation (AUC = 0.844; 95% confidence interval, 0.760-0.908) cohort. The calibration curve illustrated a high accuracy for HE prediction.

CONCLUSION

The nomogram derived from NCCT markers and clinical factors outperformed the NCCT signs-only model in predicting HE for patients with ICH, thus providing an effective and noninvasive tool for the risk stratification of HE.

Identifying risk factors of intracerebral hemorrhage stability using explainable attention model

Segmentation of intracerebral hemorrhage (ICH) helps improve the quality of diagnosis, draft the desired treatment methods, and clinically observe the variations with healthy patients. The clinical utilization of various ICH progression scoring systems has limitations due to the systems' modest predictive value. This paper proposes a single pipeline of a multi-task model for end-to-end hemorrhage segmentation and risk estimation. We introduce a 3D spatial attention unit and integrate it into the state-of-the-art segmentation architecture, UNet, to enhance the accuracy by bootstrapping the global spatial representation. We further extract the geometric features from the segmented hemorrhage volume and fuse them with clinical features such as CT angiography (CTA) spot, Glasgow Coma Scale (GCS), and age to predict the ICH stability. Several state-of-the-art machine learning techniques such as multilayer perceptron (MLP), support vector machine (SVM), gradient boosting, and random forests are applied to train stability estimation and to compare the performances. To align clinical intuition with model learning, we determine the shapely values (SHAP) and explain the most significant features for the ICH risk scoring system. A total of 79 patients are included, of which 20 are found in critical condition. Our proposed single pipeline model achieves a segmentation accuracy of 86.3%, stability prediction accuracy of 78.3%, and precision of 82.9%; the mean square error of exact expansion rate regression is observed to be 0.46. The SHAP analysis reveals that CTA spot sign, age, solidity, location, and length of the first axis of the ICH volume are the most critical characteristics that help define the stability of the stroke lesion. We also show that integrating significant geometric features with clinical features can improve the ICH progression scoring by predicting long-term outcomes. Graphical abstract Overview of our proposed method comprising of spatial attention and feature extraction mechanisms. The architecture is trained on the input CT images, and the first step output is the predicted segmentation of the hemorrhagic region. The output is fed into a geometric feature extractor and is fused with clinical features to estimate ICH stability using a multilayer perceptron (MLP).

Comparison of Radiomic Models Based on Different Machine Learning Methods for Predicting Intracerebral Hemorrhage Expansion

PURPOSE

The objective of this study was to predict hematoma expansion (HE) by radiomic models based on different machine learning methods and determine the best radiomic model through the comparison.

METHOD

A total of 108 patients with intracerebral hemorrhage were retrospectively evaluated. Images of baseline non-contrast computed tomography (NCCT) and follow-up NCCT scan within 24 h were retrospectively reviewed. An HE was defined as a volume increase of more than 33% or an increase greater than 12.5 mL from the volume of the baseline NCCT. Texture parameters of the baseline NCCT images were selected by the least absolute shrinkage and selection operator (LASSO) regression. We used support vector machine (SVM), decision tree (DT), conditional inference trees (CIT), random forest (RF), k‑nearest neighbors (KNN), back-propagation neural network (BPNet) and Bayes to build models. Receiver operating characteristic (ROC) analysis and decision curve analysis (DCA) was performed and compared among models.

RESULTS

Every model had a relatively high AUC (all > 0.75), SVM and KNN had the highest AUC of 0.91. There were significant differences between SVM and CIT (Z > 2.266, p = 0.02345), KNN and CIT (Z = 2.4834, p = 0.01301), RF and CIT (Z = 2.6956, p = 0.007027), KNN and BPNet (Z = 2.0122, p = 0.0442), RF and BPNet (Z = 1.9793, p = 0.04778). There was no significant difference among SVM, DT, RF, KNN and Bayes (p > 0.05). The SVM obtained the largest net benefit when the threshold probability was less than 0.33, while KNN obtained the largest net benefit when the threshold probability was greater than 0.33. Combined with ROC and DCA, SVM and KNN performed better in all the models for predicting HE.

CONCLUSION

Radiomic models based on different machine learning methods can be used to predict HE and the models generated by SVM and KNN performed best.

Quantitative CT radiomics-based models for prediction of haematoma expansion and poor functional outcome in primary intracerebral haemorrhage

OBJECTIVES

To test radiomics-based features extracted from noncontrast CT of patients with spontaneous intracerebral haemorrhage for prediction of haematoma expansion and poor functional outcome and compare them with radiological signs and clinical factors.

MATERIALS AND METHODS

Seven hundred fifty-four radiomics-based features were extracted from 1732 scans derived from the TICH-2 multicentre clinical trial. Features were harmonised and a correlation-based feature selection was applied. Different elastic-net parameterisations were tested to assess the predictive performance of the selected radiomics-based features using grid optimisation. For comparison, the same procedure was run using radiological signs and clinical factors separately. Models trained with radiomics-based features combined with radiological signs or clinical factors were tested. Predictive performance was evaluated using the area under the receiver operating characteristic curve (AUC) score.

RESULTS

The optimal radiomics-based model showed an AUC of 0.693 for haematoma expansion and an AUC of 0.783 for poor functional outcome. Models with radiological signs alone yielded substantial reductions in sensitivity. Combining radiomics-based features and radiological signs did not provide any improvement over radiomics-based features alone. Models with clinical factors had similar performance compared to using radiomics-based features, albeit with low sensitivity for haematoma expansion. Performance of radiomics-based features was boosted by incorporating clinical factors, with time from onset to scan and age being the most important contributors for haematoma expansion and poor functional outcome prediction, respectively.

CONCLUSION

Radiomics-based features perform better than radiological signs and similarly to clinical factors on the prediction of haematoma expansion and poor functional outcome. Moreover, combining radiomics-based features with clinical factors improves their performance.

KEY POINTS

• Linear models based on CT radiomics-based features perform better than radiological signs on the prediction of haematoma expansion and poor functional outcome in the context of intracerebral haemorrhage. • Linear models based on CT radiomics-based features perform similarly to clinical factors known to be good predictors. However, combining these clinical factors with radiomics-based features increases their predictive performance.

The Role of Serum Calcium Level in Intracerebral Hemorrhage Hematoma Expansion: Is There Any?

Spontaneous intracerebral hemorrhage (ICH) is a devastating form of stroke, with a high rate of mortality and morbidity. Even with the best current medical or surgical interventions, outcomes remain poor. The location and initial hematoma volume are strong predictors of mortality. Hematoma expansion (HE) is a further marker of poor prognosis that may be at least partly preventable. Several risk factors for HE have been identified, including baseline ICH volume, anticoagulation, and computed tomography angiography spot signs. Recent studies have shown the correlation of serum calcium (Ca⁺⁺) levels on admission with HE. Low serum Ca⁺⁺ level has been associated with larger hematoma volume at the time of presentation, HE, and worse outcome. Although the causal and mechanistic links between low serum Ca⁺⁺ level and HE are not well understood, several mechanisms have been proposed including coagulopathy, platelet dysfunction, and higher blood pressure (BP) in the context of low serum Ca⁺⁺ level. However, low serum Ca⁺⁺ level might be only a biomarker of the adaptive response due to acute inflammatory response following acute ICH. The purpose of the current review is to discuss the evidence regarding the possible role of low serum Ca⁺⁺ level on HE in acute ICH.

Is four-dimensional CT angiography as effective as digital subtraction angiography in the detection of the underlying causes of intracerebral haemorrhage: a systematic review

PURPOSE

To determine whether the sensitivity and specificity of four-dimensional CTA (4D-CTA) are equivalent to digital subtraction angiography (DSA) in the detection of underlying vascular abnormalities in patients with intracerebral haemorrhage (ICH).

METHODS

A systematic review of studies comparing 4D-CTA with DSA in the detection of the underlying structural causes of ICH was performed on the literature published between 1998 and 2019.

RESULTS

We identified a total of 237 articles from PubMed, SCOPUS and Web of Science using the following Medical Subject Headings (MeSH) terms: primary intracerebral haemorrhage, 4D-CTA, DSA, cerebral haemorrhage, angiography, digital subtraction, arteriovenous malformations, 4D, CTA, dynamic-CTA and time-resolved CTA. Following the removal of duplicate publications and articles failing to meet our inclusion criteria, there were four articles potentially viable for analysis. Therefore, there were not sufficient studies to provide a statistically meaningful meta-analysis.

CONCLUSION

The review of current literature has demonstrated that there are few published studies comparing 4D-CTA with DSA in spontaneous ICH, with only four suitable studies identified for potential analysis. However, due to the restricted number of patients and high sensitivity and specificity of 3 studies (100%), performing a meta-analysis was not meaningful. Qualitative analysis of the data concluded that 4D-CTA has the diagnostic potential to replace invasive DSA in certain cases with vascular abnormalities. However, further research studies directly comparing 4D-CTA with DSA using larger prospective patient cohorts are required to strengthen the evidence base.

Correlation between Spot Sign and Intracranial Hemorrhage Expansion on Dual-Phase CT Angiography

Purpose: Expansion of intracranial hemorrhage (ICH) is an important predictor of poor clinical outcome. ICH expansion can be predicted with a spot sign on computed tomographic angiography (CTA). We aimed to evaluate the correlation between spot signs on CTA and ICH expansion on dual-phase CTA. Methods: Patients with spontaneous ICH between January 2017 and April 2019 who underwent an initial CT, dual-phase CTA, and a subsequent CT were retrospectively identified. ICH expansion was defined as volume growth of >33% or >6 mL. We analyzed the presence and change in size of the spot sign in the first phase and second phase CTA. Also, we divided the morphological status of the spot sign, such as a dot-like lesion or linear contrast extravasation, in the first and second phase CTA. Results: A total of 206 patients, including 38 (18.5%) with ICH expansion and 45 (21.8%) with a spot sign, qualified for analysis. Of patients with a spot sign, 26 (57.8%) had ICH expansion on subsequent CT. Increased size of a spot sign in second-phase CTA was more frequent in the ICH expansion group than in the no-expansion group (96.2% vs. 52.6%, p < 0.001). First visualization of a spot sign in the second phase was more common in the no-expansion group than in the ICH expansion group (47.4% vs. 3.8%, p < 0.001). The morphological patterns of a spot sign between the two groups were not significantly different. Conclusion: Spot signs on dual-phase CTA have different sizes and morphological patterns. Increased size of a spot sign in the second phase of CTA can help identify patients at risk for ICH expansion.

The risk factors and prognosis of delayed perihematomal edema in patients with spontaneous intracerebral hemorrhage

PURPOSE

We hypothesize delayed perihematomal edema (DHE) leads to secondary injury after spontaneous intracerebral hemorrhage (sICH) with a poor prognosis. Hence, we need to investigate the risk factors of DHE and identify whether DHE will predict the poor outcome of sICH.

METHODS

We retrospectively recruited 121 patients with sICH admitted to the Department of Neurology from January 2014 to August 2018. After dividing all these patients into DHE group and non-DHE group, we analyzed the potential risk factors and outcome of DHE using a multivariate logistic regression model.

RESULTS

We conclude DHE after sICH associates with age, hospitalization time, hematoma shape, blood pressure upon admission, alcohol consumption, blood sodium level, and baseline hematoma volume within 24 hours after symptom onset, among which differences were statistically significant (P < .05). Logistic regression analysis finally identified that age (OR = 0.958, 95% CI = 0.923-0.995) and the baseline hematoma volume (OR = 1.161, 95% CI = 1.089-1.238) were the most significant risk factors for DHE, and moreover, the DHE (OR = 3.062, 95% CI = 1.196-7.839) was also a risk factor for poor prognosis in sICH patients.

CONCLUSION

We suggest DHE is a clinical predictor of secondary injury following sICH and poor prognosis. In addition, age and baseline hematoma volume are considered significant high-risk factors for DHE in patients with sICH.

Noncontrast computer tomography-based radiomics model for predicting intracerebral hemorrhage expansion: preliminary findings and comparison with conventional radiological model

OBJECTIVES

To develop a radiomics model for predicting hematoma expansion in patients with intracerebral hemorrhage (ICH) and to compare its predictive performance with a conventional radiological feature-based model.

METHODS

We retrospectively analyzed 251 consecutive patients with acute ICH. Two radiologists independently assessed baseline noncontrast computed tomography (NCCT) images. For each radiologist, a radiological model was constructed from radiological variables; a radiomics score model was constructed from high-dimensional quantitative features extracted from NCCT images; and a combined model was constructed using both radiological variables and radiomics score. Development of models was constructed in a primary cohort (n = 177). We then validated the results in an independent validation cohort (n = 74). The primary outcome was hematoma expansion. We compared the three models for predicting hematoma expansion. Predictive performance was assessed with the receiver operating characteristic (ROC) curve analysis.

RESULTS

In the primary cohort, combined model and radiomics model showed greater AUCs than radiological model for both readers (all p < .05). In the validation cohort, combined model and radiomics model showed greater AUCs, sensitivities, and accuracies than radiological model for reader 2 (all p < .05). Combined model showed greater AUC than radiomics model for reader 1 only in the primary cohort (p = .03). Performance of three models was comparable between reader 1 and reader 2 in both cohorts (all p > .05).

CONCLUSIONS

NCCT-based radiomics model showed high predictive performance and outperformed radiological model in the prediction of early hematoma expansion in ICH patients.

KEY POINTS

• Radiomics model showed better performance for prediction of hematoma expansion in patients with intracerebral hemorrhage than radiological feature-based model. • Hematomas which expanded in follow-up NCCT tended to be larger in baseline volume, more irregular in shape, more heterogeneous in composition, and coarser in texture. • A radiomics model provides a convenient and objective tool for prediction of hematoma expansion that helps to define subsets of patients who would benefit from anti-expansion therapy.

Imaging of Brain Trauma

Conventional imaging in the acute setting of brain trauma, relevant pathophysiology of injury, and advanced imaging techniques that may provide value in understanding the immediate management and long-term sequela of traumatic brain injury are reviewed. Key imaging findings that can guide clinical management related to such injuries as concussions, hematomas, dissections, dural atrioventricular fistula, and diffuse axonal injury are discussed. The role and accuracy of computed tomography, dual-energy computed tomography, computed tomography angiography, and magnetic resonance angiography in the acute setting are evaluated. In addition, caveats related to imaging the elderly and pediatric population are addressed.

EIT Imaging of Intracranial Hemorrhage in Rabbit Models Is Influenced by the Intactness of Cranium

Electrical impedance tomography (EIT) has been shown to be a promising, bedside imaging method to monitor the progression of intracranial hemorrhage (ICH). However, the observed impedance changes within brain related to ICH differed among groups, and we hypothesized that the cranium intactness (open or closed) may be the one of potential reasons leading to the difference. Therefore, the aim of this study was to investigate this effect of open or closed cranium on impedance changes within brain in the rabbit ICH model. In this study, we first established the ICH model in 12 rabbits with the open cranium and in 12 rabbits with the closed cranium. Simultaneously, EIT measurements on the rabbits' heads were performed to record the impedance changes caused by injecting the autologous nonheparinized blood into cerebral parenchyma. Finally, the regional impedance changes on EIT images and the whole impedance changes were analyzed. It was surprisingly found that when the cranium was open, the impedance of the area where the blood was injected, as well as the whole brain impedance, decreased with the amount of blood being injected; when the cranium was closed, while the impedance of the area where blood was not injected continued to increase, the impedance of the area where blood was injected decreased within 20s of the blood being injected and then remained almost unchanged, and the whole brain impedance had a small fall and then notably increased. The results have validated that the cranium completeness (open or closed) has influences on impedance changes within brain when using EIT to monitor ICH. In future study on application of EIT to monitor ICH, the cranium completeness should be taken into account for establishing an ICH model and analyzing the corresponding EIT results.

Active Reperfusion Hemorrhage during Thrombectomy: Angiographic Findings and Real-Time Correlation with the CT "Spot Sign"

Introduction

Symptomatic intracranial hemorrhage represents one of the most feared complications of endovascular reperfusion. We aim to describe a series of patients that experienced immediate reperfusion injury with active intraprocedural extravasation within the territory of the deep penetrating arteries and provide real-time correlation with CT "spot sign."

Methods

This was a retrospective analysis of patients that suffered reperfusion injury with active arterial extravasation during endovascular stroke treatment in two tertiary care centers.

Results

Five patients were identified. Median age was 63 (58-71) years, 66% were male. Median NIHSS was 13.5 (9.5-23.0), platelet level 212,000 (142,000-235,000), baseline systolic blood pressure 152 (133-201) mm Hg, and non-contrast CT ASPECTS 7.0 (6.5-9.0). Two patients were taking aspirin and one had received intravenous thrombolysis. There were three middle cerebral artery M1, one internal carotid artery terminus, and one vertebrobasilar junction occlusion. Three patients had anterior circulation tandem occlusions. Stroke etiology was extracranial atherosclerosis (n = 2), intracranial atherosclerosis (n = 2), and cervical dissection (n = 1). The median time from onset to puncture was 5.5 (3.9-8.6) h. Intravenous heparin was administered in all patients (median dose of 4,750 [3,250-6,000] units) and intravenous abciximab in four. All tandem cases had the cervical lesion addressed first. Four lenticulostriates and one paramedian pontine artery were involved. Intraprocedural flat-panel CT was performed in four (80%) cases and provided real-time correlation between the active contrast extravasation and the "spot sign." The bailout included use of protamine, blood pressure control, and balloon guide catheter or intracranial compliant balloon inflation plus coiling of targeted vessel. All patients had angiographic cessation of bleeding at the end of the procedure with parenchymal hemorrhage type 1 in one case and type 2 in four. Three patients had modified Rankin score of 4 and two were dead at 90 days.

Conclusions

Active reperfusion hemorrhage involving perforator arteries was observed to correlate with the CT "spot sign" and to be associated with poor outcomes.