Hematoma Expansion in Intracerebral Hemorrhage: An Update on Prediction and Treatment

Monocyte-to-Albumin Ratio Is Associated With Hematoma Expansion in Spontaneous Intracerebral Hemorrhage

BACKGROUND

Hematoma expansion (HE) after spontaneous intracerebral hemorrhage (ICH) is a severe complication that independently predicts poor prognosis. In this study, we aimed to investigate whether monocyte-to-albumin ratio (MAR), a novel marker of systemic inflammation, could predict HE in patients with ICH.

METHODS

We retrospectively assessed the data of patients with ICH. The clinical, imaging, and laboratory test data including, the MAR on admission, were analyzed. A multivariate logistic regression analysis was carried out to explore the relationship between MAR and hematoma growth. The receiver operating characteristic (ROC) curve was employed to investigate the predictive value of MAR for HE after ICH.

RESULTS

A total of 246 patients were included in the present study. Multivariate logistic regression analysis demonstrated that the MAR was associated with HE (odds ratio [OR] = 1.179; 95% confidence interval, 1.093-1.272; p = 0.000). ROC curve analysis showed that MAR could predict HE, with an area under the curve of 0.802 (95% CI: 0.744-0.859, p < 0.001). The optimal predictive cutoff value of MAR for HE was 10.01 (sensitivity: 72.43%, specificity: 77.05%).

CONCLUSIONS

Our results suggested that a high MAR on admission was associated with an increased risk of HE in ICH patients, and MAR can become an independent predictor of HE in ICH patients.

Acute calcitriol treatment mitigates vitamin D deficiency-associated mortality after intracerebral haemorrhage

OBJECTIVE

Vitamin D deficiency (VDD) is emerging as a predictor of poor prognosis in various neurological conditions, where clinical outcomes are often worse in stroke patients with VDD. This study aimed to provide experimental evidence on whether and how pre-existing VDD would affect survival and neurofunctional outcomes in intracerebral haemorrhage (ICH), and to evaluate whether acute vitamin D (VD) supplementation would improve post-stroke outcomes.

METHODS

Experimental ICH models were induced in mice with and without VDD. Haematoma size was measured using T2*-weighted MRI and haemoglobin concentration. Post-ICH mortality, neurofunctional outcomes and the extent of blood-brain barrier (BBB) leakage were assessed to identify their correlations with VD status. Therapeutic benefits of acute VD administration were also evaluated.

RESULTS

Mice with VDD exhibited significantly higher acute mortality rates and more severe motor deficits than mice without VDD post-ICH. Marked haematoma expansion and increased Evans blue extravasation were observed in VDD mice, suggesting that VDD was associated outcomes with increased BBB disruption. Acute treatment with a loading dose of VD (calcitriol) significantly improved outcomes in VDD mice.

CONCLUSION

This study provides novel insights into the pathophysiological mechanisms at play in ICH concomitant with VDD and a scientific rationale for acute treatment with VD.

A Deep Learning-Based Framework for Predicting Intracerebral Hemorrhage Hematoma Expansion Using Head Non-contrast CT Scan

RATIONALE AND OBJECTIVES

Hematoma expansion (HE) in intracerebral hemorrhage (ICH) is a critical factor affecting patient outcomes, yet effective clinical tools for predicting HE are currently lacking. We aim to develop a fully automated framework based on deep learning for predicting HE using only clinical non-contrast CT (NCCT) scans.

MATERIALS AND METHODS

A large retrospective dataset (n = 2484) was collected from 84 centers, while a prospective dataset (n = 500) was obtained from 26 additional centers. Baseline NCCT scans and follow-up NCCT scans were conducted within 6 h and 48 h from symptom onset, respectively. HE was defined as a volume increase of more than 6 mL on the follow-up NCCT. The retrospective dataset was divided into a training set (n = 1876) and a validation set (n = 608) by patient inclusion time. A two-stage framework was trained to predict HE, and its performance was evaluated on both the validation and prospective sets. Receiver operating characteristics area under the curve (AUC), sensitivity, and specificity were leveraged.

RESULTS

Our two-stage framework achieved an AUC of 0.760 (95% CI 0.724-0.799) on the retrospective validation set and 0.806 (95% CI 0.750-0.859) on the prospective set, outperforming the commonly used BAT score, which had AUCs of 0.582 and 0.699, respectively.

CONCLUSION

Our framework can automatically and robustly identify ICH patients at high risk of HE using admission head NCCT scans, providing more accurate predictions than the BAT score.

CT radiomics combined with clinical and radiological factors predict hematoma expansion in hypertensive intracerebral hemorrhage

OBJECTIVES

This study aimed to establish a hematoma expansion (HE) prediction model for hypertensive intracerebral hemorrhage (HICH) patients by combining CT radiomics, clinical information, and conventional imaging signs.

METHODS

A retrospective continuous collection of HICH patients from three medical centers was divided into a training set (n = 555), a validation set (n = 239), and a test set (n = 77). Extract radiomics features from baseline CT plain scan images and combine them with clinical information and conventional imaging signs to construct radiomics models, clinical imaging sign models, and hybrid models, respectively. The models will be evaluated using the area under the curve (AUC), clinical decision curve analysis (DCA), net reclassification index (NRI), and integrated discrimination improvement (IDI).

RESULTS

In the training, validation, and testing sets, the radiomics model predicts an AUC of HE of 0.885, 0.827, and 0.894, respectively, while the clinical imaging sign model predicts an AUC of HE of 0.759, 0.725, and 0.765, respectively. Glasgow coma scale score at admission, first CT hematoma volume, irregular hematoma shape, and radiomics score were used to construct a hybrid model, with AUCs of 0.901, 0.838, and 0.917, respectively. The DCA shows that the hybrid model had the highest net profit rate. Compared with the radiomics model and the clinical imaging sign model, the hybrid model showed an increase in NRI and IDI.

CONCLUSION

The hybrid model based on CT radiomics combined with clinical and radiological factors can effectively individualize the evaluation of the risk of HE in patients with HICH.

CLINICAL RELEVANCE STATEMENT

CT radiomics combined with clinical information and conventional imaging signs can identify HICH patients with a high risk of HE and provide a basis for clinical-targeted treatment.

KEY POINTS

HE is an important prognostic factor in patients with HICH. The hybrid model predicted HE with training, validation, and test AUCs of 0.901, 0.838, and 0.917, respectively. This model provides a tool for a personalized clinical assessment of early HE risk.

Telemedicine Protocols for the Management of Patients with Acute Spontaneous Intracerebral Hemorrhage in Rural and Medically Underserved Areas in Gangwon State : Recommendations for Doctors with Less Expertise at Local Emergency Rooms

Previously, we reported the concept of a cloud-based telemedicine platform for patients with intracerebral hemorrhage (ICH) at local emergency rooms in rural and medically underserved areas in Gangwon state by combining artificial intelligence and remote consultation with a neurosurgeon. Developing a telemedicine ICH treatment protocol exclusively for doctors with less ICH expertise working in emergency rooms should be part of establishing this system. Difficulties arise in providing appropriate early treatment for ICH in rural and underserved areas before the patient is transferred to a nearby hub hospital with stroke specialists. This has been an unmet medical need for decade. The available reporting ICH guidelines are realistically possible in university hospitals with a well-equipped infrastructure. However, it is very difficult for doctors inexperienced with ICH treatment to appropriately select and deliver ICH treatment based on the guidelines. To address these issues, we developed an ICH telemedicine protocol. Neurosurgeons from four university hospitals in Gangwon state first wrote the guidelines, and professors with extensive ICH expertise across the country revised them. Guidelines and recommendations for ICH management were described as simply as possible to allow more doctors to use them easily. We hope that our effort in developing the telemedicine protocols will ultimately improve the quality of ICH treatment in local emergency rooms in rural and underserved areas in Gangwon state.

Immune-mediated disruption of the blood-brain barrier after intracerebral hemorrhage: Insights and potential therapeutic targets

AIMS

Intracerebral hemorrhage (ICH) is a condition that arises due to the rupture of cerebral blood vessels, leading to the flow of blood into the brain tissue. One of the pathological alterations that occurs during an acute ICH is an impairment of the blood-brain barrier (BBB), which leads to severe perihematomal edema and an immune response.

DISCUSSION

A complex interplay between the cells of the BBB, for example, pericytes, astrocytes, and brain endothelial cells, with resident and infiltrating immune cells, such as microglia, monocytes, neutrophils, T lymphocytes, and others accounts for both damaging and protective mechanisms at the BBB following ICH. However, the precise immunological influence of BBB disruption has yet to be richly ascertained, especially at various stages of ICH.

CONCLUSION

This review summarizes the changes in different cell types and molecular components of the BBB associated with immune-inflammatory responses during ICH. Furthermore, it highlights promising immunoregulatory therapies to protect the integrity of the BBB after ICH. By offering a comprehensive understanding of the mechanisms behind BBB damage linked to cellular and molecular immunoinflammatory responses after ICH, this article aimed to accelerate the identification of potential therapeutic targets and expedite further translational research.

Predicting Outcome of Patients With Cerebral Hemorrhage Using a Computed Tomography-Based Interpretable Radiomics Model: A Multicenter Study

OBJECTIVE

The aim of this study was to develop and validate an interpretable and highly generalizable multimodal radiomics model for predicting the prognosis of patients with cerebral hemorrhage.

METHODS

This retrospective study involved 237 patients with cerebral hemorrhage from 3 medical centers, of which a training cohort of 186 patients (medical center 1) was selected and 51 patients from medical center 2 and medical center 3 were used as an external testing cohort. A total of 1762 radiomics features were extracted from nonenhanced computed tomography using Pyradiomics, and the relevant macroscopic imaging features and clinical factors were evaluated by 2 experienced radiologists. A radiomics model was established based on radiomics features using the random forest algorithm, and a radiomics-clinical model was further trained by combining radiomics features, clinical factors, and macroscopic imaging features. The performance of the models was evaluated using area under the curve (AUC), sensitivity, specificity, and calibration curves. Additionally, a novel SHAP (SHAPley Additive exPlanations) method was used to provide quantitative interpretability analysis for the optimal model.

RESULTS

The radiomics-clinical model demonstrated superior predictive performance overall, with an AUC of 0.88 (95% confidence interval, 0.76-0.95; P < 0.01). Compared with the radiomics model (AUC, 0.85; 95% confidence interval, 0.72-0.94; P < 0.01), there was a 0.03 improvement in AUC. Furthermore, SHAP analysis revealed that the fusion features, rad score and clinical rad score, made significant contributions to the model's decision-making process.

CONCLUSION

Both proposed prognostic models for cerebral hemorrhage demonstrated high predictive levels, and the addition of macroscopic imaging features effectively improved the prognostic ability of the radiomics-clinical model. The radiomics-clinical model provides a higher level of predictive performance and model decision-making basis for the risk prognosis of cerebral hemorrhage.

Predicting hematoma expansion using machine learning: An exploratory analysis of the ATACH 2 trial

INTRODUCTION

Hematoma expansion (HE) in patients with intracerebral hemorrhage (ICH) is a key predictor of poor prognosis and potentially amenable to treatment. This study aimed to build a classification model to predict HE in patients with ICH using deep learning algorithms without using advanced radiological features.

METHODS

Data from the ATACH-2 trial (Antihypertensive Treatment of Acute Cerebral Hemorrhage) was utilized. Variables included in the models were chosen as per literature consensus on salient variables associated with HE. HE was defined as increase in either >33% or 6 mL in hematoma volume in the first 24 h. Multiple machine learning algorithms were employed using iterative feature selection and outcome balancing methods. 70% of patients were used for training and 30% for internal validation. We compared the ML models to a logistic regression model and calculated AUC, accuracy, sensitivity and specificity for the internal validation models respective models.

RESULTS

Among 1000 patients included in the ATACH-2 trial, 924 had the complete parameters which were included in the analytical cohort. The median [interquartile range (IQR)] initial hematoma volume was 9.93.mm3 [5.03-18.17] and 25.2% had HE. The best performing model across all feature selection groups and sampling cohorts was using an artificial neural network (ANN) for HE in the testing cohort with AUC 0.702 [95% CI, 0.631-0.774] with 8 hidden layer nodes The traditional logistic regression yielded AUC 0.658 [95% CI, 0.641-0.675]. All other models performed with less accuracy and lower AUC. Initial hematoma volume, time to initial CT head, and initial SBP emerged as most relevant variables across all best performing models.

CONCLUSION

We developed multiple ML algorithms to predict HE with the ANN classifying the best without advanced radiographic features, although the AUC was only modestly better than other models. A larger, more heterogenous dataset is needed to further build and better generalize the models.

Molecular Pathogenesis of Ischemic and Hemorrhagic Strokes: Background and Therapeutic Approaches

Stroke represents one of the neurological diseases most responsible for death and permanent disability in the world. Different factors, such as thrombus, emboli and atherosclerosis, take part in the intricate pathophysiology of stroke. Comprehending the molecular processes involved in this mechanism is crucial to developing new, specific and efficient treatments. Some common mechanisms are excitotoxicity and calcium overload, oxidative stress and neuroinflammation. Furthermore, non-coding RNAs (ncRNAs) are critical in pathophysiology and recovery after cerebral ischemia. ncRNAs, particularly microRNAs, and long non-coding RNAs (lncRNAs) are essential for angiogenesis and neuroprotection, and they have been suggested to be therapeutic, diagnostic and prognostic tools in cerebrovascular diseases, including stroke. This review summarizes the intricate molecular mechanisms underlying ischemic and hemorrhagic stroke and delves into the function of miRNAs in the development of brain damage. Furthermore, we will analyze new perspectives on treatment based on molecular mechanisms in addition to traditional stroke therapies.

Research advances in predicting the expansion of hypertensive intracerebral hemorrhage based on CT images: an overview

Hematoma expansion (HE) is an important risk factor for death or poor prognosis in patients with hypertensive intracerebral hemorrhage (HICH). Accurately predicting the risk of HE in patients with HICH is of great clinical significance for timely intervention and improving patient prognosis. Many imaging signs reported in literatures showed the important clinical value for predicting HE. In recent years, the development of radiomics and artificial intelligence has provided new methods for HE prediction with high accuracy. Therefore, this article reviews the latest research progress in CT imaging, radiomics, and artificial intelligence of HE, in order to help identify high-risk patients for HE in clinical practice.

Black hole sign migration in short-term brain CT scans: A possible link with clot evolution and histology

The black hole sign (BHS) is a rare radiological sign seen in the hyperacute phase of bleeding. It manifests within a hemorrhage in early hours, with limited studies exploring clot formation and evolution over a short duration. Despite various hypothesized mechanisms, the precise lifetime and dynamics of black hole sign development remain unclear. We describe the rare finding of a black hole sign within a deep brain hemorrhage, initially observed in the lateral portion of the clot during the first CT scan. Remarkably, in a subsequent CT scan, just 1 hour later, the BHS migrated towards the inner edge. Notably, while the hemorrhage size remained largely unchanged within this short timeframe, hyperacute bleeding led to increased perihematomal edema and sulci flattening. Histopathological features of the "evolving clot" are initially characterized by heightened cellularity. This increased cell density renders the hematoma less resistant to compressive forces, such as heightened endocranial pressure, offering a plausible explanation for the crushing and displacement of the BHS. Our study sheds light on the unique radiological progression of BHS within a deep brain ICH, emphasizing its association with dynamic clot formation and the consequential impact on surrounding structures.

Artificial Intelligence and Machine Learning Applications in Critically Ill Brain Injured Patients

The utilization of Artificial Intelligence (AI) and Machine Learning (ML) is paving the way for significant strides in patient diagnosis, treatment, and prognostication in neurocritical care. These technologies offer the potential to unravel complex patterns within vast datasets ranging from vast clinical data and EEG (electroencephalogram) readings to advanced cerebral imaging facilitating a more nuanced understanding of patient conditions. Despite their promise, the implementation of AI and ML faces substantial hurdles. Historical biases within training data, the challenge of interpreting multifaceted data streams, and the "black box" nature of ML algorithms present barriers to widespread clinical adoption. Moreover, ethical considerations around data privacy and the need for transparent, explainable models remain paramount to ensure trust and efficacy in clinical decision-making.This article reflects on the emergence of AI and ML as integral tools in neurocritical care, discussing their roles from the perspective of both their scientific promise and the associated challenges. We underscore the importance of extensive validation in diverse clinical settings to ensure the generalizability of ML models, particularly considering their potential to inform critical medical decisions such as withdrawal of life-sustaining therapies. Advancement in computational capabilities is essential for implementing ML in clinical settings, allowing for real-time analysis and decision support at the point of care. As AI and ML are poised to become commonplace in clinical practice, it is incumbent upon health care professionals to understand and oversee these technologies, ensuring they adhere to the highest safety standards and contribute to the realization of personalized medicine. This engagement will be pivotal in integrating AI and ML into patient care, optimizing outcomes in neurocritical care through informed and data-driven decision-making.

Uncertainty-aware deep-learning model for prediction of supratentorial hematoma expansion from admission non-contrast head computed tomography scan

Hematoma expansion (HE) is a modifiable risk factor and a potential treatment target in patients with intracerebral hemorrhage (ICH). We aimed to train and validate deep-learning models for high-confidence prediction of supratentorial ICH expansion, based on admission non-contrast head Computed Tomography (CT). Applying Monte Carlo dropout and entropy of deep-learning model predictions, we estimated the model uncertainty and identified patients at high risk of HE with high confidence. Using the receiver operating characteristics area under the curve (AUC), we compared the deep-learning model prediction performance with multivariable models based on visual markers of HE determined by expert reviewers. We randomly split a multicentric dataset of patients (4-to-1) into training/cross-validation (n = 634) versus test (n = 159) cohorts. We trained and tested separate models for prediction of ≥6 mL and ≥3 mL ICH expansion. The deep-learning models achieved an AUC = 0.81 for high-confidence prediction of HE≥6 mL and AUC = 0.80 for prediction of HE≥3 mL, which were higher than visual maker models AUC = 0.69 for HE≥6 mL (p = 0.036) and AUC = 0.68 for HE≥3 mL (p = 0.043). Our results show that fully automated deep-learning models can identify patients at risk of supratentorial ICH expansion based on admission non-contrast head CT, with high confidence, and more accurately than benchmark visual markers.

Neutrophil-to-lymphocyte ratio, white blood cell, and C-reactive protein predicts poor outcome and increased mortality in intracerebral hemorrhage patients: a meta-analysis

Objective

Inflammation participates in the pathology and progression of secondary brain injury after intracerebral hemorrhage (ICH). This meta-analysis intended to explore the prognostic role of inflammatory indexes, including neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), white blood cell (WBC), and C-reactive protein (CRP) in ICH patients.

Methods

Embase, PubMed, Web of Science, and Cochrane Library were searched until June 2023. Two outcomes, including poor outcome and mortality were extracted and measured. Odds ratio (OR) and 95% confidence interval (CI) were presented for outcome assessment.

Results

Forty-six studies with 25,928 patients were included in this meta-analysis. The high level of NLR [OR (95% CI): 1.20 (1.13-1.27), p < 0.001], WBC [OR (95% CI): 1.11 (1.02-1.21), p = 0.013], and CRP [OR (95% CI): 1.29 (1.08-1.54), p = 0.005] were related to poor outcome in ICH patients. Additionally, the high level of NLR [OR (95% CI): 1.06 (1.02-1.10), p = 0.001], WBC [OR (95% CI): 1.39 (1.16-1.66), p < 0.001], and CRP [OR (95% CI): 1.02 (1.01-1.04), p = 0.009] were correlated with increased mortality in ICH patients. Nevertheless, PLR was not associated with poor outcome [OR (95% CI): 1.00 (0.99-1.01), p = 0.749] or mortality [OR (95% CI): 1.00 (0.99-1.01), p = 0.750] in ICH patients. The total score of risk of bias assessed by Newcastle-Ottawa Scale criteria ranged from 7-9, which indicated the low risk of bias in the included studies. Publication bias was low, and stability assessed by sensitivity analysis was good.

Conclusion

This meta-analysis summarizes that the high level of NLR, WBC, and CRP estimates poor outcome and higher mortality in ICH patients.

Predictive models for occurrence of expansive intracranial hematomas and surgical evacuation outcomes in traumatic brain injury patients in Uganda: A prospective cohort study

BACKGROUND

Hematoma expansion is a common manifestation of acute intracranial hemorrhage (ICH) which is associated with poor outcomes and functional status. Objective We determined the prevalence of expansive intracranial hematomas (EIH) and assessed the predictive model for EIH occurrence and surgical evacuation outcomes in patients with traumatic brain injury (TBI) in Uganda. Methods We recruited adult patients with TBI with intracranial hematomas in a prospective cohort study. Data analysis using logistic regression to identify relevant risk factors, assess the interactions between variables, and developing a predictive model for EIH occurrence and surgical evacuation outcomes in TBI patients was performed. The predictive accuracies of these algorithms were compared using the area under the receiver operating characteristic curve (AUC). A p-values of < 0.05 at a 95% Confidence interval (CI) was considered significant. Results A total of 324 study participants with intracranial hemorrhage were followed up for 6 months after surgery. About 59.3% (192/324) had expansive intracranial hemorrhage. The study participants with expansive intracranial hemorrhage had poor quality of life at both 3 and 6-months with p < 0.010 respectively. Among the 5 machine learning algorithms, the random forest performed the best in predicting EIH in both the training cohort (AUC = 0.833) and the validation cohort (AUC = 0.734). The top five features in the random forest algorithm-based model were subdural hematoma, diffuse axonal injury, systolic and diastolic blood pressure, association between depressed fracture and subdural hematoma. Other models demonstrated good discrimination with AUC for intraoperative complication (0.675) and poor discrimination for mortality (0.366) after neurosurgical evacuation in TBI patients. Conclusion Expansive intracranial hemorrhage is common among patients with traumatic brain injury in Uganda. Early identification of patients with subdural hematoma, diffuse axonal injury, systolic and diastolic blood pressure, association between depressed fracture and subdural hematoma, were crucial in predicting EIH and intraoperative complications.

A systematic review and meta-analysis on the efficacy of glibenclamide in animal models of intracerebral hemorrhage

Intracerebral hemorrhage (ICH) is a devastating stroke with many mechanisms of injury. Edema worsens outcome and can lead to mortality after ICH. Glibenclamide (GLC), a sulfonylurea 1- transient receptor potential melastatin 4 (Sur1-Trpm4) channel blocker, has been shown to attenuate edema in ischemic stroke models, raising the possibility of benefit in ICH. This meta-analysis synthesizes current pre-clinical (rodent) literature regarding the efficacy of post-ICH GLC administration (vs. vehicle controls) on behaviour (i.e., neurological deficit, motor, and memory outcomes), edema, hematoma volume, and injury volume. Six studies (5 in rats and 1 in mice) were included in our meta-analysis (PROSPERO registration = CRD42021283614). GLC significantly improved behaviour (standardized mean difference (SMD) = -0.63, [-1.16, -0.09], n = 70-74) and reduced edema (SMD = -0.91, [-1.64, -0.18], n = 70), but did not affect hematoma volume (SMD = 0.0788, [-0.5631, 0.7207], n = 18-20), or injury volume (SMD = 0.2892, [-0.4950, 1.0734], n = 24). However, these results should be interpreted cautiously. Findings were conflicted with 2 negative and 4 positive reports, and Egger regressions indicated missing negative edema data (p = 0.0001), and possible missing negative behavioural data (p = 0.0766). Experimental quality assessed via the SYRCLE and CAMARADES checklists was concerning, as most studies demonstrated high risks of bias. Studies were generally low-powered (e.g., average n = 14.4 for behaviour), and future studies should employ sample sizes of 41 to detect our observed effect size in behaviour and 33 to detect our observed effect in edema. Overall, missing negative studies, low study quality, high risk of bias, and incomplete attention to key recommendations (e.g., investigating female, aged, and co-morbid animals) suggest that further high-powered confirmatory studies are needed before conclusive statements about GLC's efficacy in ICH can be made, and before further clinical trials are performed.

Role of artificial intelligence and machine learning in the diagnosis of cerebrovascular disease

Introduction

Cerebrovascular diseases are known to cause significant morbidity and mortality to the general population. In patients with cerebrovascular disease, prompt clinical evaluation and radiographic interpretation are both essential in optimizing clinical management and in triaging patients for critical and potentially life-saving neurosurgical interventions. With recent advancements in the domains of artificial intelligence (AI) and machine learning (ML), many AI and ML algorithms have been developed to further optimize the diagnosis and subsequent management of cerebrovascular disease. Despite such advances, further studies are needed to substantively evaluate both the diagnostic accuracy and feasibility of these techniques for their application in clinical practice. This review aims to analyze the current use of AI and MI algorithms in the diagnosis of, and clinical decision making for cerebrovascular disease, and to discuss both the feasibility and future applications of utilizing such algorithms.

Methods

We review the use of AI and ML algorithms to assist clinicians in the diagnosis and management of ischemic stroke, hemorrhagic stroke, intracranial aneurysms, and arteriovenous malformations (AVMs). After identifying the most widely used algorithms, we provide a detailed analysis of the accuracy and effectiveness of these algorithms in practice.

Results

The incorporation of AI and ML algorithms for cerebrovascular patients has demonstrated improvements in time to detection of intracranial pathologies such as intracerebral hemorrhage (ICH) and infarcts. For ischemic and hemorrhagic strokes, commercial AI software platforms such as RapidAI and Viz.AI have bene implemented into routine clinical practice at many stroke centers to expedite the detection of infarcts and ICH, respectively. Such algorithms and neural networks have also been analyzed for use in prognostication for such cerebrovascular pathologies. These include predicting outcomes for ischemic stroke patients, hematoma expansion, risk of aneurysm rupture, bleeding of AVMs, and in predicting outcomes following interventions such as risk of occlusion for various endovascular devices. Preliminary analyses have yielded promising sensitivities when AI and ML are used in concert with imaging modalities and a multidisciplinary team of health care providers.

Conclusion

The implementation of AI and ML algorithms to supplement clinical practice has conferred a high degree of accuracy, efficiency, and expedited detection in the clinical and radiographic evaluation and management of ischemic and hemorrhagic strokes, AVMs, and aneurysms. Such algorithms have been explored for further purposes of prognostication for these conditions, with promising preliminary results. Further studies should evaluate the longitudinal implementation of such techniques into hospital networks and residency programs to supplement clinical practice, and the extent to which these techniques improve patient care and clinical outcomes in the long-term.

Dual-energy computed tomography material decomposition improves prediction accuracy of hematoma expansion in traumatic intracranial hemorrhage

OBJECTIVE

The angiographic spot sign (AS) on CT angiography (CTA) is known to be useful for predicting expansion in intracranial hemorrhage, but its use is limited due to its relatively low sensitivity. Recently, dual-energy computed tomography (DECT) has been shown to be superior in distinguishing between hemorrhage and iodine. This study aimed to evaluate the diagnostic performance of hematoma expansion (HE) using DECT AS in traumatic intracranial hemorrhage.

METHODS

We recruited participants with intracranial hemorrhage confirmed via CTA for suspected traumatic cerebrovascular injuries. We evaluated AS on both conventional-like and fusion images of DECT. AS is grouped into three categories: intralesional enhancement without change, delayed enhancement (DE), and growing contrast leakage (GL). HE was evaluated by measuring hematoma size on DECT and follow-up CT. Logistic regression analysis was used to evaluate whether AS on fusion images was a significant risk factor for HE. Diagnostic accuracy was calculated, and the results from conventional-like and fusion images were compared.

RESULTS

Thirty-nine hematomas in 24 patients were included in this study. Of these, 18 hematomas in 13 patients showed expansion on follow-up CT. Among the expanders, AS and GL on fusion images were noted in 13 and 5 hematomas, respectively. In non-expanders, 10 and 1 hematoma showed AS and GL, respectively. In the logistic regression model, GL on the fusion image was a significant independent risk factor for predicting HE. However, when AS was used on conventional-like images, no factors significantly predicted HE. In the receiver operating characteristic curve analysis, the area under the curve of AS on the fusion images was 0.71, with a sensitivity and specificity of 66.7% and 76.2%, respectively.

CONCLUSIONS

GL on fusion images of DECT in traumatic intracranial hemorrhage is a significant independent radiologic risk factor for predicting HE. The AS of DECT fusion images has improved sensitivity compared to that of conventional-like images.

The Clinical Value of Neutrophil-to-Lymphocyte Ratio and Platelet-to-Lymphocyte Ratio for Predicting Hematoma Expansion and Poor Outcomes in Patients with Acute Intracerebral Hemorrhage

There is little knowledge of the effect of inflammatory markers on the prognoses of hematoma expansion (HE) in patients with intracranial hemorrhage (ICH). We evaluated the impact of neutrophil-lymphocyte ratio (NLR) and platelet-lymphocyte ratio (PLR) on HE and worse outcomes after acute ICH. This study included 520 consecutive patients with ICH from the registry database enrolled over 80 months. Patients' whole blood samples were collected upon arrival in the emergency department. Brain computed tomography scans were performed during hospitalization and repeated at 24 h and 72 h. The primary outcome measure was HE, defined as relative growth >33% or absolute growth <6 mL. A total of 520 patients were enrolled in this study. Multivariate analysis showed that NLR and PLR were associated with HE (NLR: odds ratio [OR], [95% CI] = 1.19 [1.12-1.27], p < 0.001; PLR: OR, [95% CI] = 1.01 [1.00-1.02], p = 0.04). Receiver operating characteristic curve analysis revealed that NLR and PLR could predict HE (AUC of NLR: 0.84, 95% CI [0.80-0.88], p < 0.001; AUC of PLR: 0.75 95% CI [0.70-0.80], p < 0.001). The cut-off value of NLR for predicting HE was 5.63, and that of PLR was 23.4. Higher NLR and PLR values increase HE risk in patients with ICH. NLR and PLR were reliable for predicting HE after ICH.

Radiomics Outperforms Clinical and Radiologic Signs in Predicting Spontaneous Basal Ganglia Hematoma Expansion: A Pilot Study

Prediction of the hematoma expansion (HE) of spontaneous basal ganglia hematoma (SBH) from the first non-contrast CT can result in better management, which has the potential of improving outcomes. This study has been designed to compare the performance of "Radiomics analysis," "radiology signs," and "clinical-laboratory data" for this task. We retrospectively reviewed the electronic medical records for clinical, demographic, and laboratory data in patients with SBH. CT images were reviewed for the presence of radiologic signs, including black-hole, blend, swirl, satellite, and island signs. Radiomic features from the SBH on the first brain CT were extracted, and the most predictive features were selected. Different machine learning models were developed based on clinical, laboratory, and radiology signs and selected Radiomic features to predict hematoma expansion (HE). The dataset used for this analysis included 116 patients with SBH. Among different models and different thresholds to define hematoma expansion (10%, 20%, 25%, 33%, 40%, and 50% volume enlargement thresholds), the Random Forest based on 10 selected Radiomic features achieved the best performance (for 25% hematoma enlargement) with an area under the curve (AUC) of 0.9 on the training dataset and 0.89 on the test dataset. The models based on clinical-laboratory and radiology signs had low performance (AUCs about 0.5-0.6).

Noncontrast Computed Tomography Markers Associated with Hematoma Expansion: Analysis of a Multicenter Retrospective Study

BACKGROUND

Hematoma expansion (HE) is a significant predictor of poor outcomes in patients with intracerebral hemorrhage (ICH). Non-contrast computed tomography (NCCT) markers in ICH are promising predictors of HE. We aimed to determine the association of the NCCT markers with HE by using different temporal HE definitions.

METHODS

We utilized Risa-MIS-ICH trial data (risk stratification and minimally invasive surgery in acute intracerebral hemorrhage). We defined four HE types based on the time to baseline CT (BCT) and the time to follow-up CT (FCT). Hematoma volume was measured by software with a semi-automatic edge detection tool. HE was defined as a follow-up CT hematoma volume increase of >6 mL or a 33% hematoma volume increase relative to the baseline CT. Multivariable regression analyses were used to determine the HE parameters. The prediction potential of indicators for HE was evaluated using receiver-operating characteristic analysis.

RESULTS

The study enrolled 158 patients in total. The time to baseline CT was independently associated with HE in one type (odds ratio (OR) 0.234, 95% confidence interval (CI) 0.077-0.712, p = 0.011), and the blend sign was independently associated with HE in two types (OR, 6.203-6.985, both p < 0.05). Heterogeneous density was independently associated with HE in all types (OR, 6.465-88.445, all p < 0.05) and was the optimal type for prediction, with an area under the curve of 0.674 (p = 0.004), a sensitivity of 38.9%, and specificity of 96.0%.

CONCLUSION

In specific subtypes, the time to baseline CT, blend sign, and heterogeneous density were independently associated with HE. The association between NCCT markers and HE is influenced by the temporal definition of HE. Heterogeneous density is a stable and robust predictor of HE in different subtypes of hematoma expansion.

Targeted drug delivery to the brain endothelium dominates over passive delivery via vascular leak in experimental intracerebral hemorrhage

Intracerebral hemorrhage (ICH) is one of the most common causes of fatal stroke, yet has no specific drug therapies. Many attempts at passive intravenous (IV) delivery in ICH have failed to deliver drugs to the salvageable area around the hemorrhage. The passive delivery method assumes vascular leak through the ruptured blood-brain barrier will allow drug accumulation in the brain. Here we tested this assumption using intrastriatal injection of collagenase, a well-established experimental model of ICH. Fitting with hematoma expansion in clinical ICH, we showed that collagenase-induced blood leak drops significantly by 4 h after ICH onset and is gone by 24 h. We observed passive-leak brain accumulation also declines rapidly over ∼4 h for 3 model IV therapeutics (non-targeted IgG; a protein therapeutic; PEGylated nanoparticles). We compared these passive leak results with targeted brain delivery by IV monoclonal antibodies (mAbs) that actively bind vascular endothelium (anti-VCAM, anti-PECAM, anti-ICAM). Even at early time points after ICH induction, where there is high vascular leak, brain accumulation via passive leak is dwarfed by brain accumulation of endothelial-targeted agents: At 4 h after injury, anti-PECAM mAbs accumulate at 8-fold higher levels in the brain vs. non-immune IgG; anti-VCAM nanoparticles (NPs) deliver a protein therapeutic (superoxide dismutase, SOD) at 4.5-fold higher levels than the carrier-free therapeutic at 24 h after injury. These data suggest that relying on passive vascular leak provides inefficient delivery of therapeutics even at early time points after ICH, and that a better strategy might be targeted delivery to the brain endothelium, which serves as the gateway for the immune attack on the peri-hemorrhage inflamed brain region.

Inflammatory score predicts early hematoma expansion and poor outcomes in patients with intracerebral hemorrhage

BACKGROUND

This study aimed to develop a prediction score named inflammatory score based on proper integration of several inflammatory markers and investigate whether it was associated with hematoma expansion and poor outcomes in patients with intracerebral hemorrhage (ICH).

METHODS

This study involved a consecutive series of spontaneous ICH patients of two cohorts admitted within 24 hours after symptom onset. Inflammatory score (0-9) was developed with the combination of neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, monocyte-to-lymphocyte ratio, systemic immune-inflammation index, lactate dehydrogenase, and C-reactive protein. The authors investigated the association between inflammatory score and hematoma expansion and poor outcomes by using univariate and multivariate logistic regression analyses. The optimal cutoff point of inflammatory score was determined by receiver operating characteristic analysis in the development cohort and then validated.

RESULTS

A total of 301 and 154 ICH patients were enrolled in the development and validation cohorts. Inflammatory score was significantly higher in patients with hematoma expansion and poor outcomes. The multivariate logistic regression analysis revealed inflammatory score was independently associated with hematoma expansion, secondary neurological deterioration within 48 hours, 30-day mortality, and 3-month poor modified Rankin scale (4-6). The diagnostic accuracy of inflammatory score exhibited by area under the curve showed numerically or statistically higher than most of the individual indicators. Moreover, inflammatory score greater than or equal to 5 was selected as the optimal cutoff point, which was further prospectively validated with high diagnostic accuracy.

CONCLUSIONS

The inflammatory score is a reliable predictor for early hematoma expansion and short-term and long-term poor outcomes with good diagnostic accuracies in ICH patients.

Intracerebral haemorrhage expansion: definitions, predictors, and prevention

Haematoma expansion affects a fifth of patients within 24 h of the onset of acute intracerebral haemorrhage and is associated with death and disability, which makes it an appealing therapeutic target. The time in which active intervention can be done is short as expansion occurs mostly within the first 3 h after onset. Baseline haemorrhage volume, antithrombotic treatment, and CT angiography spot signs are each associated with increased risk of haematoma expansion. Non-contrast CT features are promising predictors of haematoma expansion, but their potential contribution to current models is under investigation. Blood pressure lowering and haemostatic treatment minimise haematoma expansion but have not led to improved functional outcomes in randomised clinical trials. Ultra-early enrolment and selection of participants on the basis of non-contrast CT imaging markers could focus future clinical trials to show clinical benefit in people at high risk of expansion or investigate heterogeneity of treatment effects in clinical trials with broad inclusion criteria.

Early Antiplatelet Resumption and the Risks of Major Bleeding After Intracerebral Hemorrhage

BACKGROUND

The appropriate timing of resuming antithrombotic therapy after intracerebral hemorrhage (ICH) remains unclear. The aim of this study was to compare the risks of major bleeding between early and late antiplatelet resumption in ICH survivors.

METHODS

Between 2008 and 2017, ICH patients were available in the National Health Insurance Research Database. Patients with a medication possession ratio of antiplatelet treatment ≥50% before ICH and after antiplatelet resumption were screened. We excluded patients with atrial fibrillation, heart failure, under anticoagulant or hemodialysis treatment, and developed cerebrovascular events or died before antiplatelet resumption. Finally, 1584 eligible patients were divided into EARLY (≤30 days) and LATE groups (31-365 days after the index ICH) based on the timing of antiplatelet resumption. Patients were followed until the occurrence of a clinical outcome, end of 1-year follow-up, death, or until December 31, 2018. The primary outcome was recurrent ICH. The secondary outcomes included all-cause mortality, major hemorrhagic events, major occlusive vascular events, and ischemic stroke. Cox proportional hazard model after matching was used for comparison between the 2 groups.

RESULTS

Both the EARLY and LATE groups had a similar risk of 1-year recurrent ICH (EARLY versus LATE: 3.12% versus 3.27%; adjusted hazard ratio [AHR], 0.967 [95% CI, 0.522-1.791]) after matching. Both groups also had a similar risk of each secondary outcome at 1-year follow-up. Subgroup analyses disclosed early antiplatelet resumption in the patients without prior cerebrovascular disease were associated with lower risks of all-cause mortality (AHR, 0.199 [95% CI, 0.054-0.739]) and major hemorrhagic events (AHR, 0.090 [95% CI, 0.010-0.797]), while early antiplatelet resumption in the patients with chronic kidney disease were associated with a lower risk of ischemic stroke (AHR, 0.065 [95% CI, 0.012-0.364]).

CONCLUSIONS

Early resumption of antiplatelet was as safe as delayed antiplatelet resumption in ICH patients. Besides, those without prior cerebrovascular disease or with chronic kidney disease may benefit more from early antiplatelet resumption.

Progress on siRNA-based gene therapy targeting secondary injury after intracerebral hemorrhage

Intracerebral hemorrhage (ICH) is a life-threatening condition with a high mortality rate. For survivors, quality of life is determined by primary and secondary phases of injury. The prospects for injury repair and recovery after ICH are highly dependent on the extent of secondary injury. Currently, no effective treatments are available to prevent secondary injury or its long-term effects. One promising strategy that has recently garnered attention is gene therapy, in particular, small interfering RNAs (siRNA), which silence specific genes responsible for destructive effects after hemorrhage. Gene therapy as a potential treatment for ICH is being actively researched in animal studies. However, there are many barriers to the systemic delivery of siRNA-based therapy, as the use of naked siRNA has limitations. Recently, the Food and Drug Administration approved two siRNA-based therapies, and several are undergoing Phase 3 clinical trials. In this review, we describe the advancements in siRNA-based gene therapy for ICH and also summarize its advantages and disadvantages.

Subarachnoid extension and unfavorable outcomes in patients with supratentorial intracerebral hemorrhage

OBJECTIVE

Our study aimed to investigate the association between the subarachnoid extension of intracranial hemorrhage (SAHE) and clinical outcomes in patients with supratentorial intracerebral hemorrhage (ICH).

METHODS

We analyzed the data from a prospective, multi-center, and registry-based database. Two experienced investigators independently assessed ICH imaging data. We compared baseline characteristics and follow-up outcomes. Multivariable logistic regression analysis was used to evaluate the association between SAHE and poor clinical outcomes. We also performed Kaplan-Meier curves and Cox proportional hazards regression analyses to analyze whether SAHE was relevant to a higher mortality rate.

RESULTS

A total of 931 patients were included in this study (SAHE vs. no SAHE, 121 [13.0%] vs. 810 [87.0%]). Patients with SAHE had more severe neurological deficits, higher scores of the mRS, and more remarkable mortality rates at follow-up (all p values < 0.05). In multivariable-adjusted models, SAHE was independently associated with a higher risk of poor outcomes (adjusted OR [95%CI]: 2.030 [1.142-3.608] at 3 months; 2.348 [1.337-4.123] at 1 year). In addition, SAHE remained an independent association with an increased death rate at 1 year (adjusted HR [95%CI], 1.314[1.057-1.635]). In the subgroup analysis, the correlation between SAHE and prognosis exists in patients with lobar or deep ICH.

CONCLUSIONS

SAHE is independently associated with poor outcomes in patients with supratentorial ICH. It may provide a promising target for developing new predictive tools targeting ICH.

Efficacy of non-enhanced computer tomography-based radiomics for predicting hematoma expansion: A meta-analysis

Background

This meta-analysis aimed to assess the efficacy of radiomics using non-enhanced computed tomography (NCCT) for predicting hematoma expansion in patients with spontaneous intracerebral hemorrhage.

Methods

Throughout the inception of the project to April 11, 2022, a comprehensive search was conducted on PubMed, Embase, and Cochrane Central Register of Controlled Trials. The methodological quality of studies in this analysis was assessed by the radiomics quality scoring system (RQS). A meta-analysis of radiomic studies based on NCCT for predicting hematoma expansion in patients with intracerebral hemorrhage was performed. The efficacy of the radiomics approach and non-contrast CT markers was compared using network meta-analysis (NMA).

Results

Ten articles comprising a total of 1525 patients were quantitatively analyzed for hematoma expansion after cerebral hemorrhage using radiomics. Based on the included studies, the mean RQS was 14.4. The AUC value (95% confidence interval) of the radiomics model was 0.80 (0.76-0.83). Five articles comprising 846 patients were included in the NMA. The results synthesized according to Bayesian NMA revealed that the predictive ability of the radiomics model outperformed most of the NCCT biomarkers.

Conclusions

The NCCT-based radiomics approach has the potential to predict hematoma expansion. Compared to NCCT biomarkers, we recommend a radiomics approach. Standardization of the radiomics approach is required for further clinical implementation.

Systematic review registration

https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=324034, identifier [CRD42022324034].

Stroke: Molecular mechanisms and therapies: Update on recent developments

Stroke, a neurological disease, is one of the leading causes of death worldwide, resulting in long-term disability in most survivors. Annual stroke costs in the United States alone were estimated at $46 billion recently. Stroke pathophysiology is complex, involving multiple causal factors, among which atherosclerosis, thrombus, and embolus are prevalent. The molecular mechanisms involved in the pathophysiology are essential to understanding targeted drug development. Some common mechanisms are excitotoxicity and calcium overload, oxidative stress, and neuroinflammation. In addition, various modifiable and non-modifiable risk factors increase the chances of stroke manifolds. Once a patient encounters a stroke, complete restoration of motor ability and cognitive skills is often rare. Therefore, shaping therapeutic strategies is paramount for finding a viable therapeutic agent. Apart from tPA, an FDA-approved therapy that is applied in most stroke cases, many other therapeutic strategies have been met with limited success. Stroke therapies often involve a combination of multiple strategies to restore the patient's normal function. Certain drugs like Gamma-aminobutyric receptor agonists (GABA), Glutamate Receptor inhibitors, Sodium, and Calcium channel blockers, and fibrinogen-depleting agents have shown promise in stroke treatment. Recently, a drug, DM199, a recombinant (synthetic) form of a naturally occurring protein called human tissue kallikrein-1 (KLK1), has shown great potential in treating stroke with fewer side effects. Furthermore, DM199 has been found to overcome the limitations presented when using tPA and/or mechanical thrombectomy. Cell-based therapies like Neural Stem Cells, Hematopoietic stem cells (HSCs), and Human umbilical cord blood-derived mesenchymal stem cells (HUCB-MSCs) are also being explored as a treatment of choice for stroke. These therapeutic agents come with merits and demerits, but continuous research and efforts are being made to develop the best therapeutic strategies to minimize the damage post-stroke and restore complete neurological function in stroke patients.

3D slicer-based calculation of hematoma irregularity index for predicting hematoma expansion in intracerebral hemorrhage

BACKGROUND

Irregular hematoma is considered as a risk sign of hematoma expansion. The aim of this study was to quantify hematoma irregularity with computed tomography based on 3D Slicer.

METHODS

Patients with spontaneous intracerebral hemorrhage who underwent an initial and subsequent non-contrast computed tomography (CT) at a single medical center between January 2019 to January 2020 were retrospectively identified. The Digital Imaging and Communication in Medicine (DICOM) standard images were loaded into the 3D Slicer, and the surface area (S) and volume (V) of hematoma were calculated. The hematoma irregularity index (HII) was defined as [Formula: see text]. Logistic regression analyses and receiver operating characteristic (ROC) curve analysis were performed to assess predictive performance of HII.

RESULTS

The enrolled patients were divided into those with hematoma enlargement (n = 36) and those without the enlargement (n = 57). HII in hematoma expansion group was 130.4 (125.1-140.0), and the index in non-enlarged hematoma group was 118.6 (113.5-122.3). There was significant difference in HII between the two groups (P < 0.01). Multivariate logistic regression analysis revealed that the HII was significantly associated with hematoma expansion before (odds ratio = 1.203, 95% confidence interval [CI], 1.115-1.298; P < 0.001) and after adjustment for age, hematoma volume, Glasgow Coma Scale score (odds ratio = 1.196, 95% CI, 1.102-1.298, P < 0.001). The area under the ROC curve was 0.86 (CI, 0.78-0.93, P < 0.01), and the best cutoff of HII for predicting hematoma growth was 123.8.

CONCLUSION

As a quantitative indicator of irregular hematoma, HII can be calculated using the 3D Slicer. And the HII was independently correlated with hematoma expansion.

A non-contrast computed tomography-based radiomics nomogram for the prediction of hematoma expansion in patients with deep ganglionic intracerebral hemorrhage

Background and purpose

Hematoma expansion (HE) is a critical event following acute intracerebral hemorrhage (ICH). We aimed to construct a non-contrast computed tomography (NCCT) model combining clinical characteristics, radiological signs, and radiomics features to predict HE in patients with spontaneous ICH and to develop a nomogram to assess the risk of early HE.

Materials and methods

We retrospectively reviewed 388 patients with ICH who underwent initial NCCT within 6 h after onset and follow-up CT within 24 h after initial NCCT, between January 2015 and December 2021. Using the LASSO algorithm or stepwise logistic regression analysis, five models (clinical model, radiological model, clinical-radiological model, radiomics model, and combined model) were developed to predict HE in the training cohort (n = 235) and independently verified in the test cohort (n = 153). The Akaike information criterion (AIC) and the likelihood ratio test (LRT) were used for comparing the goodness of fit of the five models, and the AUC was used to evaluate their ability in discriminating HE. A nomogram was developed based on the model with the best performance.

Results

The combined model (AIC = 202.599, χ2 = 80.6) was the best fitting model with the lowest AIC and the highest LRT chi-square value compared to the clinical model (AIC = 232.263, χ2 = 46.940), radiological model (AIC = 227.932, χ2 = 51.270), clinical-radiological model (AIC = 212.711, χ2 = 55.490) or radiomics model (AIC = 217.647, χ2 = 57.550). In both cohorts, the nomogram derived from the combined model showed satisfactory discrimination and calibration for predicting HE (AUC = 0.900, sensitivity = 83.87%; AUC = 0.850, sensitivity = 80.10%, respectively).

Conclusion

The NCCT-based model combining clinical characteristics, radiological signs, and radiomics features could efficiently discriminate early HE, and the nomogram derived from the combined model, as a non-invasive tool, exhibited satisfactory performance in stratifying HE risks.

Postoperative Hematoma Expansion in Patients Undergoing Decompressive Hemicraniectomy for Spontaneous Intracerebral Hemorrhage

Introduction: The aim of the study was to analyze risk factors for hematoma expansion (HE) in patients undergoing decompressive hemicraniectomy (DC) in patients with elevated intracranial pressure due to spontaneous intracerebral hematoma (ICH). Methods: We retrospectively evaluated 72 patients with spontaneous ICH who underwent DC at our institution. We compared the pre- and postoperative volumes of ICH and divided the patients into two groups: first, patients with postoperative HE > 6 cm3 (group 1), and second, patients without HE (group 2). Additionally, we screened the medical history for anticoagulant and antiplatelet medication (AC/AP), bleeding-related comorbidities, age, admission Glasgow coma scale and laboratory parameters. Results: The rate of AC/AP medication was higher in group 1 versus group 2 (15/16 vs. 5/38, p < 0.00001), and patients were significantly older in group 1 versus group 2 (65.1 ± 16.2 years vs. 54.4 ± 14.3 years, p = 0.02). Furthermore, preoperative laboratory tests showed lower rates of hematocrit (34.1 ± 5.4% vs. 38.1 ± 5.1%, p = 0.01) and hemoglobin (11.5 ± 1.6 g/dL vs. 13.13 ± 1.8 g/dL, p = 0.0028) in group 1 versus group 2. In multivariate analysis, the history of AC/AP medication was the only independent predictor of HE (p < 0.0001, OR 0.015, CI 95% 0.001−0.153). Conclusion: We presented a comprehensive evaluation of risk factors for hematoma epansion by patients undergoing DC due to ICH.

Appraising the use of tranexamic acid in traumatic and non-traumatic intracranial hemorrhage: A narrative review

Recently there has been increasing interest and debate on the use of tranexamic acid (TXA), an antifibrinolytic drug, in both traumatic and non-traumatic intracranial hemorrhage. In this review we aim to discuss recent investigations looking at TXA in traumatic brain injury (TBI) and different categories of spontaneous intracranial hemorrhage. We also discuss differences between setting (hospital vs pre-hospital), dosing and timing strategies, and other logistical challenges surrounding optimal use of TXA for isolated intracranial hemorrhage. Last, we hope to provide guidance for clinicians when considering the use of TXA in a patient with traumatic or non-traumatic intracranial hemorrhage based on appraisal of the available literature as well as some potential ideas for future research in this area.

The Patterns of Morphological Change During Intracerebral Hemorrhage Expansion: A Multicenter Retrospective Cohort Study

Objectives: Hemorrhage expansion (HE) is a common and serious condition in patients with intracerebral hemorrhage (ICH). In contrast to the volume changes, little is known about the morphological changes that occur during HE. We developed a novel method to explore the patterns of morphological change and investigate the clinical significance of this change in ICH patients. Methods: The morphological changes in the hematomas of ICH patients with available paired non-contrast CT data were described in quantitative terms, including the diameters of each hematoma in three dimensions, the longitudinal axis type, the surface regularity (SR) index, the length and direction changes of the diameters, and the distance and direction of movement of the center of the hematoma. The patterns were explored by descriptive analysis and difference analysis in subgroups. We also established a prognostic nomogram model for poor outcomes in ICH patients using both morphological changes and clinical parameters. Results: A total of 1,094 eligible patients from four medical centers met the inclusion criteria. In 266 (24.3%) cases, the hematomas enlarged; the median absolute increase in volume was 14.0 [interquartile range (IQR), 17.9] mL. The initial hematomas tended to have a more irregular shape, reflected by a larger surface regularity index, than the developed hematomas. In subtentorial and deep supratentorial hematomas, the center moved in the direction of gravity. The distance of center movement and the length changes of the diameters were small, with median values of less than 4 mm. The most common longitudinal axis type was anterior-posterior (64.7%), and the axis type did not change between initial and repeat imaging in most patients (95.2%). A prognostic nomogram model including lateral expansion, a parameter of morphological change, showed good performance in predicting poor clinical outcomes in ICH patients. Conclusions: The present study provides a morphological perspective on HE using a novel automatic approach. We identified certain patterns of morphological change in HE, and we believe that some morphological change parameters could help physicians predict the prognosis of ICH patients.

The stress hyperglycemia ratio predicts early hematoma expansion and poor outcomes in patients with spontaneous intracerebral hemorrhage

BACKGROUND

Different from diabetic hyperglycemia, stress-induced hyperglycemia (SIH) can better reflect elevated blood glucose owing to intracerebral hemorrhage (ICH). However, studies about the outcome of ICH patients with SIH are still very limited.

AIMS

This study aimed to investigate whether SIH measured by stress-induced hyperglycemia ratio (SHR) was associated with hematoma expansion and poor outcomes in patients with ICH.

METHODS

A consecutive series of patients with spontaneous ICH from two clinical centers admitted within 24 h after symptom onset were enrolled for prospective analysis. SHR was defined as admission fasting blood glucose divided by estimated average glucose [1.59 × Hemoglobin A1c (%) - 2.59]. This study investigated the association between SHR and hematoma expansion, and short-term and long-term poor outcomes using univariate and multivariate logistic regression analyses.

RESULTS

A total of 313 ICH patients were enrolled in the study. SHR was markedly higher in patients with hematoma expansion and poor outcomes (p < 0.001). The multivariate logistic regression analysis demonstrated SHR independently associated with hematoma expansion (p < 0.001) and poor outcomes, including secondary neurological deterioration within 48 h, 30-day mortality, and 3-month poor modified Rankin Scale (mRS 4-6) (p < 0.001), while the blood glucose only predicted 30-day mortality. Meanwhile, the diagnostic accuracy of SHR exhibited by area under the curve in receiver operating characteristic analysis was statistically equal to or higher than the well-known predictors.

CONCLUSION

SHR is a reliable predictor for early hematoma expansion and poor outcomes in patients with ICH.

Liver fibrosis indices associated with substantial hematoma expansion in Chinese patients with primary intracerebral hemorrhage

Background

Whether liver fibrosis is associated with increased risk for substantial hematoma expansion (HE) after intracerebral hemorrhage (ICH) is still uncertain. We evaluated the association between various liver fibrosis indices and substantial HE in a Chinese population with primary ICH.

Methods

Primary ICH patients admitted to West China Hospital within 24 h of onset between January 2015 and June 2018 were consecutively enrolled. Six liver fibrosis indices were calculated, including aspartate aminotransferase (AST)-platelet ratio index (APRI), AST/alanine aminotransferase ratio-platelet ratio index (AARPRI), fibrosis-4 (FIB-4), modified fibrosis-4 (mFIB-4), fibrosis quotient (FibroQ) and Forns index. Substantial HE was defined as an increase of more than 33% or 6 mL from baseline ICH volume. The association of each fibrosis index with substantial HE was analyzed using binary logistic regression.

Results

Of 436 patients enrolled, about 85% showed largely normal results on standard hepatic assays and coagulation parameters. Substantial HE occurred in 115 (26.4%) patients. After adjustment, AARPRI (OR 1.26, 95% CI 1.00-1.57) and FIB-4 (OR 1.15, 95% CI 1.02-1.30) were independently associated with substantial HE in ICH patients within 24 h of onset, respectively. In ICH patients within 6 h of onset, each of the following indices was independently associated with substantial HE: APRI (OR 2.64, 95% CI 1.30-5,36), AARPRI (OR 1.55, 95% CI 1.09-2.21), FIB-4 (OR 1.35, 95% CI 1.08-1.68), mFIB-4 (OR 1.09, 95% CI 1.01-1.18), FibroQ (OR 1.08, 95% CI 1.00-1.16) and Forns index (OR 1.37, 95% CI 1.10-1.69).

Conclusions

Liver fibrosis indices are independently associated with higher risk of substantial HE in Chinese patients with primary ICH, which suggesting that subclinical liver fibrosis could be routinely assessed in such patients to identify those at high risk of substantial HE.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12883-021-02494-0.

From Spot Sign to Bleeding on the Spot: Classic and Original Signs of Expanding Primary Spontaneous Intracerebral Hematoma

Expansion of a primary spontaneous intracranial hemorrhage (PSICH) has become lately of increasing interest, especially after the emergence of its early predictors. However, these signs lacked sensitivity and specificity. The flood phenomenon, defined as a drastic increase in the size of a PSICH during the same magnetic resonance study, was first described in this paper based on the data of a university medical center in Lebanon. Moreover, further review of this data resulted in 205 studies with presumed diagnosis of primary spontaneous intracranial hemorrhage within the last 10 years, of which 29 exams showed typical predictors of hematoma expansion on computed tomography. The intended benefit of this observation is to draw the radiologists' attention towards minimal variations in the volume of the hematoma between the two extreme sequences of the same MRI study, in order to detect inconspicuous flood phenomena—a direct sign of hematoma expansion.

Spatial and temporal intracerebral hemorrhage patterns in Dutch-type hereditary cerebral amyloid angiopathy

Aim

To investigate whether there is a topographical and temporal pattern of index and recurrent intracerebral hemorrhages (ICH) in Dutch-type hereditary Cerebral Amyloid Angiopathy (D-CAA) to increase our understanding on CAA-related ICH development.

Methods

We included patients with DNA confirmed D-CAA or a history with ≥1 lobar ICH and ≥1 first-degree relative with D-CAA. Topographical pattern was studied by location (proportion frontal/parietal/temporal/occipital; infra/supratentorial and occurrence ratios relative to lobe volume) and volume of index and recurrent ICHs were determined on CT. Temporal pattern was examined by time between recurrent ICHs was retrieved from medical records.

Results

We included 72 patients with D-CAA (mean age at index ICH 55 years) with in total 214 ICH. The median follow-up time was 7 years (range 0.8 to 28 years). All ICH were lobar and supratentorial. The index ICH was most frequently located in the occipital lobe (34% vs. 22% in the other three lobes; with index ICH occurrence ratios relative to lobe volume of 1.9 for occipital, 1.0 for temporal, 1.2 for parietal, and 0.5 for frontal, p = 0.001). In 16/47 (34%) patients with multiple ICH, the second ICH was located in the same lobe as the index ICH. The median time-interval between subsequent ICH was #1-2 ICH 27 months, #2-3 ICH 14 months, and #3-4 ICH 7 months (p = 0.6) There was no difference in volume between index and recurrent ICHs.

Conclusions

We found that index and recurrent ICHs in D-CAA have a preference for the occipital lobe and are least frequent in the frontal lobe, which adds to the existing knowledge of histopathological studies on amyloid load in CAA. Surprisingly, there was no acceleration in time nor gradual increase of hematoma volume between subsequent ICHs.

Intracerebral Hemorrhage

PURPOSE OF REVIEW

Nontraumatic intracerebral hemorrhage (ICH) is the second most common type of stroke. This article summarizes the basic pathophysiology, classification, and management of ICH and discusses the available evidence on therapy for hematoma, hematoma expansion, and perihematomal edema.

RECENT FINDINGS

Current available data on potential therapeutic options for ICH are promising, although none of the trials have shown improvement in mortality rate. The literature available on reversal of anticoagulation and antiplatelet agents after an ICH and resumption of these medications is also increasing.

SUMMARY

ICH continues to have high morbidity and mortality. Advances in therapeutic options to target secondary brain injury from the hematoma, hematoma expansion, and perihematomal edema are increasing. Data on reversal therapy for anticoagulant-associated or antiplatelet-associated ICH and resumption of these medications are evolving.

Primary Brainstem Hemorrhage: A Review of Prognostic Factors and Surgical Management

Primary brainstem hemorrhage (PBSH) is the most fatal subtype of intracerebral hemorrhage and is invariably associated with poor prognosis. Several prognostic factors are involved, of which the two most predominant and consistent are the initial level of consciousness and hemorrhage size. Other predictors, such as age, hyperthermia, and hydrocephalus, are generally not dependable indicators for making prognoses. Scoring systems have now been developed that can predict mortality and functional outcomes in patients suffering from PBSH, which can thus guide treatment decision-making. A novel grading scale, entitled “the new primary pontine hemorrhage (PPH) score,” represents the latest approach in scoring systems. In this system, patients with a score of 2–3 points appear to benefit from surgical management, although this claim requires further verification. The four main surgical options for the treatment of PBSH are craniotomy, stereotactic hematoma puncture and drainage, endoscopic hematoma removal, and external ventricular drainage. Nevertheless, the management of PBSH still primarily involves conservative treatment methods and surgery is generally not recommended, according to current practice. However, the ongoing clinical trial, entitled Safety and Efficacy of Surgical Treatment in Severe Primary Pontine Hemorrhage Evacuation (STIPE), should provide additional evidence to support the surgical treatment of PBSH. Therefore, we advocate the update of epidemiological data and re-evaluation of PBSH treatment in a contemporary context.

Tranexamic acid in intracerebral hemorrhage: a meta-analysis

OBJECTIVE

Evidence-based medicine was used to evaluate the efficacy and safety of tranexamic acid in patients with intracerebral hemorrhage.

METHODS

Pubmed (MEDLINE), Embase, and Cochrane Library were searched from January 2001 to October 2020 for randomized controlled trials (RCTs), cohort studies, and retrospective case series .The Jadad scale and RevMan software version 5.3 were used for literature quality assessment and meta-analysis.

RESULTS

In total, 4 randomized controlled trials and 1 retrospective case series with 2808 participants were included in the meta-analysis. Compared with control intervention in intracerebral hemorrhage, tranexamic acid could significantly reduce growth of hemorrhagic mass (odds ratio (OR) =0.81; 95% confidence interval(CI)=0.68 to 0.99; p = 0.04) and Modified Rankin Scale score (MRS) at 90 days at 0-3 (OR = 1.20; 95% CI = 1.00 to 1.43; p = 0.05), mortality by day 90 (OR= 1.03; 95% CI= 0.85-1.25; p = 0.77) and major thromboembolic events (OR= 1.14; 95% CI= 0.73-1.77; p = 0.58).

CONCLUSIONS

Treatment with tranexamic acid could reduce hematoma expansion in intracerebral hemorrhage, and the treatment was safe with no increase in thromboembolic complications. But showed no notable impact on good functional outcomes and mortality.

The 'swirl sign' as a marker for haematoma expansion and outcome in intra-cranial haemorrhage: A meta-analysis

The 'swirl sign' is a CT imaging finding associated with haematoma expansion and poor prognosis. We performed a systematic review and meta-analysis to determine its prognostic value. PubMed/MEDLINE and EMBASE were searched until 16/12/2020 for related articles. Articles detailing the relationship between the swirl sign and any of haematoma expansion (HE), neurological outcome in the form of Glasgow Outcome Score (GOS) or mortality were included. A meta-analysis was performed and the pooled sensitivity, specificity, positive likelihood ratio (PLR) and negative likelihood ratio (NLR) were calculated for each of HE, GOS and mortality. 15 papers were assessed. Nine papers related to HE, for which the pooled sensitivity was 50% (95% CI 30-71), specificity was 77% (95%CI 67-85) and PLR was 2.16 (95%CI 1.89-2.42). There was significant heterogeneity (I² = 70%, Q = 26.9). Three papers related to GOS, for which the pooled sensitivity was 45% (95%CI 20-74), specificity was 78.3% (95%CI 40-95.2) and PLR was 1.77 (95%CI 1.04-2.62). Three papers related to mortality, for which the pooled sensitivity was 65% (95% CI 32-88), specificity was 75% (95%CI 42-92) and pooled PLR was 2.64 (95%CI 1.60-4.13). Our findings indicated that the swirl sign is a useful prognostic marker in the radiological evaluation of intracranial haemorrhage. However, more research is needed to assess its independence from other risk factors for haematoma expansion.