CT angiography "spot sign" predicts hematoma expansion in acute intracerebral hemorrhage

Spot sign score is associated with hematoma expansion and longer hospital stay but not functional outcomes in primary intracerebral hemorrhage survivors

PURPOSE

The computed tomography angiography (CTA) spot sign is a validated predictor of 30-day mortality in intracerebral hemorrhage (ICH). However, its role in predicting unfavorable functional outcomes remains unclear. This study explores the frequency of the spot sign and its association with functional outcomes, hematoma expansion, and length of hospital stay among survivors of ICH.

MATERIALS AND METHODS

This was a retrospective analysis of consecutive patients with primary ICH who received CTA within 24 h of admission to two medical centers between January 2007 and August 2022. Patients who died before discharge and those referred from other hospitals were excluded. Spot signs were assessed by an experienced neuroradiologist. Functional outcomes were determined by modified Rankin Scale (mRS) scores and the Barthel Index (BI).

RESULTS

In total, 98 patients were included; 14 (13.64%) had a spot sign. No significant differences were observed in the baseline characteristics between the patients with and without a spot sign. Higher spot sign scores were associated with higher odds of experiencing hematoma expansion (p = 0.013, 95% CI = 1.16-3.55), undergoing surgery (p = 0.012, 95% CI = 0.19-1.55), and having longer hospital stay (p = 0.02, 95% CI = 1.22-13.92). However, higher spot sign scores were not associated with unfavorable functional outcomes (p = 0.918 for BI, and p = 0.782 for mRS).

CONCLUSION

Spot signs are common findings among patients with ICH, and higher spot sign scores were associated with subsequent hematoma expansion and longer hospital stays but not unfavorable functional outcomes.

Hematoma expansion prediction in intracerebral hemorrhage patients by using synthesized CT images in an end-to-end deep learning framework

Spontaneous intracerebral hemorrhage (ICH) is a type of stroke less prevalent than ischemic stroke but associated with high mortality rates. Hematoma expansion (HE) is an increase in the bleeding that affects 30%-38% of hemorrhagic stroke patients. It is observed within 24 h of onset and associated with patient worsening. Clinically it is relevant to detect the patients that will develop HE from their initial computed tomography (CT) scans which could improve patient management and treatment decisions. However, this is a significant challenge due to the predictive nature of the task and its low prevalence, which hinders the availability of large datasets with the required longitudinal information. In this work, we present an end-to-end deep learning framework capable of predicting which cases will exhibit HE using only the initial basal image. We introduce a deep learning framework based on the 2D EfficientNet B0 model to predict the occurrence of HE using initial non-contrasted CT scans and their corresponding lesion annotation as priors. We used an in-house acquired dataset of 122 ICH patients, including 35 HE cases, containing longitudinal CT scans with manual lesion annotations in both basal and follow-up (obtained within 24 h after the basal scan). Experiments were conducted using a 5-fold cross-validation strategy. We addressed the limited data problem by incorporating synthetic images into the training process. To the best of our knowledge, our approach is novel in the field of HE prediction, being the first to use image synthesis to enhance results. We studied different scenarios such as training only with the original scans, using standard image augmentation techniques, and using synthetic image generation. The best performance was achieved by adding five generated versions of each image, along with standard data augmentation, during the training process. This significantly improved (p=0.0003) the performance obtained with our baseline model using directly the original CT scans from an Accuracy of 0.56 to 0.84, F1-Score of 0.53 to 0.82, Sensitivity of 0.51 to 0.77, and Specificity of 0.60 to 0.91, respectively. The proposed approach shows promising results in predicting HE, especially with the inclusion of synthetically generated images. The obtained results highlight the significance of this research direction, which has the potential to improve the clinical management of patients with hemorrhagic stroke. The code is available at: https://github.com/NIC-VICOROB/HE-prediction-SynthCT.

Leakage Sign Is a Reliable Predictor of Hematoma Expansion in Acute Epidural Hematoma

BACKGROUND

Hematoma expansion (H-Ex) in small-/medium-sized acute epidural hematoma (AEDH) cases upon emergency admission is critical. Predicting H-Ex can lead to early surgical interventions, improving outcomes, and eliminating the need to check for expansion via computed tomography (CT). This study aimed to identify the most reliable predictors of AEDH expansion.

METHODS

We retrospectively collected data from patients with pure AEDH not requiring surgical treatment upon emergency admission from 2012 to 2022. We assessed clinical and laboratory data, time from injury to the first CT, and time to follow-up CT. Factors predictive of H-Ex on the second follow-up CT, including the leakage sign (LS), were analyzed.

RESULTS

A total of 23 patients with pure AEDH without surgery at admission were included, and LS was positive in 18. Thirteen patients showed H-Ex. The H-Ex group showed a significantly higher rate of positive LS and a lower mean platelet count than the group without H-Ex. LS's predictive value for AEDH expansion showed 100% sensitivity and 50% specificity. All patients with negative LS and normal platelet counts showed no H-Ex. Analyzing the time from injury to the first CT suggested that LS (+) within 120 minutes strongly predicted H-Ex. Reconstructed three-dimensional images of the leakage point on the skull revealed multiple mottled bleeding points on the dural surface.

CONCLUSIONS

LS can predict H-Ex in patients with pure AEDH for whom emergency surgery is unnecessary at admission. The time from injury and platelet counts must also be considered.

Early seizures and risk of epilepsy and death after intracerebral haemorrhage: The MUCH Italy

INTRODUCTION

It is unclear which patients with non-traumatic (spontaneous) intracerebral haemorrhage (ICH) are at risk of developing acute symptomatic seizures (provoked seizures occurring within the first week after stroke onset; early seizures, ES) and whether ES predispose to the occurrence of remote symptomatic seizures (unprovoked seizures occurring more than 1 week after stroke; post-stroke epilepsy, PSE) and long-term mortality.

PATIENTS AND METHODS

In the setting of the Multicenter Study on Cerebral Haemorrhage in Italy (MUCH-Italy) we examined the risk of ES and whether they predict the occurrence of PSE and all-cause mortality in a cohort of patients with first-ever spontaneous ICH and no previous history of epilepsy, consecutively hospitalized in 12 Italian neurological centers from 2002 to 2014.

RESULTS

Among 2570 patients (mean age, 73.4 ± 12.5 years; males, 55.4%) 228 (8.9%) had acute ES (183 (7.1%) short seizures and 45 (1.8%) status epilepticus (SE)). Lobar location of the hematoma (OR, 1.49; 95% CI, 1.06-2.08) was independently associated with the occurrence of ES. Of the 2,037 patients who were followed-up (median follow-up time, 68.0 months (25th-75th percentile, 77.0)), 155 (7.6%) developed PSE. ES (aHR, 2.34; 95% CI, 1.42-3.85), especially when presenting as short seizures (aHR, 2.35; 95% CI, 1.38-4.00) were associated to PSE occurrence. Unlike short seizures, SE was an independent predictor of all-cause mortality (aHR, 1.50; 95% CI, 1.005-2.26).

DISCUSSION AND CONCLUSION

The long-term risk of PSE and death after an ICH vary according to ES subtype. This might have implications for the design of future clinical trials targeting post-ICH epileptic seizures.

Co-localization of NCCT hypodensity and CTA spot sign predicts substantial intracerebral hematoma expansion: The Black-&-White sign

BACKGROUND

Existing radiological markers of hematoma expansion (HE) show modest predictive accuracy. We aim to investigate a novel radiological marker that co-localizes findings from non-contrast CT (NCCT) and CT angiography (CTA) to predict HE.

METHODS

Consecutive acute intracerebral hemorrhage patients admitted at Foothills Medical Centre in Calgary, Canada, were included. The Black-&-White sign was defined as any visually identified spot sign on CTA co-localized with a hypodensity sign on the corresponding NCCT. The primary outcome was hematoma expansion (⩾6 mL or ⩾33%). Secondary outcomes included absolute (<3, 3-6, 6-12, ⩾12 mL) and relative (0%, <25%, 25%-50%, 50%-75%, or >75%) hematoma growth scales.

RESULTS

Two-hundred patients were included, with 50 (25%) experiencing HE. Forty-four (22%) showed the spot sign, 69 (34.5%) the hypodensity sign, and 14 (7%) co-localized both as the Black-&-White sign. Those with the Black-&-White sign had higher proportions of HE (100% vs 19.4%, p < 0.001), greater absolute hematoma growth (23.37 mL (IQR = 15.41-30.27) vs 0 mL (IQR = 0-2.39), p < 0.001) and relative hematoma growth (120% (IQR = 49-192) vs 0% (0-15%), p < 0.001). The Black-&-White sign had a specificity of 100% (95%CI = 97.6%-100%), a positive predictive value of 100% (95%CI = 76.8%-100%), and an overall accuracy of 82% (95%CI = 76%-87.1%). Among the 14 patients with the Black-&-White sign, 13 showed an absolute hematoma growth ⩾12 mL, and 10 experienced a HE exceeding 75% of the initial volume. The inter-rater agreement was excellent (kappa coefficient = 0.84).

CONCLUSION

The Black-&-White sign is a robust predictor of hematoma expansion occurrence and severity, yet further validation is needed to confirm these compelling findings.

Assessments of arterial and venous phase radiodensity does not improve carotid near-occlusion diagnostics

The hypothesis of this study was that evaluation of radiodensity assessment beyond a carotid stenosis in arterial and/or venous phase can be used to separate near-occlusion and conventional ≥ 50% stenosis. We prospectively included participants with ≥ 50% carotid stenosis with inclusion preference for cases with extracranial internal carotid artery (ICA) asymmetry. All participants were examined with a research biphasic computed tomography angiography (CTA) protocol (arterial and venous phase). Reference diagnosis was set by interpretation on CTA and radiodensity difference between ipsilateral and contralateral ICA (c-corrected) or vertebral (v-corrected) was compared. We included 93 participants, 62 with near-occlusion and 31 with conventional ≥ 50% stenosis. Just beyond the stenosis, median c-corrected radiodensity was - 20 Hounsfield units (HU) among near-occlusions and - 1 HU among conventional ≥ 50% stenoses (p < 0.001) in the arterial phase. For the venous phase, these findings were + 17 HU and + 3 HU (p = 0.007). Similar group differences were seen for v-correction. No parameter had good diagnostic performance, area under the curve ≤ 0.82. With specificity set at ≥ 95%, detected near-occlusions were foremost those with large side-to-side differences in distal ICA-diameter. Carotid near-occlusions can have reduced radiodensity beyond the stenosis in arterial phases and increased radiodensity in venous phases compared to a reference artery-which was not clearly seen for conventional stenoses. However, these radiodensity findings are best seen in near-occlusion cases that are not diagnostically challenging, while they work poorly as additional diagnostic aids.

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.

The clinical potential of radiomics to predict hematoma expansion in spontaneous intracerebral hemorrhage: a narrative review

Spontaneous intracerebral hemorrhage (sICH) is associated with significant morbidity and mortality, with subsequent hematoma expansion (HE) linked to worse neurologic outcomes. Accurate, real-time predictions of the risk of HE could enable tailoring management-including blood pressure control or surgery-based on individual patient risk. Although multiple radiographic markers of HE have been proposed based on standard imaging, their clinical utility remains limited by a reliance on subjective interpretation of often ambiguous findings and a poor overall predictive power. Radiomics refers to the quantitative analysis of medical images that can be combined with machine-learning algorithms to identify predictive features for a chosen clinical outcome with a granularity beyond human limitations. Emerging data have supported the potential utility of radiomics in the prediction of HE after sICH. In this review, we discuss the current clinical management of sICH, the impact of HE and standard imaging predictors, and finally, the current data and potential future role of radiomics in HE prediction and management of patients with sICH.

Systematic Evaluation of Hematoma Expansion Models in Spontaneous Intracerebral Hemorrhage: A Meta-Analysis and Meta-Regression Approach

INTRODUCTION

Accurate prediction of hematoma expansion (HE) in spontaneous intracerebral hemorrhage (sICH) is crucial for tailoring patient-specific treatments and improving outcomes. Recent advancements have yielded numerous HE risk factors and predictive models. This study aims to evaluate the characteristics and efficacy of existing HE prediction models, offering insights for performance enhancement.

METHODS

A comprehensive search was conducted in PubMed for observational studies and randomized controlled trials focusing on HE prediction, written in English. The prediction models were categorized based on their incorporated features and modeling methodology. Rigorous quality and bias assessments were performed. A meta-analysis of studies reporting C-statistics was executed to assess and compare the performance of current HE prediction models. Meta-regression was utilized to explore heterogeneity sources.

RESULTS

From 358 initial records, 22 studies were deemed eligible, encompassing traditional models, hematoma imaging feature models, and models based on artificial intelligence or radiomics. Meta-analysis of 11 studies, involving 12,087 sICH patients, revealed an aggregated C-statistic of 0.74 (95% CI: 0.69-0.78) across seven HE prediction models. Eight characteristics related to development cohorts were identified as key factors contributing to performance variability among these models.

CONCLUSION

The findings indicate that the current predictive capacity for HE risk remains suboptimal. Enhanced accuracy in HE prediction is vital for effectively targeting patient populations most likely to benefit from tailored treatment strategies.

An interpretable artificial intelligence model based on CT for prognosis of intracerebral hemorrhage: a multicenter study

OBJECTIVES

To develop and validate a novel interpretable artificial intelligence (AI) model that integrates radiomic features, deep learning features, and imaging features at multiple semantic levels to predict the prognosis of intracerebral hemorrhage (ICH) patients at 6 months post-onset.

MATERIALS AND METHODS

Retrospectively enrolled 222 patients with ICH for Non-contrast Computed Tomography (NCCT) images and clinical data, who were divided into a training cohort (n = 186, medical center 1) and an external testing cohort (n = 36, medical center 2). Following image preprocessing, the entire hematoma region was segmented by two radiologists as the volume of interest (VOI). Pyradiomics algorithm library was utilized to extract 1762 radiomics features, while a deep convolutional neural network (EfficientnetV2-L) was employed to extract 1000 deep learning features. Additionally, radiologists evaluated imaging features. Based on the three different modalities of features mentioned above, the Random Forest (RF) model was trained, resulting in three models (Radiomics Model, Radiomics-Clinical Model, and DL-Radiomics-Clinical Model). The performance and clinical utility of the models were assessed using the Area Under the Receiver Operating Characteristic Curve (AUC), calibration curve, and Decision Curve Analysis (DCA), with AUC compared using the DeLong test. Furthermore, this study employs three methods, Shapley Additive Explanations (SHAP), Grad-CAM, and Guided Grad-CAM, to conduct a multidimensional interpretability analysis of model decisions.

RESULTS

The Radiomics-Clinical Model and DL-Radiomics-Clinical Model exhibited relatively good predictive performance, with an AUC of 0.86 [95% Confidence Intervals (CI): 0.71, 0.95; P < 0.01] and 0.89 (95% CI: 0.74, 0.97; P < 0.01), respectively, in the external testing cohort.

CONCLUSION

The multimodal explainable AI model proposed in this study can accurately predict the prognosis of ICH. Interpretability methods such as SHAP, Grad-CAM, and Guided Grad-Cam partially address the interpretability limitations of AI models. Integrating multimodal imaging features can effectively improve the performance of the model.

CLINICAL RELEVANCE STATEMENT

Predicting the prognosis of patients with ICH is a key objective in emergency care. Accurate and efficient prognostic tools can effectively prevent, manage, and monitor adverse events in ICH patients, maximizing treatment outcomes.

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.

Swirl sign score system: a novel and practical tool for predicting hematoma expansion risk after spontaneous intracerebral haemorrhage

OBJECTIVE

To methodically analyse the swirl sign and construct a scoring system to predict the risk of hematoma expansion (HE) after spontaneous intracerebral haemorrhage (sICH).

METHODS

We analysed 231 of 683 sICH patients with swirl signs on baseline noncontrast CT (NCCT) images. The characteristics of the swirl sign were analysed, including the number, maximum diameter, shape, boundary, minimum CT value of the swirl sign, and the minimum distance from the swirl sign to the edge of the hematoma. In the development cohort, univariate and multivariate analyses were used to identify independent predictors of HE, and logistic regression analysis was used to construct the swirl sign score system. The swirl sign score system was verified in the validation cohort.

RESULTS

The number and the minimum CT value of the swirl sign were independent predictors of HE. The swirl sign score system was constructed (2 points for the number of swirl signs >1 and 1 point for the minimum CT value ≤41 Hounsfield units). The area under the curve of the swirl sign score system in predicting HE was 0.773 and 0.770 in the development and validation groups, respectively.

CONCLUSIONS

The swirl sign score system is an easy-to-use radiological grading scale that requires only baseline NCCT images to effectively identify subjects at high risk of HE.

ADVANCES IN KNOWLEDGE

Our newly developed semiquantitative swirl sign score system greatly improves the ability of swirl sign to predict HE.

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.

Timing of Spot Sign Appearance, Spot Sign Volume, and Leakage Rate among Phases of Multiphase CTA Predict Intracerebral Hemorrhage Growth

BACKGROUND AND PURPOSE

The presence of spot sign is associated with a high risk of hematoma growth. Our aim was to investigate the timing of the appearance, volume, and leakage rate of the spot sign for predicting hematoma growth in acute intracerebral hemorrhage using multiphase CTA.

MATERIALS AND METHODS

In this single-center retrospective study, multiphase CTA in 3 phases was performed in acute intracerebral hemorrhage (defined as intraparenchymal ± intraventricular hemorrhages). Phases of the spot sign first appearance, spot sign volumes (microliter), and leakage rates among phases (microliter/second) were measured. Associations between baseline clinical and imaging variables including spot sign volume parameters (volume and leakage rate divided by median) and hematoma growth (>6 mL) were investigated using regression models. Receiver operating characteristic analysis was used as appropriate.

RESULTS

Two hundred seventeen patients (131 men; median age, 70 years) were included. The spot sign was detected in 21.7%, 30.0%, and 29.0% in the first, second, and third phases, respectively, with median volumes of 19.7, 31.4, and 34.8 μl in these phases. Hematoma growth was seen in 44 patients (20.3%). By means of modeling, the following variables, namely the spot sign appearing in the first phase, first phase spot sign volume, spot sign appearing in the second or third phase, and spot sign positive and negative leakage rates, were associated with hematoma growth. Among patients with a spot sign, the absolute leakage rate accounting for both positive and negative leakage rates was also associated with hematoma growth (per 1-μl/s increase; OR, 1.26; 95% CI, 1.04-1.52). Other hematoma growth predictors were stroke history, baseline NIHSS score, onset-to-imaging time, and baseline hematoma volume (all P values < .05).

CONCLUSIONS

The timing of the appearance of the spot sign, volume, and leakage rate were all associated with hematoma growth. Development of automated software to generate these spot sign volumetric parameters would be an important next step to maximize the potential of temporal intracerebral hemorrhage imaging such as multiphase CTA for identifying those most at risk of hematoma growth.

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.

Predictors of early neurological deterioration in patients with intracerebral hemorrhage: a systematic review and meta-analysis

BACKGROUND

Early neurological deterioration, a common complication in patients with intracerebral hemorrhage, is associated with poor outcomes. Despite the fact that the prevalence and predictors of early neurological impairment are widely addressed, few studies have consolidated these findings. This study aimed to systematically investigate the prevalence and predictors of early neurological deterioration.

METHODS

The PubMed, Embase, Cochrane Library, CIHNAL, and Web of Science databases were systematically searched for relevant studies from the inception to December 2023. The data were extracted using a predefined worksheet. Quality assessment was conducted using the Newcastle-Ottawa Scale. Two reviewers independently performed the study selection, data extraction, and quality appraisal. The pooled effect size and 95% confidence intervals were calculated using the STATA 17.0 software package.

RESULTS

In total, 32 studies and 5,014 patients were included in this meta-analysis. The prevalence of early neurological deterioration was 23% (95% CI 21-26%, p < 0.01). The initial NIHSS score (OR = 1.24, 95% CI 1.17, 1.30, p < 0.01), hematoma volume (OR = 1.07, 95% CI 1.06, 1.09, p < 0.01), intraventricular hemorrhage (OR = 3.50, 95% CI 1.64, 7.47, p < 0.01), intraventricular extension (OR = 3.95, 95% CI 1.96, 7.99, p < 0.01), hematoma expansion (OR = 9.77, 95% CI 4.43, 17.40, p < 0.01), and computed tomographic angiography spot sign (OR = 5.77, 95% CI 1.53, 20.23, p = 0.01) were predictors of early neurological deterioration. The funnel plot and Egger's test revealed significant publication bias (p < 0.001).

CONCLUSIONS

This meta-analysis revealed a pooled prevalence of early neurological deterioration of 23% in patients with intracerebral hemorrhage. The initial NIHSS score, hematoma volume, intraventricular hemorrhage, intraventricular expansion, hematoma expansion, and spot sign enhanced the probability of early neurological deterioration. These findings provide healthcare providers with an evidence-based basis for detecting and managing early neurological deterioration in patients with intracerebral hemorrhage.

Optimal Timing for Resumption of Anticoagulation After Intracranial Hemorrhage in Patients With Mechanical Heart Valves

BACKGROUND

Anticoagulation in patients with intracranial hemorrhage (ICH) and mechanical heart valves is often held for risk of ICH expansion; however, there exists a competing risk of acute ischemic stroke (AIS). Optimal timing to resume anticoagulation remains uncertain.

METHODS AND RESULTS

We retrospectively studied patients with ICH and mechanical heart valves from 2000 to 2018. The primary outcome was a composite end point of symptomatic hematoma expansion or new ICH, AIS, and intracardiac thrombus up to 30 days post-ICH. The exposure was timing of reinitiation of anticoagulation classified as early (resumed up to 7 days after ICH), late (≥7 and up to 30 days after ICH), and never if not resumed or resumed after 30 days post-ICH. We included 184 patients with ICH and mechanical heart valves (65 anticoagulated early, 100 late, 19 not resumed by day 30 post-ICH). Twelve patients had AIS, 16 new ICH, and 6 intracardiac thromboses. The mean time from ICH to anticoagulation was 12.7 days. Composite outcomes occurred in 12 patients resumed early (18.5%), 14 resumed late (14.0%), and 4 never resumed (21.1%). There was no increased hazard of the composite outcome (hazard ratio [HR], 1.1 [95% CI, 0.2-6.0]), AIS, or worsening or new ICH among patients resumed early versus late. There was no difference in the composite among patients never resumed versus resumed. Patients who never resumed anticoagulation had significantly more severe ICH (median Glasgow Coma Scale: 10.6, 13.9, and 13.9 among those who resumed never, early, and late, respectively; P=0.0001), higher in-hospital mortality (56.5%, 0%, and 0%, respectively; P<0.0001), and an elevated 30-day AIS risk (HR, 15.9 [95% CI, 1.9-129.7], P=0.0098).

CONCLUSIONS

In this study of patients with ICH and mechanical heart valves, there was no difference in 30-day thrombotic and hemorrhagic brain-related outcomes when anticoagulation was resumed within 7 versus 7 to 30 days after ICH. Withholding anticoagulation >30 days was associated with severe baseline ICH, higher in-hospital case fatality, and elevated AIS risk.

Red cell distribution width to lymphocyte ratio could serve as a new inflammatory biomarker for predicting hematoma expansion in patients with intracerebral hemorrhage

BACKGROUND

Hematoma expansion is a critical factor associated with increased mortality and adverse outcomes in patients with intracerebral hemorrhage (ICH). Identifying and preventing hematoma expansion early on is crucial for effective therapeutic intervention. This study aimed to investigate the potential association between the Red cell distribution width to lymphocyte ratio (RDWLR) and hematoma expansion in ICH patients.

METHODS

We conducted a retrospective analysis of clinical data from 303 ICH patients treated at our department between May 2018 and May 2023. Demographic, clinical, radiological, and laboratory data, including RDWLR upon admission, were assessed. Binary logistic regression analysis was employed to determine independent associations between various variables and hematoma expansion.

RESULTS

The study included 303 ICH patients, comprising 167 (55.1%) males and 136 (44.9%) females, with a mean age of 65.25 ± 7.32 years at admission. Hematoma expansion occurred in 73 (24.1%) cases. Multivariate analysis revealed correlations between hematoma volume at baseline (OR, 2.73; 95% CI: 1.45 -4,78; P < 0.001), admission systolic blood pressure (OR, 2.98 ; 95% CI: 1.54-4.98; P < 0.001), Glasgow Coma Scale (GCS) (OR, 1.58; 95% CI: 1.25-2.46; P = 0.017), and RDWLR (OR, 1.58; 95% CI: 1.13-2.85; P = 0.022) and hematoma expansion in these patients.

CONCLUSIONS

Our findings suggest that RDWLR could serve as a new inflammatory biomarker for hematoma expansion in ICH patients. This cost-effective and readily available biomarker has the potential for early prediction of hematoma expansion in these patients.

Effects of Achieving Rapid, Intensive, and Sustained Blood Pressure Reduction in Intracerebral Hemorrhage Expansion and Functional Outcome

BACKGROUND AND OBJECTIVES

The time taken to achieve blood pressure (BP) control could be pivotal in the benefits of reducing BP in acute intracerebral hemorrhage (ICH). We aimed to assess the relationship between the rapid achievement and sustained maintenance of an intensive systolic BP (SBP) target with radiologic, clinical, and functional outcomes.

METHODS

Rapid, Intensive, and Sustained BP lowering in Acute ICH (RAINS) was a multicenter, prospective, observational cohort study of adult patients with ICH <6 hours and SBP ≥150 mm Hg at 4 Comprehensive Stroke Centers during a 4.5-year period. Patients underwent baseline and 24-hour CT scans and 24-hour noninvasive BP monitoring. BP was managed under a rapid (target achievement ≤60 minutes), intensive (target SBP <140 mm Hg), and sustained (target stability for 24 hours) BP protocol. SBP target achievement ≤60 minutes and 24-hour SBP variability were recorded. Outcomes included hematoma expansion (>6 mL or >33%) at 24 hours (primary outcome), early neurologic deterioration (END, 24-hour increase in NIH Stroke Scale score ≥4), and 90-day ordinal modified Rankin scale (mRS) score. Analyses were adjusted by age, sex, anticoagulation, onset-to-imaging time, ICH volume, and intraventricular extension.

RESULTS

We included 312 patients (mean age 70.2 ± 13.3 years, 202 [64.7%] male). Hematoma expansion occurred in 70/274 (25.6%) patients, END in 58/291 (19.9%), and the median 90-day mRS score was 4 (interquartile range, 2-5). SBP target achievement ≤60 minutes (178/312 [57.1%]) associated with a lower risk of hematoma expansion (adjusted odds ratio [aOR] 0.43, 95% confidence interval [CI] 0.23-0.77), lower END rate (aOR 0.43, 95% CI 0.23-0.80), and lower 90-day mRS scores (aOR 0.48, 95% CI 0.32-0.74). The mean 24-hour SBP variability was 21.0 ± 7.6 mm Hg. Higher 24-hour SBP variability was not related to expansion (aOR 0.99, 95% CI 0.95-1.04) but associated with higher END rate (aOR 1.15, 95% CI 1.09-1.21) and 90-day mRS scores (aOR 1.06, 95% CI 1.04-1.10).

DISCUSSION

Among patients with acute ICH, achieving an intensive SBP target within 60 minutes was associated with lower hematoma expansion risk. Rapid SBP reduction and stable sustention within 24 hours were related to improved clinical and functional outcomes. These findings warrant the design of randomized clinical trials examining the impact of effectively achieving rapid, intensive, and sustained BP control on hematoma expansion.

CLASSIFICATION OF EVIDENCE

This study provides Class III evidence that in adults with spontaneous ICH and initial SBP ≥150 mm Hg, lowering SBP to <140 mm Hg within the first hour and maintaining this for 24 hours is associated with decreased hematoma expansion.

The relationship between perihematomal edema and hematoma expansion in acute spontaneous intracerebral hemorrhage: an exploratory radiomics analysis study

Background

The relationship between early perihematomal edema (PHE) and hematoma expansion (HE) is unclear. We investigated this relationship in patients with acute spontaneous intracerebral hemorrhage (ICH), using radiomics.

Methods

In this multicenter retrospective study, we analyzed 490 patients with spontaneous ICH who underwent non-contrast computed tomography within 6 h of symptom onset, with follow-up imaging at 24 h. We performed HE and PHE image segmentation, and feature extraction and selection to identify HE-associated optimal radiomics features. We calculated radiomics scores of hematoma (Radscores_HEA) and PHE (Radscores_PHE) and constructed a combined model (Radscore_HEA_PHE). Relationships of the PHE radiomics features or Radscores_PHE with clinical variables, hematoma imaging signs, Radscores_HEA, and HE were assessed by univariate, correlation, and multivariate analyses. We compared predictive performances in the training (n = 296) and validation (n = 194) cohorts.

Results

Shape_VoxelVolume and Shape_MinorAxisLength of PHE were identified as optimal radiomics features associated with HE. Radscore_PHE (odds ratio = 1.039, p = 0.032) was an independent HE risk factor after adjusting for the ICH onset time, Glasgow Coma Scale score, baseline hematoma volume, hematoma shape, hematoma density, midline shift, and Radscore_HEA. The areas under the receiver operating characteristic curve of Radscore_PHE in the training and validation cohorts were 0.808 and 0.739, respectively. After incorporating Radscore_PHE, the integrated discrimination improvements of Radscore_HEA_PHE in the training and validation cohorts were 0.009 (p = 0.086) and -0.011 (p < 0.001), respectively.

Conclusion

Radscore_PHE, based on Shape_VoxelVolume and Shape_MinorAxisLength of PHE, independently predicts HE, while Radscore_PHE did not add significant incremental value to Radscore_HEA.

Predicting hematoma expansion in acute spontaneous intracerebral hemorrhage: integrating clinical factors with a multitask deep learning model for non-contrast head CT

PURPOSE

To predict hematoma growth in intracerebral hemorrhage patients by combining clinical findings with non-contrast CT imaging features analyzed through deep learning.

METHODS

Three models were developed to predict hematoma expansion (HE) in 572 patients. We utilized multi-task learning for both hematoma segmentation and prediction of expansion: the Image-to-HE model processed hematoma slices, extracting features and computing a normalized DL score for HE prediction. The Clinical-to-HE model utilized multivariate logistic regression on clinical variables. The Integrated-to-HE model combined image-derived and clinical data. Significant clinical variables were selected using forward selection in logistic regression. The two models incorporating clinical variables were statistically validated.

RESULTS

For hematoma detection, the diagnostic performance of the developed multi-task model was excellent (AUC, 0.99). For expansion prediction, three models were evaluated for predicting HE. The Image-to-HE model achieved an accuracy of 67.3%, sensitivity of 81.0%, specificity of 64.0%, and an AUC of 0.76. The Clinical-to-HE model registered an accuracy of 74.8%, sensitivity of 81.0%, specificity of 73.3%, and an AUC of 0.81. The Integrated-to-HE model, merging both image and clinical data, excelled with an accuracy of 81.3%, sensitivity of 76.2%, specificity of 82.6%, and an AUC of 0.83. The Integrated-to-HE model, aligning closest to the diagonal line and indicating the highest level of calibration, showcases superior performance in predicting HE outcomes among the three models.

CONCLUSION

The integration of clinical findings with non-contrast CT imaging features analyzed through deep learning showed the potential for improving the prediction of HE in acute spontaneous intracerebral hemorrhage patients.

Long-Term Risk of Arterial Thrombosis After Intracerebral Hemorrhage: MUCH-Italy

BACKGROUND

The identification of patients surviving an acute intracerebral hemorrhage who are at a long-term risk of arterial thrombosis is a poorly defined, crucial issue for clinicians.

METHODS

In the setting of the MUCH-Italy (Multicenter Study on Cerebral Haemorrhage in Italy) prospective observational cohort, we enrolled and followed up consecutive 30-day intracerebral hemorrhage survivors to assess the long-term incidence of arterial thrombotic events, to assess the impact of clinical and radiological variables on the risk of these events, and to develop a tool for estimating such a risk at the individual level. Primary end point was a composite of ischemic stroke, myocardial infarction, or other arterial thrombotic events. A point-scoring system was generated by the β-coefficients of the variables independently associated with the long-term risk of arterial thrombosis, and the predictive MUCH score was calculated as the sum of the weighted scores.

RESULTS

Overall, 1729 patients (median follow-up time, 43 months [25th to 75th percentile, 69.0]) qualified for inclusion. Arterial thrombotic events occurred in 169 (9.7%) patients. Male sex, diabetes, hypercholesterolemia, atrial fibrillation, and personal history of coronary artery disease were associated with increased long-term risk of arterial thrombosis, whereas the use of statins and antithrombotic medications after the acute intracerebral hemorrhage was associated with a reduced risk. The area under the receiver operating characteristic curve of the MUCH score predictive validity was 0.716 (95% CI, 0.56-0.81) for the 0- to 1-year score, 0.672 (95% CI, 0.58-0.73) for the 0- to 5-year score, and 0.744 (95% CI, 0.65-0.81) for the 0- to 10-year score. C statistic for the prediction of events that occur from 0 to 10 years was 0.69 (95% CI, 0.64-0.74).

CONCLUSIONS

Intracerebral hemorrhage survivors are at high long-term risk of arterial thrombosis. The MUCH score may serve as a simple tool for risk estimation.

Early blood pressure lowering therapy is associated with good functional outcome in patients with intracerebral hemorrhage

BACKGROUND

The implementation of a care bundle might improve functional outcome for patients with intracerebral hemorrhage (ICH). However, the impact of anti-hypertensive treatment on ICH outcomes remains uncertain. Our objective is to examine whether early blood pressure (BP) lowering therapy within first 12 h is associated with good outcome in ICH patients.

METHODS

We included acute ICH patients who had baseline computed tomography (CT) scans within 6 h after onset of symptoms between October 2013 and December 2021. Early BP reduction was defined as use of anti-hypertensive agents within 12 h after onset of symptom. The clinical characteristics were compared between patients who received early BP lowering therapy and those without. The associations between early BP lowering and good outcome and functional independence at 3 months were assessed by using multivariable logistic regression analyses.

RESULTS

A total of 377 patients were finally included in this study for outcome analysis. Of those, 212 patients received early BP reduction within 12 h after ICH. A total of 251 (66.6%) patients had good outcome. After adjustment for age, admission systolic BP, admission GCS score, baseline hematoma volume, hematoma expansion, and presence of intraventricular hemorrhage, early BP lowering therapy was associated with functional independence (adjusted odd ratio:1.72, 95% confidence interval:1.03-2.87; P = 0.039) and good outcome (adjusted odd ratio: 2.02, 95% confidence interval:1.08-3.76; P = 0.027).

CONCLUSIONS

In ICH patients presenting within 6 h after symptom onset, early BP reduction within first 12 h is associated with good outcome and functional independence when compared to those who do not undergo such early intervention. Implementation of quality measures to ensure early BP reduction is crucial for management of ICH.

The impact of time to evacuation on outcomes in endoscopic surgery for supratentorial spontaneous intracerebral hemorrhage: a single-center retrospective study

Supratentorial spontaneous intracerebral hemorrhage (SICH) can be treated with endoscopic surgery, but the optimal timing remains uncertain. We retrospectively analyzed data from 46 patients who underwent endoscopic surgery for supratentorial SICH. We examined the relationship between time to evacuation and functional outcome at 3 months, adjusting for prognostic factors. Surgical outcomes and complications were compared between patients with early (≤ 12 h) or late (> 12 h) evacuation. Median time to evacuation was 12 h, and the rate of unfavorable outcome (modified Rankin Scale > 3 at 3 months) was 32.6%. Longer time to evacuation was independently associated with unfavorable outcome (odds ratio per hour delay: 1.26). Late evacuation carried a 7.25-fold higher risk of unfavorable outcome compared to early evacuation. This association held across subgroups based on hematoma volume, location, and intraventricular extension (P for interaction > 0.05). Patients with late evacuation had fewer spot signs (24% vs. 4.8%, P = 0.035) and markers of hemorrhagic expansion (36% vs. 9.5%, P = 0.018), longer neurosurgical intensive care unit (NSICU) stay (3.2 vs. 1.9 days, P = 0.011) and hospital stay (15.7 vs. 11.9 days, P = 0.014), and higher 30-day mortality (28.6 vs. 4%, P = 0.036) and complication rates (57.1% vs. 28.0%, P = 0.023). This study suggests a potential association between early endoscopic evacuation of supratentorial SICH and improved functional outcomes, lower 30-day mortality and reduced complications. The need for timely intervention in managing supratentorial SICH is highlighted, yet further validation through multi-center prospective studies is essential to substantiate these findings and provide a higher level of evidence.

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.

Clinical Features, Non-Contrast CT Radiomic and Radiological Signs in Models for the Prediction of Hematoma Expansion in Intracerebral Hemorrhage

PURPOSE

Rapid identification of hematoma expansion (HE) risk at baseline is a priority in intracerebral hemorrhage (ICH) patients and may impact clinical decision making. Predictive scores using clinical features and Non-Contract Computed Tomography (NCCT)-based features exist, however, the extent to which each feature set contributes to identification is limited. This paper aims to investigate the relative value of clinical, radiological, and radiomics features in HE prediction.

METHODS

Original data was retrospectively obtained from three major prospective clinical trials ["Spot Sign" Selection of Intracerebral Hemorrhage to Guide Hemostatic Therapy (SPOTLIGHT)NCT01359202; The Spot Sign for Predicting and Treating ICH Growth Study (STOP-IT)NCT00810888] Patients baseline and follow-up scans following ICH were included. Clinical, NCCT radiological, and radiomics features were extracted, and multivariate modeling was conducted on each feature set.

RESULTS

317 patients from 38 sites met inclusion criteria. Warfarin use (p=0.001) and GCS score (p=0.046) were significant clinical predictors of HE. The best performing model for HE prediction included clinical, radiological, and radiomic features with an area under the curve (AUC) of 87.7%. NCCT radiological features improved upon clinical benchmark model AUC by 6.5% and a clinical & radiomic combination model by 6.4%. Addition of radiomics features improved goodness of fit of both clinical (p=0.012) and clinical & NCCT radiological (p=0.007) models, with marginal improvements on AUC. Inclusion of NCCT radiological signs was best for ruling out HE whereas the radiomic features were best for ruling in HE.

CONCLUSION

NCCT-based radiological and radiomics features can improve HE prediction when added to clinical features.

A reassessment of hemoglobin and hematoma expansion in intracerebral hemorrhage

BACKGROUND

In patients with spontaneous intracerebral hemorrhage (ICH), prior studies identified an increased risk of hematoma expansion (HE) in those with lower admission hemoglobin (Hgb) levels. We aimed to reproduce these findings in an independent cohort.

METHODS

We conducted a cohort study of patients admitted to a Comprehensive Stroke Center for acute ICH within 24 hours of onset. Admission laboratory and CT imaging data on ICH characteristics including HE (defined as >33% or >6 mL), and 3-month outcomes were collected. We compared laboratory data between patients with and without HE and used multivariable logistic regression to determine associations between Hgb, HE, and unfavorable 3-month outcomes (modified Rankin Scale 4-6) while adjusting for confounders including anticoagulant use, and laboratory markers of coagulopathy.

RESULTS

Among 345 patients in our cohort (mean [SD] age 72.9 [13.7], 49% male), 71 (21%) had HE. Patients with HE had similar Hgb versus those without HE (mean [SD] 13.1 [1.8] g/dl vs. 13.1 [1.9] g/dl, p=0.92). In fully adjusted multivariable models, Hgb was not associated with HE (OR per 1g/dl 1.01, 95% CI 0.86 -1.17, p = 0.94), however higher admission Hgb levels were associated with lower odds of unfavorable 3-month outcome (OR 0.83 per 1 g/dl Hgb, 95% CI 0.72-0.96, p=0.01).

CONCLUSION

We did not confirm a previously reported association between admission Hgb and HE in patients with ICH, although Hgb and HE were both associated with poor outcome. These findings suggest that the association between Hgb and poor outcome is mediated by other factors.

Venous spot sign: Indicator of rapid hematoma expansion in venous thrombosis type of submission: Case reports

BACKGROUND

The term "spot sign" was coined by Wada et al in 2007 and thought to be due to ongoing arterial bleeding in primary intraparenchymal haemorrhage (IPH).1 Spot sign has also been described in the context of intraventricular haemorrhage (IVH).2 Over the years arterial spot signs have been found to correlate with intraparenchymal hematoma expansion, worse clinical outcomes and increased risk of surgical intervention.3 We are describing a unique instance of a spot sign in venous sinus thrombosis that initially misled the clinical diagnosis.

CASE PRESENTATION

An 83-year-old woman on dual antiplatelet therapy, with a history of minor stroke, presented with sudden right-sided weakness and dysarthria. Serial CT brain imaging revealed rapidly enlarging intraparenchymal haemorrhage (IPH). Contrast enhanced CT displayed multiple spot signs typically associated with arterial bleeding pattern. Initially possibility of antithrombotic related IPH was kept, however venogram confirmed venous pathology with focal superior sagittal sinus thrombosis (SSS). Unfortunately, the patient deteriorated and eventually succumbed to the illness before the diagnosis could be made.

CONCLUSION

The case exemplifies the potential of venous sinus thrombosis to manifest as a spot sign, thereby emphasizing the need for a broader differential diagnosis. The rarity of venous spot signs may be attributed to patient-specific venous anatomy and poor collateralization in the occluded sinus territory.

Prognostic Neuroimaging Biomarkers in Acute Vascular Brain Injury and Traumatic Brain Injury

Prognostic imaging biomarkers after acute brain injury inform treatment decisions, track the progression of intracranial injury, and can be used in shared decision-making processes with families. Herein, key established biomarkers and prognostic scoring systems are surveyed in the literature, and their applications in clinical practice and clinical trials are discussed. Biomarkers in acute ischemic stroke include computed tomography (CT) hypodensity scoring, diffusion-weighted lesion volume, and core infarct size on perfusion imaging. Intracerebral hemorrhage biomarkers include hemorrhage volume, expansion, and location. Aneurysmal subarachnoid biomarkers include hemorrhage grading, presence of diffusion-restricting lesions, and acute hydrocephalus. Traumatic brain injury CT scoring systems, contusion expansion, and diffuse axonal injury grading are reviewed. Emerging biomarkers including white matter disease scoring, diffusion tensor imaging, and the automated calculation of scoring systems and volumetrics are discussed.

Blend Sign and Haemorrhage Location and Volume Predict Late Recurrence and Mortality in Intracerebral Haemorrhage Patients

BACKGROUND

Studies on risk factors for primary intracerebral haemorrhage (ICH) focus on short-term predictive values of distinct clinical parameters or computed tomography (CT) markers and disregard the others. We, therefore, studied independent predictive values of demographic, clinical, and CT markers regarding ICH expansion, late ICH recurrence, and late mortality.

METHODS

In a retrospective study of 288 patients with primary ICH, ICH localization (158 lobar, 81 deep, and 49 cerebellar), volume, blend sign, spot sign, finger-like projections, and subarachnoid haemorrhages were evaluated. ICH localization-specific differences for demographic (age, sex), clinical parameters (vascular risk factors, antiplatelet, and anticoagulation therapy), and CT markers were evaluated using logistic regression. We applied Cox proportional hazards modelling using these parameters to predict risk factors for ICH expansion, late ICH recurrence, and late mortality.

RESULTS

The blend sign in lobar ICH relates to increased risk of ICH expansion (HR2.3), late ICH recurrence (HR2.3), and mortality (HR1.6). Age, conditions requiring antiplatelet medication, deep ICH localization, volume, and blend sign represented the most important independent factors impacting overall mortality.

CONCLUSIONS

Blend sign at baseline ICH is a manifestation of underlying detrimental vascular processes that signal increased ICH expansion risk, although is also indicative of long-term risks for late recurrent ICH and late mortality.

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.

Derivation and validation of three intracerebral hemorrhage expansion scores using different CT modalities

OBJECTIVES

To derivate and validate three scores for the prediction of intracerebral hemorrhage (ICH) expansion depending on the use of non-contrast CT (NCCT), single-phase CTA, or multiphase CTA markers of hematoma expansion, and to evaluate the added value of single-phase and multiphase CTA over NCCT.

METHODS

After prospectively deriving NCCT, single-phase CTA, and multiphase CTA hematoma expansion scores in 156 patients with ICH < 6 h, we validated them in 120 different patients. Discrimination and calibration of the three scores was assessed. Primary outcome was substantial hematoma expansion > 6 mL or > 33% at 24 h.

RESULTS

The evaluation of single-phase and multiphase CTA markers gave a steadily increase in discrimination for substantial hematoma expansion over NCCT markers. The C-index (95% confidence interval) in derivation and validation cohorts was 0.69 (0.58-0.80) and 0.59 (0.46-0.72) for NCCT score, significantly lower than 0.75 ([0.64-0.87], p = 0.038) and 0.72 ([0.59-0.84], p = 0.016) for single-phase CTA score, and than 0.79 ([0.68-0.89], p = 0.033) and 0.73 ([0.62-0.85], p = 0.031) for multiphase CTA score, respectively. The three scores showed good calibration in both derivation and validation cohorts: NCCT (χ2 statistic 0.389, p = 0.533; and χ2 statistic 0.352, p = 0.553), single-phase CTA (χ2 statistic 2.052, p = 0.359; and χ2 statistic 2.230, p = 0.328), and multiphase CTA (χ2 statistic 0.559, p = 0.455; and χ2 statistic 0.020, p = 0.887) scores, respectively.

CONCLUSION

This study shows the added prognostic value of more advanced CT modalities in acute ICH evaluation. NCCT, single-phase CTA, and multiphase CTA scores may help to refine the selection of patients at risk of expansion in different decision-making scenarios.

KEY POINTS

• This study shows the added prognostic value of more advanced CT modalities in acute intracerebral hemorrhage evaluation. • The evaluation of single-phase and multiphase CTA markers provides a steadily increase in discrimination for intracerebral hemorrhage expansion over non-contrast CT markers. • Non-contrast CT, single-phase CTA, and multiphase CTA scores may help clinicians and researchers to refine the selection of patients at risk of intracerebral hemorrhage expansion in different decision-making scenarios.

Direct bilirubin: A predictor of hematoma expansion after intracerebral hemorrhage

BACKGROUND

Previous evidence demonstrated that several biomarkers involved in the pathological process of coagulation/hemostasis dysfunction, impairment of brain vascular integrity and inflammation are associated with hematoma expansion (HE) after intracerebral hemorrhage (ICH). We aimed to explore whether there were unreported laboratory biomarkers associated with HE that were readily and commonly available in clinical practice.

METHODS

We retrospectively analyzed consecutive acute ICH patients from 2012 to 2020 with admission laboratory tests and baseline and follow-up computed tomography (CT) scans. Univariate and multivariate regression analyses were used to evaluate associations between conventional laboratory indicators and HE. The results were verified in a prospective validation cohort. The relationship of candidate biomarker and 3-month outcomes was also investigated and mediation analysis was undertaken to determine causal associations among candidate biomarker, HE and outcome.

RESULTS

Of 734 ICH patients, 163 (22.2%) presented HE. Among the included laboratory indicators, higher direct bilirubin (DBil) was associated with HE (adjusted odds ratio [OR] of per 1.0 μmol/L change 1.082; 95% confidence interval [CI] 1.011-1.158). DBil >5.65 μmol/L was a predictor of HE in validation cohort. Higher DBil was also associated with poor 3-month outcomes. The mediation analysis indicated that the association of higher DBil and poor outcomes was partially mediated by HE.

CONCLUSIONS

DBil is a predictor of HE and poor 3-month outcomes after ICH. DBil's metabolic process and involvement in the pathological mechanism of HE are likely to contribute to the association between DBil and HE. Interventions targeting DBil to improve post-ICH prognosis may be meaningful and worthy of further exploration.

Using Radiomics and Convolutional Neural Networks for the Prediction of Hematoma Expansion After Intracerebral Hemorrhage

Background

Hematoma enlargement (HE) is a common complication following acute intracerebral hemorrhage (ICH) and is associated with early deterioration and unfavorable clinical outcomes. This study aimed to evaluate the predictive performance of a computed tomography (CT) based model that utilizes deep learning features in identifying HE.

Methods

A total of 408 patients were retrospectively enrolled between January 2015 and December 2020 from our institution. We designed an automatic model that could mask the hematoma area and fusion features of radiomics, clinical data, and convolutional neural network (CNN) in a hybrid model. We assessed the model's performance by using confusion matrix metrics (CM), the area under the receiver operating characteristics curve (AUC), and other statistical indicators.

Results

After automated masking, 408 patients were randomly divided into two cohorts with 204 patients in the training set and 204 patients in the validation set. The first cohort trained the CNN model, from which we then extracted radiomics, clinical data, and CNN features for the second validation cohort. After feature selection by K-highest score, a support vector machines (SVM) model classification was used to predict HE. Our hybrid model exhibited a high AUC of 0.949, and 0.95 of precision, 0.83 of recall, and 0.94 of average precision (AP). The CM found that only 5 cases were misidentified by the model.

Conclusion

The automatic hybrid model we developed is an end-to-end method and can assist in clinical decision-making, thereby facilitating personalized treatment for patients with ICH.

Research advances in imaging markers for predicting hematoma expansion in intracerebral hemorrhage: a narrative review

Introduction

Stroke is a major global health concern and is ranked as the second leading cause of death worldwide, with the third highest incidence of disability. Intracerebral hemorrhage (ICH) is a devastating form of stroke that is responsible for a significant proportion of stroke-related morbidity and mortality worldwide. Hematoma expansion (HE), which occurs in up to one-third of ICH patients, is a strong predictor of poor prognosis and can be potentially preventable if high-risk patients are identified early. In this review, we provide a comprehensive summary of previous research in this area and highlight the potential use of imaging markers for future research studies.

Recent advances

Imaging markers have been developed in recent years to aid in the early detection of HE and guide clinical decision-making. These markers have been found to be effective in predicting HE in ICH patients and include specific manifestations on Computed Tomography (CT) and CT Angiography (CTA), such as the spot sign, leakage sign, spot-tail sign, island sign, satellite sign, iodine sign, blend sign, swirl sign, black hole sign, and hypodensities. The use of imaging markers holds great promise for improving the management and outcomes of ICH patients.

Conclusion

The management of ICH presents a significant challenge, and identifying high-risk patients for HE is crucial to improving outcomes. The use of imaging markers for HE prediction can aid in the rapid identification of such patients and may serve as potential targets for anti-HE therapies in the acute phase of ICH. Therefore, further research is needed to establish the reliability and validity of these markers in identifying high-risk patients and guiding appropriate treatment decisions.

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.

Potential of Satellite Sign for Prediction of Hematoma Expansion in Small Spontaneous Hematoma within 7 Days' Follow-Up

Background Hematoma expansion (HE) is the most important modifiable predictor that can change the clinical outcome of intracerebral hemorrhage (ICH) patients. The study aimed to investigate the potential of satellite sign for prediction of HE in spontaneous ICH patients who had follow-up non-contrast computed tomography (NCCT) within 7 days after the initial CT scan.

Methods We retrospectively reviewed data and NCCT from 142 ICH patients who were treated at our hospital at Bangkok, Thailand. All included patients were treated conservatively, had baseline NCCT within 12 hours after symptom onset, and had follow-up NCCT within 168 hours after baseline NCCT. HE was initially estimated by two radiologists, and then by image analysis software. Association between satellite sign and HE was evaluated.

Results HE occurred in 45 patients (31.7%). Patients with HE had significantly higher activated partial thromboplastin time (p = 0.001) and baseline hematoma volume (p = 0.001). The prevalence of satellite sign was 43.7%, and it was significantly independently associated with HE (p = 0.021). The sensitivity, specificity, and accuracy of satellite sign for predicting HE was 57.8, 62.9, and 61.3%, respectively. From image analysis software, the cutoff of greater than 9% relative growth in hematoma volume on follow-up NCCT had the highest association with satellite sign (p = 0.024), with a sensitivity of 55%, specificity of 64.6%, and accuracy of 60.5%.

Conclusion Satellite sign, a new NCCT predictor, was found to be significantly associated with HE in Thai population. With different context of Thai population, HE was found in smaller baseline hematoma volume. Satellite sign was found more common in lobar hematoma. Further studies to validate satellite sign for predicting HE and to identify an optimal cutoff in Thai population that is correlated with clinical outcomes are warranted.

Neuroimaging biomarkers of small vessel disease in cerebral amyloid angiopathy-related intracerebral hemorrhage

AIMS

The significance of the correlation of computed tomography (CT)-based cerebral small vessel disease (SVD) markers with the clinical outcomes in patients with cerebral amyloid angiopathy (CAA)-related intracerebral hemorrhage (ICH) remains uncertain. Thus, this study aimed to explore the relationship between SVD markers and short-term outcomes of CAA-ICH.

METHODS

A total of 183 patients with CAA-ICH admitted to the Xuanwu Hospital, and Beijing Fengtai You'anmen Hospital, from 2014 to 2021 were included. The multivariate logistic regression analysis was performed to identify the correlation between SVD markers based on CT and clinical outcomes at 7-day and 90-day.

RESULTS

Of the 183 included patients, 66 (36%) were identified with severe SVD burden. The multivariate analysis showed that the total SVD burden, white matter lesion (WML) grade, and brain atrophy indicator were independent risk factors for unfavorable outcomes at 90-day. The brain atrophy indicator was independently associated with mortality at 90-day. Severe cortical atrophy was significantly associated with early neurological deterioration.

CONCLUSIONS

The neuroimaging profiles of SVD based on CT in patients with CAA-ICH might predict the short-term outcome more effectively. Further studies are required to validate these findings and identify modifiable factors for preventing CAA-ICH development.

Resumption of Antiplatelet Therapy after Major Bleeding

Major bleeding is a common threat in patients requiring antiplatelet therapy. Timing and intensity with regard to resumption of antiplatelet therapy represent a major challenge in clinical practice. Knowledge of the patient's bleeding risk, defining transient/treatable and permanent/untreatable risk factors for bleeding, and weighing these against thrombotic risk are key to successful prevention of major adverse events. Shared decision-making involving various disciplines is essential to determine the optimal strategy. The present article addresses clinically relevant questions focusing on the most life-threatening or frequently occurring bleeding events, such as intracranial hemorrhage and gastrointestinal bleeding, and discusses the evidence for antiplatelet therapy resumption using individual risk assessment in high-risk cardiovascular disease patients.

A high neutrophil-to-platelet ratio is associated with hematoma expansion in patients with spontaneous intracerebral hemorrhage: a retrospective study

BACKGROUND

Early hematoma expansion (HE) occurs in 20 to 40% of spontaneous intracerebral hemorrhage (ICH) patients and is a primary determinant of early deterioration and poor prognosis. Previous studies have shown that inflammation is a major pathological feature of ICH, and the neutrophil-to-platelet ratio (NPR) is a marker of systemic inflammation. Therefore, we aimed to assess the association between the NPR and HE in ICH patients.

METHODS

We retrospectively collected and analyzed data from ICH patients who received treatment at our institution from January 2018 to November 2019. The NPR was calculated from the admission blood test. Brain computed tomography (CT) scans were performed at admission and repeated within 24 h. Hematoma growth was defined as relative growth > 33% or absolute growth > 6 ml.

RESULTS

A total of 317 patients were enrolled in our study. Multivariate logistic regression analysis indicated that the NPR was an independent predictor of HE [odds ratio (OR) = 1.742; 95% CI: 1.508-2.012, p < 0.001]. Receiver operating characteristic (ROC) curve analysis revealed that the NPR could predict HE, with an area under the curve of 0.838 (95% CI, 0.788-0.888, p < 0.001). The best predictive cut-off of the NPR for HE was 5.47 (sensitivity, 75.3%; specificity, 77.6%).

CONCLUSIONS

A high NPR was associated with an increased risk of HE in patients with ICH.

Intracerebral Hemorrhage Segmentation on Noncontrast Computed Tomography Using a Masked Loss Function U-Net Approach

OBJECTIVE

Intracerebral hemorrhage (ICH) volume is a strong predictor of outcome in patients presenting with acute hemorrhagic stroke. It is necessary to segment the hematoma for ICH volume estimation and for computerized extraction of features, such as spot sign, texture parameters, or extravasated iodine content at dual-energy computed tomography. Manual and semiautomatic segmentation methods to delineate the hematoma are tedious, user dependent, and require trained personnel. This article presents a convolutional neural network to automatically delineate ICH from noncontrast computed tomography scans of the head.

METHODS

A model combining a U-Net architecture with a masked loss function was trained on standard noncontrast computed tomography images that were down sampled to 256 × 256 size. Data augmentation was applied to prevent overfitting, and the loss score was calculated using the soft Dice loss function. The Dice coefficient and the Hausdorff distance were computed to quantitatively evaluate the segmentation performance of the model, together with the sensitivity and specificity to determine the ICH detection accuracy.

RESULTS

The results demonstrate a median Dice coefficient of 75.9% and Hausdorff distance of 2.65 pixels in segmentation performance, with a detection sensitivity of 77.0% and specificity of 96.2%.

CONCLUSIONS

The proposed masked loss U-Net is accurate in the automatic segmentation of ICH. Future research should focus on increasing the detection sensitivity of the model and comparing its performance with other model architectures.

The comprehensive comparison of imaging sign from CT angiography and noncontrast CT for predicting intracranial hemorrhage expansion: A comparative study

Expansion of intracranial hemorrhage (ICH) is an important predictor of poor clinical outcomes. Various imaging markers on non-contrast computed tomography (NCCT) or computed tomographic angiography (CTA) have been reported as predictors of ICH expansion. We aimed to compare the associations between various CT imaging markers and ICH expansion. Patients with spontaneous ICH who underwent initial NCCT, CTA, and subsequent NCCT between January 2016 and December 2019 were retrospectively identified. ICH expansion was defined as a volume increase of > 33% or > 6 mL. We analyzed the presence of imaging markers such as the black hole sign, blend sign, island sign, or swirl sign on initial NCCT or spot sign on CTA. An alternative free-response receiver operating characteristic curve analysis was performed using a 4-point scoring system based on the consensus of the reviewers. The predictive value of each marker was assessed using univariate and multivariate logistic regression analyses. A total of 250 patients, including 60 (24.0%) with ICH expansion, qualified for the analysis. Among the patients with spontaneous ICH, 118 (47.2%) presented with a black hole sign, 52 (20.8%) with a blend sign, 93 (37.2%) with an island sign, 79 (31.6%) with a swirl sign, and 56 (22.4%) with a spot sign. In univariate logistic regression, the initial ICH volume (P = .038), initial intraventricular hemorrhage (IVH) presence (P < .001), swirl sign (P < .001), and spot sign (P < .001) were associated with ICH expansion. Multivariate analysis confirmed that the presence of initial IVH (odds ratio, 4.111; P = .002) and spot sign (odds ratio, 109.5; P < .001) were independent predictors of ICH expansion. Initial ICH volume, IVH, swirl sign, and spot sign are associated with ICH expansion. The presence of spot signs and IVH were independent predictors of ICH expansion.

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.

Computed tomography in acute intracerebral hemorrhage: neuroimaging predictors of hematoma expansion and outcome

Intracerebral hemorrhage (ICH) accounts for 10–20% of all strokes worldwide and is associated with serious outcomes, including a 30-day mortality rate of up to 40%. Neuroimaging is pivotal in diagnosing ICH as early detection and determination of underlying cause, and risk for expansion/rebleeding is essential in providing the correct treatment. Non-contrast computed tomography (NCCT) is the most used modality for detection of ICH, identification of prognostic markers and measurements of hematoma volume, all of which are of major importance to predict outcome. The strongest predictors of 30-day mortality and functional outcome for ICH patients are baseline hematoma volume and hematoma expansion. Even so, exact hematoma measurement is rare in clinical routine practice, primarily due to a lack of tools available for fast, effective, and reliable volumetric tools. In this educational review, we discuss neuroimaging findings for ICH from NCCT images, and their prognostic value, as well as the use of semi-automatic and fully automated hematoma volumetric methods and assessment of hematoma expansion in prognostic studies.

Pseudoaneurysm as a differential for the computed tomography angiography “spot sign” in atypical presentations of intracerebral hemorrhage: illustrative case

BACKGROUND

The computed tomography angiography (CTA) “spot sign” is a well-recognized radiographic marker in primary intracerebral hemorrhage (ICH). Although it has been demonstrated to represent an area of active hemorrhage or contrast extravasation, the exact pathophysiology remains unclear. Vascular mimics of the spot sign have been identified; however, those representing pseudoaneurysm and small vessel aneurysm have rarely been reported.

OBSERVATIONS

A 57-year-old female with a past medical history of hypertension and diabetes mellitus presented with 2 weeks of acute-onset, worsening headache. Computed tomography scanning showed a right interior frontal lobe intraparenchymal hemorrhage. CTA demonstrated a punctate focus of hyperattenuation within the hematoma, consistent with a spot sign, which corresponded to a distal anterior cerebral artery pseudoaneurysm on a cerebral angiogram. The patient subsequently underwent emergent resection of the pseudoaneurysm and hematoma evacuation without complications. Her postoperative course was unremarkable without acute concerns or residual symptoms at the 4-month follow-up.

LESSONS

The authors present a unique case of a distal anterior cerebral artery pseudoaneurysm presenting as a spot sign in a relatively young patient without underlying vascular disease. Given the need for emergent intervention, intracranial pseudoaneurysm is an important diagnosis to consider in the presence of a spot sign in atypical clinical presentations of primary ICH.

Long-term outcome of cerebral amyloid angiopathy-related hemorrhage

OBJECT

The long-term functional outcome of cerebral amyloid angiopathy-related hemorrhage (CAAH) patients is unclear. We sought to assess the long-term functional outcome of CAAH and determine the prognostic factors associated with unfavorable outcomes.

METHODS

We enrolled consecutive CAAH patients from 2014 to 2020 in this observational study. Baseline characteristics and clinical outcomes were presented. Multivariable logistic regression analysis was performed to identify the prognostic factors associated with long-term outcome.

RESULTS

Among the 141 CAAH patients, 76 (53.9%) achieved favorable outcomes and 28 (19.9%) of them died at 1-year follow-up. For the longer-term follow-up with a median observation time of 19.0 (interquartile range, 12.0-26.5) months, 71 (50.4%) patients obtained favorable outcomes while 33 (23.4%) died. GCS on admission (OR, 0.109; 95% CI, 0.021-0.556; p = 0.008), recurrence of ICH (OR, 2923.687; 95% CI, 6.282-1360730.14; p = 0.011), WML grade 3-4 (OR, 31.007; 95% CI, 1.041-923.573; p = 0.047), severe central atrophy (OR, 4220.303; 95% CI, 9.135-1949674.84; p = 0.008) assessed by CT was identified as independent predictors for long-term outcome.

INTERPRETATION

Nearly 50% of CAAH patients achieved favorable outcomes at long-term follow-up. GCS, recurrence of ICH, WML grade and cerebral atrophy were identified as independent prognostic factors of long-term outcome.

Intracerebral hemorrhage markers on non-contrast computed tomography as predictors of the dynamic spot sign on CT perfusion and associations with hematoma expansion and outcome

PURPOSE

To assess the association between non-contrast computed tomography (NCCT) hematoma markers and the dynamic spot sign on computed tomography perfusion (CTP), and their associations with hematoma expansion, clinical outcome, and in-hospital mortality.

METHODS

Patients who presented with intracerebral hemorrhage (ICH) to a stroke center over an 18-month period and underwent baseline NCCT and CTP, and a follow-up NCCT within 24 h after the baseline scan were included. The initial and follow-up hematoma volumes were calculated. Two raters independently assessed the baseline NCCT for hematoma markers and concurrently assessed the CTP for the dynamic spot sign. Univariate and multivariate logistic regression analyses were performed to assess the association between the hematoma markers and the dynamic spot sign, adjusting for known ICH expansion predictors.

RESULTS

Eighty-five patients were included in our study and 55 patients were suitable for expansion analysis. Heterogeneous density was the only NCCT hematoma marker to be associated with the dynamic spot sign after multivariate analysis (odds ratio, 58.61; 95% confidence interval, 9.13-376.05; P < 0.001). The dynamic spot sign was present in 22 patients (26%) and significantly predicted hematoma expansion (odds ratio, 36.6; 95% confidence interval, 2.51-534.2; P = 0.008). All patients with a spot sign had a swirl sign. A co-located hypodensity and spot sign was significantly associated with in-hospital mortality (odds ratio, 6.17; 95% confidence interval, 1.09-34.78; P = 0.039).

CONCLUSION

Heterogeneous density and swirl sign are associated with the dynamic spot sign. The dynamic spot sign is a stronger predictor than NCCT hematoma markers of significant hematoma expansion. A co-located spot sign and hypodensity predicts in-hospital mortality.