Triage of 5 Noncontrast Computed Tomography Markers and Spot Sign for Outcome Prediction After Intracerebral Hemorrhage

Neurologic prognostication in coma and disorders of consciousness

Coma and disorders of consciousness (DoC) are clinical syndromes primarily resulting from severe acute brain injury, with uncertain recovery trajectories that often necessitate prolonged supportive care. This imposes significant socioeconomic burdens on patients, caregivers, and society. Predicting recovery in comatose patients is a critical aspect of neurocritical care, and while current prognostication heavily relies on clinical assessments, such as pupillary responses and motor movements, which are far from precise, contemporary prognostication has integrated more advanced technologies like neuroimaging and electroencephalogram (EEG). Nonetheless, neurologic prognostication remains fraught with uncertainty and significant inaccuracies and is impacted by several forms of prognostication biases, including self-fulfilling prophecy bias, affective forecasting, and clinician treatment biases, among others. However, neurologic prognostication in patients with disorders of consciousness impacts life-altering decisions including continuation of treatment interventions vs withdrawal of life-sustaining therapies (WLST), which have a direct influence on survival and recovery after severe acute brain injury. In recent years, advancements in neuro-monitoring technologies, artificial intelligence (AI), and machine learning (ML) have transformed the field of prognostication. These technologies have the potential to process vast amounts of clinical data and identify reliable prognostic markers, enhancing prediction accuracy in conditions such as cardiac arrest, intracerebral hemorrhage, and traumatic brain injury (TBI). For example, AI/ML modeling has led to the identification of new states of consciousness such as covert consciousness and cognitive motor dissociation, which may have important prognostic significance after severe brain injury. This chapter reviews the evolving landscape of neurologic prognostication in coma and DoC, highlights current pitfalls and biases, and summarizes the integration of clinical examination, neuroimaging, biomarkers, and neurophysiologic tools for prognostication in specific disease states. We will further discuss the future of neurologic prognostication, focusing on the integration of AI and ML techniques to deliver more individualized and accurate prognostication, ultimately improving patient outcomes and decision-making process in neurocritical care.

Predicting Intracerebral Hemorrhage Expansion with Inflammation Indices, Non-Contrast Computed Tomography Signs and Computed Tomography Angiography Spot Sign

Aim

We aimed to investigate whether a combination of inflammatory and radiological biomarkers can improve intracerebral hemorrhage (ICH) hematoma expansion (HE) prediction.

Methods

A retrospective analysis was conducted on patients with primary supratentorial ICH within 6 h of symptom onset between September 2021 and April 2022. Predictors were explored using univariate and logistic regression analysis. We compared the discrimination of inflammatory indice-based model 1 with models 2 and 3, which included image biomarkers, using the receiver operating characteristic curve and De Long test for area under the curve comparison.

Results

A total of 205 eligible participants were included, 56 (27.3%) of whom experienced HE. The neutrophil-to-lymphocyte ratio (NLR), black hole sign, BAT score, and computed tomography angiography (CTA) spot sign were independently associated with HE in the logistic regression (P<0.05). The addition of non-contrast computed tomography (NCCT) signs did not provide significant discrimination improvement (AUC, Model 2 0.875 [95% CI, 0.822-0.929] versus Model 1. 0.811 [95% CI, 0.747-0.875], p=0.089), whereas the added value of the CTA spot sign remained statistically significant (AUC, Model 3 0.922 [95% CI, 0.878-0.966] versus Model 2; p=0.030; Model 3 versus Model 1, p=0.005).

Conclusion

The combination of inflammatory and radiological biomarkers can predict HE with a satisfactory performance.

CT-based deep learning model for predicting hospital discharge outcome in spontaneous intracerebral hemorrhage

OBJECTIVES

To predict the functional outcome of patients with intracerebral hemorrhage (ICH) using deep learning models based on computed tomography (CT) images.

METHODS

A retrospective, bi-center study of ICH patients was conducted. Firstly, a custom 3D convolutional model was built for predicting the functional outcome of ICH patients based on CT scans from randomly selected ICH patients in H training dataset collected from H hospital. Secondly, clinical data and radiological features were collected at admission and the Extreme Gradient Boosting (XGBoost) algorithm was used to establish a second model, named the XGBoost model. Finally, the Convolution model and XGBoost model were fused to build the third "Fusion model." Favorable outcome was defined as modified Rankin Scale score of 0-3 at discharge. The prognostic predictive accuracy of the three models was evaluated using an H test dataset and an external Y dataset, and compared with the performance of ICH score and ICH grading scale (ICH-GS).

RESULTS

A total of 604 patients with ICH were included in this study, of which 450 patients were in the H training dataset, 50 patients in the H test dataset, and 104 patients in the Y dataset. In the Y dataset, the areas under the curve (AUCs) of the Convolution model, XGBoost model, and Fusion model were 0.829, 0.871, and 0.905, respectively. The Fusion model prognostic performance exceeded that of ICH score and ICH-GS (p = 0.043 and p = 0.045, respectively).

CONCLUSIONS

Deep learning models have good accuracy for predicting functional outcome of patients with spontaneous intracerebral hemorrhage.

CLINICAL RELEVANCE STATEMENT

The proposed deep learning Fusion model may assist clinicians in predicting functional outcome and developing treatment strategies, thereby improving the survival and quality of life of patients with spontaneous intracerebral hemorrhage.

KEY POINTS

• Integrating clinical presentations, CT images, and radiological features to establish deep learning model for functional outcome prediction of patients with intracerebral hemorrhage. • Deep learning applied to CT images provides great help in prognosing functional outcome of intracerebral hemorrhage patients. • The developed deep learning model performs better than clinical prognostic scores in predicting functional outcome of patients with intracerebral 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.

Machine Learning for Onset Prediction of Patients with Intracerebral Hemorrhage

Objective: Intracerebral hemorrhage (ICH) has a high mortality and long-term morbidity and thus has a significant overall health–economic impact. Outcomes are especially poor if the exact onset is unknown, but reliable imaging-based methods for onset estimation have not been established. We hypothesized that onset prediction of patients with ICH using artificial intelligence (AI) may be more accurate than human readers. Material and Methods: A total of 7421 computed tomography (CT) datasets between January 2007–July 2021 from the University Hospital Basel with confirmed ICH were extracted and an ICH-segmentation algorithm as well as two classifiers (one with radiomics, one with convolutional neural networks) for onset estimation were trained. The classifiers were trained based on the gold standard of 644 datasets with a known onset of >1 and <48 h. The results of the classifiers were compared to the ratings of two radiologists. Results: Both the AI-based classifiers and the radiologists had poor discrimination of the known onsets, with a mean absolute error (MAE) of 9.77 h (95% CI (confidence interval) = 8.52–11.03) for the convolutional neural network (CNN), 9.96 h (8.68–11.32) for the radiomics model, 13.38 h (11.21–15.74) for rater 1 and 11.21 h (9.61–12.90) for rater 2, respectively. The results of the CNN and radiomics model were both not significantly different to the mean of the known onsets (p = 0.705 and p = 0.423). Conclusions: In our study, the discriminatory power of AI-based classifiers and human readers for onset estimation of patients with ICH was poor. This indicates that accurate AI-based onset estimation of patients with ICH based only on CT-data may be unlikely to change clinical decision making in the near future. Perhaps multimodal AI-based approaches could improve ICH onset prediction and should be considered in future studies.

The "SALPARE study" of spontaneous intracerebral hemorrhage: part 1

BACKGROUND

Spontaneous intracerebral hemorrhage (ICH) is a devastating type of stroke with a huge impact on patients and families. Expanded use of oral anticoagulants and ageing population might contribute to an epidemiological change. In view of these trends, we planned a study to obtain a contemporary picture and identify early prognostic factors to improve secondary prevention.

METHODS

This multicenter prospective cohort study included consecutive adult patients with non-traumatic ICH admitted to three academic Italian hospitals (Salerno, Padova, Reggio Emilia) over a 2-year period. Demographic characteristics, vascular risk profile, clinical data and main radiological characteristics were correlated to 90-day clinical outcome.

RESULTS

Out of 682 patients [mean age: 73 ± 14 years; 316 (46.3%) females] enrolled in this study, 40% died [86/180 (47.8%) in Salerno, 120/320 (37.5%) in Padova, 67/182 (36.8%) in Reggio Emilia; p < 0.05)] and 36% were severely disabled at 90 days. Several factors were associated with a higher risk of poor functional outcome such as antithrombotic drug use, hyperglycemia, previous cerebrovascular accident, low platelet count, and pontine/massive/intraventricular hemorrhage. However, at multivariate analysis only pre-ICH mRS score (OR 30.84), GCS score at presentation (OR 11.88), initial hematoma volume (OR 29.71), and NIHSS score at presentation (OR 25.89) were independent predictors of death and poor functional outcome.

CONCLUSION

Despite the heterogeneity among centers, this study on ICH has identified four simple prognostic factors that can independently predict patients outcome, stratify their risk, and guide their management.

Irregular shape as an independent predictor of prognosis in patients with primary intracerebral hemorrhage

The utility of noncontrast computed tomography markers in the prognosis of spontaneous intracerebral hemorrhage has been studied. This study aimed to investigate the predictive value of the computed tomography (CT) irregularity shape for poor functional outcomes in patients with spontaneous intracerebral hemorrhage. We retrospectively reviewed all 782 patients with intracranial hemorrhage in our stroke emergency center from January 2018 to September 2019. Laboratory examination and CT examination were performed within 24 h of admission. After three months, the patient's functional outcome was assessed using the modified Rankin Scale. Multinomial logistic regression analyses were applied to identify independent predictors of functional outcome in patients with intracerebral hemorrhage. Out of the 627 patients included in this study, those with irregular shapes on CT imaging had a higher proportion of poor outcomes and mortality 90 days after discharge (P < 0.001). Irregular shapes were found to be significant independent predictors of poor outcome and mortality on multiple logistic regression analysis. In addition, the increase in plasma D-dimer was associated with the occurrence of irregular shapes (P = 0.0387). Patients with irregular shapes showed worse functional outcomes after intracerebral hemorrhage. The elevated expression level of plasma D-dimers may be directly related to the formation of irregular shapes.

Early Deterioration and Long-Term Prognosis of Patients With Intracerebral Hemorrhage Along With Hematoma Volume More Than 20 ml: Who Needs Surgery?

Background and Purpose: The treatment of patients with intracerebral hemorrhage along with moderate hematoma and without cerebral hernia is controversial. This study aimed to explore risk factors and establish prediction models for early deterioration and poor prognosis.

Methods: We screened patients from the prospective intracerebral hemorrhage (ICH) registration database (RIS-MIS-ICH, ClinicalTrials.gov Identifier: NCT03862729). The enrolled patients had no brain hernia at admission, with a hematoma volume of more than 20 ml. All patients were initially treated by conservative methods and followed up ≥ 1 year. A decline of Glasgow Coma Scale (GCS) more than 2 or conversion to surgery within 72 h after admission was defined as early deterioration. Modified Rankin Scale (mRS) ≥ 4 at 1 year after stroke was defined as poor prognosis. The independent risk factors of early deterioration and poor prognosis were determined by univariate and multivariate regression analysis. The prediction models were established based on the weight of the independent risk factors. The accuracy and value of models were tested by the receiver operating characteristic (ROC) curve.

Results: After screening 632 patients with ICH, a total of 123 legal patients were included. According to statistical analysis, admission GCS (OR, 1.43; 95% CI, 1.18–1.74; P < 0.001) and hematoma volume (OR, 0.9; 95% CI, 0.84–0.97; P = 0.003) were the independent risk factors for early deterioration. Hematoma location (OR, 0.027; 95% CI, 0.004–0.17; P < 0.001) and hematoma volume (OR, 1.09; 95% CI, 1.03–1.15; P < 0.001) were the independent risk factors for poor prognosis, and island sign had a trend toward significance (OR, 0.5; 95% CI, 0.16-1.57; P = 0.051). The admission GCS and hematoma volume score were combined for an early deterioration prediction model with a score from 2 to 5. ROC curve showed an area under the curve (AUC) was 0.778 and cut-off point was 3.5. Combining the score of hematoma volume, island sign, and hematoma location, a long-term prognosis prediction model was established with a score from 2 to 6. ROC curve showed AUC was 0.792 and cutoff point was 4.5.

Conclusions: The novel early deterioration and long-term prognosis prediction models are simple, objective, and accurate for patients with ICH along with a hematoma volume of more than 20 ml.

A deep learning-based model for prediction of hemorrhagic transformation after stroke

Hemorrhagic transformation (HT) is one of the most serious complications after endovascular thrombectomy (EVT) in acute ischemic stroke (AIS) patients. The purpose of this study is to develop and validate deep-learning (DL) models based on multiparametric magnetic resonance imaging (MRI) to automatically predict HT in AIS patients. Multiparametric MRI and clinical data of AIS patients with EVT from two centers (data set 1 for training and testing: n = 338; data set 2 for validating: n = 54) were used in the DL models. The acute infarction area of diffusion-weighted imaging (DWI) and hypoperfusion of perfusion-weighted imaging (PWI) was labeled manually. Two forms of data sets (volume of interest [VOI] data sets and slice data sets) were analyzed, respectively. The models based on single parameter and multiparameter models were developed and validated to predict HT in AIS patients after EVT. Performance was evaluated by area under the receiver-operating characteristic curve (AUC), accuracy (ACC), sensitivity, specificity, negative predictive value, and positive predictive value. The results showed that the performance of single parameter model based on MTT (VOI data set: AUC = 0.933, ACC = 0.843; slice data set: AUC = 0.945, ACC = 0.833) and TTP (VOI data set: AUC = 0.916, ACC = 0.873; slice data set: AUC = 0.889, ACC = 0.818) were better than the other single parameter model. The multiparameter model based on DWI & MTT & TTP & Clinical (DMTC) had the best performance for predicting HT (VOI data set: AUC = 0.948, ACC = 0.892; slice data set: AUC = 0.932, ACC = 0.873). The DMTC model in the external validation set achieved similar performance with the testing set (VOI data set: AUC = 0.939, ACC = 0.884; slice data set: AUC = 0.927, ACC = 0.871) (p > 0.05). The proposed clinical, DWI, and PWI multiparameter DL model has great potential for assisting the periprocedural management in the early prediction HT of the AIS patients with EVT.

Admission computed tomography radiomic signatures outperform hematoma volume in predicting baseline clinical severity and functional outcome in the ATACH-2 trial intracerebral hemorrhage population

BACKGROUND AND PURPOSE

Radiomics provides a framework for automated extraction of high-dimensional feature sets from medical images. We aimed to determine radiomics signature correlates of admission clinical severity and medium-term outcome from intracerebral hemorrhage (ICH) lesions on baseline head computed tomography (CT).

METHODS

We used the ATACH-2 (Antihypertensive Treatment of Acute Cerebral Hemorrhage II) trial dataset. Patients included in this analysis (n = 895) were randomly allocated to discovery (n = 448) and independent validation (n = 447) cohorts. We extracted 1130 radiomics features from hematoma lesions on baseline noncontrast head CT scans and generated radiomics signatures associated with admission Glasgow Coma Scale (GCS), admission National Institutes of Health Stroke Scale (NIHSS), and 3-month modified Rankin Scale (mRS) scores. Spearman's correlation between radiomics signatures and corresponding target variables was compared with hematoma volume.

RESULTS

In the discovery cohort, radiomics signatures, compared to ICH volume, had a significantly stronger association with admission GCS (0.47 vs. 0.44, p = 0.008), admission NIHSS (0.69 vs. 0.57, p < 0.001), and 3-month mRS scores (0.44 vs. 0.32, p < 0.001). Similarly, in independent validation, radiomics signatures, compared to ICH volume, had a significantly stronger association with admission GCS (0.43 vs. 0.41, p = 0.02), NIHSS (0.64 vs. 0.56, p < 0.001), and 3-month mRS scores (0.43 vs. 0.33, p < 0.001). In multiple regression analysis adjusted for known predictors of ICH outcome, the radiomics signature was an independent predictor of 3-month mRS in both cohorts.

CONCLUSIONS

Limited by the enrollment criteria of the ATACH-2 trial, we showed that radiomics features quantifying hematoma texture, density, and shape on baseline CT can provide imaging correlates for clinical presentation and 3-month outcome. These findings couldtrigger a paradigm shift where imaging biomarkers may improve current modelsfor prognostication, risk-stratification, and treatment triage of ICH patients.

The new Hematoma Maturity Score is highly associated with poor clinical outcome in spontaneous intracerebral hemorrhage

OBJECTIVES

The purpose of this study was to analyze the new combined indicators on noncontrast computed tomography (NCCT) to predict functional outcome at discharge, compared to previously individual radiological NCCT signs.

METHODS

Patients with spontaneous intracerebral hemorrhage (ICH) who underwent baseline CT scan were retrospectively analyzed. Black hole (BH) sign, blend sign (BS), island sign (IS), swirl sign (SwS), Barras classification, any hypodensity, any irregularity, and two combined novel indicators-Combined Barras Total Score (CBTS) and Hematoma Maturity Score-were assessed independently by two radiologists blinded to clinical information. Patients were trichotomized depending on the disability or dependency at discharge according to the Modified Rankin Scale (mRS): no symptoms or no significant/mild disability (mRS 0-2); moderate or severe disability (mRS 3-5); and mortality (mRS 6).

RESULTS

One hundred fourteen patients with spontaneous ICH confirmed by NCCT were included in the analysis. Multivariable statistical analysis was adjusted for anticoagulation, hematoma volume, ventricular expansion, hypertension, blood glucose level at admission, age, and history of atrial fibrillation and demonstrated that any hypodensity (OR 4.768, p 0.006), any irregularity (OR 4.768, p 0.006), CBTS ≥ 4 (OR 3.205, p 0.025), and the new Hematoma Maturity Score (Immature) (OR 5.872, p 0.006) are independent predictors of functional outcome at discharge.

CONCLUSIONS

The new concept of the Hematoma Maturity Score was the radiological sign on NCCT with the highest impact on clinical outcome in comparison with the rest of the evaluated radiological signs.

KEY POINTS

• This is the first manuscript where density and shape characteristics of the ICH had been evaluated together and integrated in a new Hematoma Maturity Score. • The new Hematoma Maturity Score is the radiological sign on NCCT with the highest impact on clinical outcome at discharge.

Prognostication after intracerebral hemorrhage: a review

Background

Approximately half of patients with spontaneous intracerebral hemorrhage (ICH) die within 1 year. Prognostication in this context is of great importance, to guide goals of care discussions, clinical decision-making, and risk stratification. However, available prognostic scores are hardly used in clinical practice. The purpose of this review article is to identify existing outcome prediction scores for spontaneous intracerebral hemorrhage (ICH) discuss their shortcomings, and to suggest how to create and validate more useful scores.

Main text

Through a literature review this article identifies existing ICH outcome prediction models. Using the Essen-ICH-score as an example, we demonstrate a complete score validation including discrimination, calibration and net benefit calculations. Score performance is illustrated in the Erlangen UKER-ICH-cohort (NCT03183167). We identified 19 prediction scores, half of which used mortality as endpoint, the remainder used disability, typically the dichotomized modified Rankin score assessed at variable time points after the index ICH. Complete score validation by our criteria was only available for the max-ICH score. Our validation of the Essen-ICH-score regarding prediction of unfavorable outcome showed good discrimination (area under the curve 0.87), fair calibration (calibration intercept 1.0, slope 0.84), and an overall net benefit of using the score as a decision tool. We discuss methodological pitfalls of prediction scores, e.g. the withdrawal of care (WOC) bias, physiological predictor variables that are often neglected by authors of clinical scores, and incomplete score validation. Future scores need to integrate new predictor variables, patient-reported outcome measures, and reduce the WOC bias. Validation needs to be standardized and thorough. Lastly, we discuss the integration of current ICH scoring systems in clinical practice with the awareness of their shortcomings.

Conclusion

Presently available prognostic scores for ICH do not fulfill essential quality standards. Novel prognostic scores need to be developed to inform the design of research studies and improve clinical care in patients with ICH.

Supplementary Information

The online version contains supplementary material available at 10.1186/s42466-021-00120-5.

Neuroimaging of Acute Intracerebral Hemorrhage

Intracerebral hemorrhage (ICH) accounts for 10% to 20% of all strokes worldwide and is associated with high morbidity and mortality. Neuroimaging is clinically important for the rapid diagnosis of ICH and underlying etiologies, but also for identification of ICH expansion, often as-sociated with an increased risk for poor outcome. In this context, rapid assessment of early hema-toma expansion risk is both an opportunity for therapeutic intervention and a potential hazard for hematoma evacuation surgery. In this review, we provide an overview of the current literature surrounding the use of multimodal neuroimaging of ICH for etiological diagnosis, prediction of early hematoma expansion, and prognostication of neurological outcome. Specifically, we discuss standard imaging using computed tomography, the value of different vascular imaging modalities to identify underlying causes and present recent advances in magnetic resonance imaging and computed tomography perfusion.

Noncontrast Computed Tomography Markers as Predictors of Revised Hematoma Expansion in Acute Intracerebral Hemorrhage

Background Noncontrast computed tomography (NCCT) markers are the emerging predictors of hematoma expansion in intracerebral hemorrhage. However, the relationship between NCCT markers and the dynamic change of hematoma in parenchymal tissues and the ventricular system remains unclear. Methods and Results We included 314 consecutive patients with intracerebral hemorrhage admitted to our hospital from July 2011 to May 2017. The intracerebral hemorrhage volumes and intraventricular hemorrhage (IVH) volumes were measured using a semiautomated, computer-assisted technique. Revised hematoma expansion (RHE) was defined by incorporating the original definition of hematoma expansion into IVH growth. Receiver operating characteristic curve analysis was used to compare the performance of the NCCT markers in predicting the IVH growth and RHE. Of 314 patients in our study, 61 (19.4%) had IVH growth and 93 (23.9%) had RHE. After adjustment for potential confounding variables, blend sign, black hole sign, island sign, and expansion-prone hematoma could independently predict IVH growth and RHE in the multivariate logistic regression analysis. Expansion-prone hematoma had a higher predictive performance of RHE than any single marker. The diagnostic accuracy of RHE in predicting poor prognosis was significantly higher than that of hematoma expansion. Conclusions The NCCT markers are independently associated with IVH growth and RHE. Furthermore, the expansion-prone hematoma has a higher predictive accuracy for prediction of RHE and poor outcome than any single NCCT marker. These findings may assist in risk stratification of NCCT signs for predicting active bleeding.

In spontaneous intracerebral hematoma patients, prediction of the hematoma expansion risk and mortality risk using radiological and clinical markers and a newly developed scale

Objective: In patients with spontaneous intracerebral hematoma (ICH), early-stage hematoma expansion has been associated with poor prognosis in literature. This study aimed to develop predictive parameter(s) as well as a new scale to define hematoma expansion and short-term prognosis in patients with ICH.Methods: In 46 patients with ICH, Glasgow Coma Scale (GCS) scores, non-contrast CT (NCCT) markers (hematoma volume on admission and follow-up, hypodensity, intraventricular hemorrhage, blend and island sign, BAT score), and modified Rankin Scale scores were evaluated for predicting the hematoma expansion risk and mortality risk. Furthermore, a newly developed scale called the 'HEMRICH scale' was constituted using the GCS score, hematoma volumes, and some NCCT markers.Results: Roc-Curve and Logistic Regression test results revealed that GCS score, initial hematoma volume value, hypodensity, intraventricular haemorrhage, BAT score, and HEMRICH scale score could be the best markers in predicting hematoma expansion risk whereas GCS score, intraventricular haemorrhage, BAT score, hematoma expansion, and HEMRICH scale score could be the best markers in predicting mortality risk (p = 0.01). Moreover, Factor analysis and Reliability test results showed that HEMRICH scale score could predict both hematoma expansion and mortality risks validly (Kaiser-Meyer-Olkin test value = 0.729) and reliably (Cronbach's alpha = 0.564).Conclusion: It was concluded that the GCS score, intraventricular haemorrhage, and BAT score could predict both hematoma expansion risk and mortality risk in the early stage in patients with ICH. Furthermore, it was suggested that the newly produced HEMRICH scale could be a valid and reliable scale for predicting both hematoma expansion and mortality risk.

Association of Serum Dystroglycan, MMP-2/9 and AQP-4 with Haematoma Expansion in Patients with Intracerebral Haemorrhage

OBJECTIVE

The purpose of this study was to explore association of serum dystroglycan (DG), matrix metalloproteinase-2/matrix metalloproteinase-9 (MMP-2/9), and aquaporin-4 (AQP-4) expression and haematoma expansion in patients with intracerebral haemorrhage (ICH), which are proteins involved in maintaining the integrity of the blood-brain barrier.

METHODS

We included patients older than 18 years old with ICH who had undergone baseline CT within 6 hours after intracerebral haemorrhage symptom onset in our hospital between April 2018 and December 2018. Two readers independently assessed haematoma volume and other imaging information upon admission and again within 24 hours. All patients underwent 5 mL of venous blood collection 6 and 24 hours after admission. Serum expression levels of dystroglycan, matrix metalloproteinase-2/matrix metalloproteinase-9 and aquaporin-4 were determined by quantitative enzyme-linked immunosorbent assay (ELISA). Repeated analysis of variance was used to determine whether expression of the four proteins in patients with cerebral haemorrhage changed within 24 hours and whether there were differences between the haematoma enlargement and non-haematoma enlargement groups over time. Univariate and multivariate logistic regression analyses were used to compare the correlation among expression of the four proteins, clinical characteristics of patients and haematoma enlargement.

RESULTS

Expression levels of serum matrix metalloproteinase-2/matrix metalloproteinases-9 and aquaporin-4 gradually increased within 24 hours in patients with cerebral haemorrhage (P<0.001), while expression levels of dystroglycan gradually decreased (P<0.01). Expression of serum matrix metalloproteinases-9 6 hours after onset was independently correlated with the expansion of cerebral haemorrhage. The ROC curve (AUC=0.778, 95% Cl: 0.661-0.894, P<0.001) exhibited high sensitivity (0.900) and low specificity (0.642).

CONCLUSION

These data support that expression of MMP-9 in peripheral blood is independently correlated with the enlargement of haematoma in patients with intracerebral haemorrhage 6 hours after onset and can be used as an independent predictor of haematoma enlargement in patients with intracerebral haemorrhage. However, although the expression of MMP-2, AQP-4 and DG exhibited some changes within 6 and 24 hours after onset, they were not independently correlated with early haematoma enlargement in patients with intracerebral haemorrhage. Further multi-time point exploration and expansion of the sample size is necessary in future studies.

Neuroimaging in Acute Stroke

PURPOSE OF REVIEW

This article describes how imaging can be used by physicians in diagnosing, determining prognosis, and making appropriate treatment decisions in a timely manner in patients with acute stroke.

RECENT FINDINGS

Advances in acute stroke treatment, including the use of endovascular thrombectomy in patients with large vessel occlusion and, more recently, of IV thrombolysis in an extended time window, have resulted in a paradigm shift in how imaging is used in patients with acute stroke. This paradigm shift, combined with the understanding that "time is brain," means that imaging must be fast, reliable, and available around the clock for physicians to make appropriate clinical decisions. CT has therefore become the primary imaging modality of choice. Recognition of a large vessel occlusion using CT angiography has become essential in identifying patients for endovascular thrombectomy, and techniques such as imaging collaterals on CT angiography or measuring blood flow to predict tissue fate using CT perfusion have become useful tools in selecting patients for acute stroke therapy. Understanding the use of these imaging modalities and techniques in dealing with an emergency such as acute stroke has therefore become more important than ever for physicians treating patients with acute stroke.

SUMMARY

Imaging the brain and the blood vessels supplying it using modern tools and techniques is a key step in understanding the pathophysiology of acute stroke and making appropriate and timely clinical decisions.

Hematoma Ventricle Distance on Computed Tomography Predicts Poor Outcome in Intracerebral Hemorrhage

OBJECTIVE

To investigate the relationship between hematoma ventricle distance (HVD) and clinical outcome in patients with intracerebral hemorrhage (ICH).

METHODS

We prospectively enrolled consecutive patients with ICH in a tertiary academic hospital between July 2011 and April 2018. We retrospectively reviewed images for all patients receiving a computed tomography (CT) within 6 h after onset of symptoms and at least one follow-up CT scan within 36 h. The minimum distance of hematoma border to nearest ventricle was measured as HVD. Youden index was used to evaluate the cutoff of HVD predicting functional outcome. Logistic regression model was used to assess the HVD data and clinical poor outcome (modified Rankin Scale 4-6) at 90 days.

RESULTS

A total of 325 patients were included in our final analysis. The median HVD was 2.4 mm (interquartile range, 0-5.7 mm), and 119 (36.6%) patients had poor functional outcome at 3 months. After adjusting for age, admission Glasgow coma scale, intraventricular hemorrhage, baseline ICH volume, admission systolic blood pressure, blood glucose, hematoma expansion, withdrawal of care, and hypertension, HVD ≤ 2.5 mm was associated with increased odds of clinical poor outcome [odd ratio, 3.59, (95%CI = 1.72-7.50); p = 0.001] in multivariable logistic regression analysis.

CONCLUSION

Hematoma ventricle distance allows physicians to quickly select and stratify patients in clinical trials and thereby serve as a novel and useful addition to predict ICH prognosis.

Prediction of Poor Outcome in Intracerebral Hemorrhage Based on Computed Tomography Markers

INTRODUCTION

Intracerebral hemorrhage (ICH) is the most fatal type of stroke worldwide. Herein, we aim to develop a predictive model based on computed tomography (CT) markers in an ICH cohort and validate it in another cohort.

METHODS

This retrospective observational cohort study was conducted in 3 medical centers in China. The values of CT markers, including hypodensities, hematoma density, blend sign, black hole sign, island sign, midline shift, baseline hematoma volume, and satellite sign, in predicting poor outcome were analyzed by logistic regression analysis. A nomogram was developed based on the results of multivariate logistic regression analysis in development cohort. Area under curve (AUC) and calibration plot were used to assess the accuracy of nomogram in this development cohort and validate in another cohort.

RESULTS

A total of 1,498 patients were included in this study. Multivariate logistic regression analysis indicated that hypodensities, black hole sign, island sign, midline shift, and baseline hematoma volume were independently associated with poor outcome in development cohort. The AUC was 0.75 (95% confidence interval [CI]: 0.73-0.76) in the internal validation with development cohort and 0.74 (95% CI: 0.72-0.75) in the external validation with validation cohort. The calibration plot in development and validation cohort indicated that the nomogram was well calibrated.

CONCLUSIONS

CT markers of hypodensities, black hole sign, and island sign might predict poor outcome of ICH patients within 90 days.

Noncontrast CT markers of intracerebral hemorrhage expansion and poor outcome: A meta-analysis

OBJECTIVE

To provide precise estimates of the association between noncontrast CT (NCCT) markers, hematoma expansion (HE), and functional outcome in patients presenting with intracerebral hemorrhage (ICH) through a systematic review and meta-analysis.

METHODS

We searched PubMed for English-written observational studies or randomized controlled trials reporting data on NCCT markers of HE and outcome in spontaneous ICH including at least 50 subjects. The outcomes of interest were HE (hematoma growth >33%, >33% and/or >6 mL, >33% and/or >12.5 mL), poor functional outcome (modified Rankin Scale 3-6 or 4-6) at discharge or at 90 days, and mortality. We pooled data in random-effects models and extracted cumulative odds ratio (OR) for each NCCT marker.

RESULTS

We included 25 eligible studies (n = 10,650). The following markers were associated with increased risk of HE and poor outcome, respectively: black hole sign (OR = 3.70, 95% confidence interval [CI] = 1.42-9.64 and OR = 5.26, 95% CI = 1.75-15.76), swirl sign (OR = 3.33, 95% CI = 2.42-4.60 and OR = 3.70; 95% CI = 2.47-5.55), heterogeneous density (OR = 2.74; 95% CI = 1.71-4.39 and OR = 2.80; 95% CI = 1.78-4.39), blend sign (OR = 3.49; 95% CI = 2.20-5.55 and OR = 2.21; 95% CI 1.16-4.18), hypodensities (OR = 3.47; 95% CI = 2.18-5.50 and OR = 2.94; 95% CI = 2.28-3.78), irregular shape (OR = 2.01, 95% CI = 1.27-3.19 and OR = 3.43; 95% CI = 2.33-5.03), and island sign (OR = 7.87, 95% CI = 2.17-28.47 and OR = 6.05, 95% CI = 4.44-8.24).

CONCLUSION

Our results suggest that multiple NCCT ICH shape and density features, with different effect size, are important markers for HE and clinical outcome and may provide useful information for future randomized controlled trials.

A Nomogram Model of Radiomics and Satellite Sign Number as Imaging Predictor for Intracranial Hematoma Expansion

Background

We aimed to construct and validate a nomogram model based on the combination of radiomic features and satellite sign number for predicting intracerebral hematoma expansion.

Methods

A total of 129 patients from two institutions were enrolled in this study. The preprocessed initial CT images were used for radiomic feature extraction. The ANOVA-Kruskal–Wallis test and least absolute shrinkage and selection operator regression were applied to identify candidate radiomic features and construct the Radscore. A nomogram model was developed by integrating the Radscore with a satellite sign number. The discrimination performance of the proposed model was evaluated by receiver operating characteristic (ROC) analysis, and the predictive accuracy was assessed via a calibration curve. Decision curve analysis (DCA) and Kaplan–Meier (KM) survival analysis were performed to evaluate the clinical value of the model.

Results

Four optimal features were ultimately selected and contributed to the Radscore construction. A positive correlation was observed between the satellite sign number and Radscore (Pearson’s r: 0.451). The nomogram model showed the best performance with high area under the curves in both training cohort (0.881, sensitivity: 0.973; specificity: 0.787) and external validation cohort (0.857, sensitivity: 0.950; specificity: 0.766). The calibration curve, DCA, and KM analysis indicated the high accuracy and clinical usefulness of the nomogram model for hematoma expansion prediction.

Conclusion

A nomogram model of integrated radiomic signature and satellite sign number based on noncontrast CT images could serve as a reliable and convenient measurement of hematoma expansion prediction.

Island Sign Predicts Hematoma Expansion and Poor Outcome After Intracerebral Hemorrhage: A Systematic Review and Meta-Analysis

Background: Early hematoma expansion (HE) occurs in patients with intracerebral hemorrhage (ICH) within the first few hours from ICH onset. Hematoma expansion has been considered as an independent predictor of poor clinical outcome and mortality after ICH. Island sign (IS) on the non-contrast computed tomography (NCCT) appears to increase the rate of detection of HE. However, there is insufficient evidence to declare that IS is an independent predictor for ICH patients prognosis and classification.

Objectives: To investigate whether IS on NCCT could predict HE and functional outcome following ICH.

Methods: Major databases were systematically searched, including PubMed, EMBASE, Cochrane library, and the Chinese database (CNKI, VIP, and Wanfang databases). Studies about the associations between IS and HE or IS and clinical outcome were included. The pooled result used the odds ratio (OR) with a 95% confidence interval (CI) as effect size. Heterogeneity and publication bias were assessed. Subgroup analysis and meta-regression were applied to detect potential factors of heterogeneity.

Results: Eleven studies with 4,310 patients were included in the final analysis. The average incidence rate of IS and HE were 21.58 and 33%, respectively. The ideal timing for assessing HE was also not uniform or standardized. We separately performed two meta-analyses. First, 10 studies were included to estimate the association between IS and HE. The pooled OR was statistically significant (OR = 7.61, 95% CI = 3.10–18.67, P < 0.001). Second, four studies were included in the meta-analysis, and the pooled result showed that IS had a significantly positive relationship with poor outcome (OR = 3.83, 95% CI = 2.51–5.85, P < 0.001).

Conclusions: This meta-analysis showed that NCCT IS is of great importance and value for evaluation of HE and poor outcome in patients with ICH. Future studies should focus on developing consensus guidelines, and more studies with large sample size and longitudinal design are needed to validate the conclusions.

The slice score: A novel scale measuring intraventricular hemorrhage severity and predicting poor outcome following intracerebral hemorrhage

OBJECTIVES

To quantify extent of intraventricular hemorrhage (IVH) following intracerebral hemorrhage (ICH) with a novel, simple IVH severity score, and to explore and compare its performance in predicting worse outcomes.

PATIENTS AND METHODS

A new scoring system for IVH severity was proposed and termed Slice score. The Slice score features non-septum pellucidum section, internal capsule section, third ventricle occipital horn section, three standardized scans for scoring the lateral ventricles. 652 scans from 326 subjects were retrospectively analyzed. The correlations between measured IVH volume and Slice score, original Graeb, LeRoux, and IVH score (IVHS) were compared. The association between these scores and clinical outcomes were evaluated using logistic regression. We then identified clinical thresholds of Slice score by balancing the probability of prediction and accuracy. Primary outcome was defined as 90-day poor outcome (modified Rankin Scale score ≥ 4) and secondary outcome was 90-day mortality.

RESULTS

Of 326 ICH patients, 122 (37.4%) had poor outcome and 59 (18.1%) died at 3 months. The Slice score showed the highest correlation with measured IVH volume (R = 0.73, R² = 0.54, p < 0.001). The observed area under the curve were similar among the Slice, original Graeb, LeRoux score, and IVH score for poor outcome (0.633, 0.633, 0.632, 0.634, respectively), and for mortality (0.660, 0.660, 0.660, 0.656, respectively). All IVH scales were independently associated with 90-day poor outcome and mortality with close odds ratio in adjusted models (all odds ratio > 1.07, all p < 0.05). Multivariable Analyses of categorized Slice score revealed optimal thresholds of 6 and 12 for primary and secondary outcomes (odds ratio 4.20, 95% confidence interval 1.82-10.02, p = 0.001; odds ratio 5.41, 95% confidence interval 1.66-17.43, p = 0.005, respectively).

CONCLUSIONS

The Slice score correlated highly with the IVH volume, was a reliable volumetric scale for measuring IVH severity, and could be an easy-to-use tool for predicting 90-day poor outcome and mortality in ICH.

Prognostic Value of Non-Contrast CT Markers and Spot Sign for Outcome Prediction in Patients with Intracerebral Hemorrhage under Oral Anticoagulation

Introduction: In patients with spontaneous intracerebral hemorrhage (ICH), several non-contrast computed tomography (NCCT) markers and the spot sign (SS) in computed tomography (CT) angiography (CTA) have been established for the prediction of hematoma growth and neurological outcome. However, the prognostic value of these markers in patients under oral anticoagulation (ORAC) is unclear. We hypothesized that outcome prediction by these imaging markers may be significantly different between patients with and without ORAC. Therefore, we aimed to investigate the predictive value of NCCT markers and SS in patients with ICH under ORAC. Methods: This is a retrospective study of the database for patients with ICH at a German tertiary stroke center. Inclusion criteria were (1) patients with ICH, (2) oral anticoagulation within the therapeutic range, and (3) NCCT and CTA performed on admission within 6 h after onset of symptoms. We defined a binary outcome: modified Rankin Scale (mRS) ≤ 3 = good outcome versus mRS > 3 = poor outcome at discharge. The predictive value of each sign was assessed in uni- and multivariable logistic regression models. Results: Of 129 patients with ICH under ORAC, 76 (58.9%) presented with hypodensities within the hematoma in admission NCCT, 64 (52.7%) presented with an irregular shape of the hematoma, 60 (46.5%) presented with a swirl sign, 49 (38.0%) presented with a black hole sign, and 46 (35.7%) presented with a heterogeneous density of the hematoma. Moreover, 44 (34.1%) patients had a satellite sign, in 20 (15.5%) patients, an island sign was detected, 18 (14.0%) patients were blend-sign positive, and 14 (10.9%) patients presented with a CTA spot sign. Inter-rater agreement was very high for all included characteristics between the two readers. Multivariable logistic regression analysis identified the presence of black hole sign (odds ratio 10.59; p < 0.001), swirl sign (odds ratio 14.06; p < 0.001), and satellite sign (odds ratio 6.38; p = 0.011) as independent predictors of poor outcome. Conclusions: The distribution and prognostic value of several NCCT markers and CTA spot sign in ICH patients under ORAC is comparable to those with spontaneous ICH, even though these parameters are partly based on coagulant status. These findings suggest that a similar approach can be used for further research regarding outcome prediction in ICH patients under ORAC and those with spontaneous ICH.

Inter- and Intrarater Agreement of Spot Sign and Noncontrast CT Markers for Early Intracerebral Hemorrhage Expansion

Background: The aim of this study was to assess the inter- and intrarater reliability of noncontrast CT (NCCT) markers [Black Hole Sign (BH), Blend Sign (BS), Island Sign (IS), and Hypodensities (HD)] and Spot Sign (SS) on CTA in patients with spontaneous intracerebral hemorrhage (ICH). Methods: Patients with spontaneous ICH at three German tertiary stroke centers were retrospectively included. Each CT scan was rated for four NCCT markers and SS on CTA by two radiology residents. Raters were blind to all demographic and outcome data. Inter- and intrarater agreement was determined by Cohen’s kappa (κ) coefficient and percentage of agreement. Results: Interrater agreement was excellent in 473 included patients, ranging from 96% to 99%. Interrater κ ranged from 0.85 (95% CI [0.78–0.91]) to 0.97 (95% CI [0.94–0.99]) for NCCT markers and 0.93 (95% CI [0.88–0.98]) for SS, all p-values < 0.001. Intrarrater agreement ranged from 96% to 100%, with κ ranging from 0.85 (95% CI [0.78–0.91]) to 1.00 (95% CI [0.10–0.85]) for NCCT markers and 0.96 (95% CI [0.92–1.00]) for SS, all p-values < 0.001. Conclusions: NCCT imaging findings and SS on CTA have good-to-excellent inter- and intrarater reliabilities, with the highest agreement for BH and SS.

Combination of Ultraearly Hematoma Growth and Hypodensities for Outcome Prediction after Intracerebral Hemorrhage

BACKGROUND

Noncontrast computed tomography hypodensities (HD) and ultraearly hematoma growth (uHG) are reliable markers for outcome prediction in patients with spontaneous intracerebral hemorrhage (sICH). The present study aimed to assess whether the combination of these 2 markers could improve the prognostic value for sICH.

METHODS

We recruited 242 patients with sICH who had been admitted within 6 hours from the onset of symptoms. HD was assessed by 2 independent blinded readers, and uHG was calculated as baseline ICH volume/onset-to-imaging time. We divided the study population into 4 groups: uHG(L) HD(-) (uHG <6.16 mL/hour and HD negative), uHG(L) HD(+) (uHG<6.16 mL/hour and HD positive), uHG(H) HD(-) (uHG ≥6.16 mL/hour and HD negative), and uHG(H) HD(+) (uHG ≥6.16 mL/h and HD positive). The outcome at 90 days was evaluated by the modified Rankin Scale (mRS) score and was dichotomized as good (mRS score 0-3) and poor (mRS score 4-6). The association between the combined indicators and unfavorable outcome was investigated using multivariable logistic regression models.

RESULTS

Patients with poor outcomes were more likely to have HD and higher uHG in univariate analysis. In multivariate logistic regression analysis, uHG(H) HD(+) had a higher risk of unfavorable outcomes compared with uHG(L) HD(-) (odds ratio [OR], 5.710; P < 0.001). In addition, the risk of unfavorable outcomes was increased in uHG(H) HD(-) (OR, 2.957, P = 0.044) and uHG(L) HD(+) (OR, 1.924; P = 0.232). The proportions of unfavorable prognoses were 32.6% in uHG(L) HD(-), 48.3% in uHG(L) HD(+), 72.2% in uHG(H) HD(-), and 87.5% in uHG(H) HD(+) (P < 0.001).

CONCLUSIONS

The combination of uHG and HD improves the stratification of unfavorable prognoses in patients with sICH.

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

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

Does Noncontrast Computed Tomography Scan Predict Rebleeding After Endoscopic Surgery for Spontaneous Intracerebral Hemorrhage?

BACKGROUND

The relationship between noncontrast computed tomography (CT) markers, which predict the expansion of spontaneous intracerebral hemorrhage (sICH) under conservative treatment, and postoperative rebleeding (PR) after treatment by directly removing the sICH is unknown. This study investigated the relationship between noncontrast CT markers and PR in patients with sICH treated by endoscopic surgery.

METHODS

The study population included 92 patients with available data who underwent endoscopic surgery for sICH at our institution from January 2010 to September 2018. The correlations between PR and preoperative noncontrast CT markers, including the blend sign, hypodensities, black hole sign, heterogeneous density, and island signs, were retrospectively evaluated.

RESULTS

In 5 of the 18 patients (27.8%) with the blend sign, PR developed, whereas only 5 of 74 patients (6.8%) without the blend sign developed PR. In the univariate regression analyses, manifestation of hydrocephalus (odds ratio [OR], 8.75; 95% confidence interval [CI], 2.15-35.68; P = 0.002), presence of the blend sign (OR, 5.31; 95% CI, 1.34-20.97; P = 0.02), and insertion of external ventricular drainage (OR, 13.88; 95% CI, 3.22-59.77; P < 0.001) were significant risk factors. The other radiographic markers were not associated with PR. In a multivariate analysis, the presence of the blend sign (OR, 22.07; 95% CI, 2.18-223.60; P = 0.009) was the only independent predictor of PR.

CONCLUSIONS

The blend sign is likely to be a strong predictor for PR in patients who undergo endoscopic surgery for sICH. To improve the prognosis of patients with sICH, further studies are needed to establish new treatment strategies and surgical procedures.