Noncontrast Computed Tomography Hypodensities Predict Poor Outcome in Intracerebral Hemorrhage Patients

Enhancing mortality prediction in patients with spontaneous intracerebral hemorrhage: Radiomics and supervised machine learning on non-contrast computed tomography

Purpose

This study aims to develop a Radiomics-based Supervised Machine-Learning model to predict mortality in patients with spontaneous intracerebral hemorrhage (sICH).

Methods

Retrospective analysis of a prospectively collected clinical registry of patients with sICH consecutively admitted at a single academic comprehensive stroke center between January-2016 and April-2018. We conducted an in-depth analysis of 105 radiomic features extracted from 105 patients. Following the identification and handling of missing values, radiomics values were scaled to 0-1 to train different classifiers. The sample was split into 80-20 % training-test and validation cohort in a stratified fashion. Random Forest(RF), K-Nearest Neighbor(KNN), and Support Vector Machine(SVM) classifiers were evaluated, along with several feature selection methods and hyperparameter optimization strategies, to classify the binary outcome of mortality or survival during hospital admission. A tenfold stratified cross-validation method was used to train the models, and average metrics were calculated.

Results

RF, KNN, and SVM, with the "DropOut+SelectKBest" feature selection strategy and no hyperparameter optimization, demonstrated the best performances with the least number of radiomic features and the most simplified models, achieving a sensitivity range between 0.90 and 0.95 and AUC range from 0.97 to 1 on the validation dataset. Regarding the confusion matrix, the SVM model did not predict any false negative test (negative predicted value 1).

Conclusion

Radiomics-based Supervised Machine Learning models can predict mortality during admission in patients with sICH. SVM with the "DropOut+SelectKBest" feature selection strategy and no hyperparameter optimization was the best simplified model to detect mortality during admission in patients with sICH.

Added Value of Frequency of Imaging Markers for Prediction of Outcome After Intracerebral Hemorrhage: A Secondary Analysis of Existing Data

BACKGROUND

Frequency of imaging markers (FIM) has been identified as an independent predictor of hematoma expansion in patients with intracerebral hemorrhage (ICH), but its impact on clinical outcome of ICH is yet to be determined. The aim of the present study was to investigate this association.

METHODS

This study was a secondary analysis of our prior research. The data for this study were derived from six retrospective cohorts of ICH from January 2018 to August 2022. All consecutive study participants were examined within 6 h of stroke onset on neuroimaging. FIM was defined as the ratio of the number of imaging markers on noncontrast head tomography (i.e., hypodensities, blend sign, and island sign) to onset-to-neuroimaging time. The primary poor outcome was defined as a modified Rankin Scale score of 3-6 at 3 months.

RESULTS

A total of 1253 patients with ICH were included for final analysis. Among those with available follow-up results, 713 (56.90%) exhibited a poor neurologic outcome at 3 months. In a univariate analysis, FIM was associated with poor prognosis (odds ratio 4.36; 95% confidence interval 3.31-5.74; p < 0.001). After adjustment for age, Glasgow Coma Scale score, systolic blood pressure, hematoma volume, and intraventricular hemorrhage, FIM was still an independent predictor of worse prognosis (odds ratio 3.26; 95% confidence interval 2.37-4.48; p < 0.001). Based on receiver operating characteristic curve analysis, a cutoff value of 0.28 for FIM was associated with 0.69 sensitivity, 0.66 specificity, 0.73 positive predictive value, 0.62 negative predictive value, and 0.71 area under the curve for the diagnosis of poor outcome.

CONCLUSIONS

The metric of FIM is associated with 3-month poor outcome after ICH. The novel indicator that helps identify patients who are likely within the 6-h time window at risk for worse outcome would be a valuable addition to the clinical management of ICH.

Natural History, Pathophysiology, and Recent Management Modalities of Intraventricular Hemorrhage

Intraventricular hemorrhage (IVH) is a clinical challenge observed among 40-45% of intracerebral hemorrhage (ICH) cases. IVH can be classified according to the source of the hemorrhage into primary and secondary IVH. Primary intraventricular hemorrhage (PIVH), unlike secondary IVH, involves only the ventricles with no hemorrhagic parenchymal source. Several risk factors of PIVH were reported which include hypertension, smoking, age, and excessive alcohol consumption. IVH is associated with high mortality and morbidity and several prognostic factors were identified such as IVH volume, number of ventricles with blood, involvement of fourth ventricle, baseline Glasgow Coma Scale score, and hydrocephalus. Prompt management of patients with IVH is required to stabilize the clinical status of patients upon admission. Nevertheless, further advanced management is crucial to reduce the morbidity and mortality associated with intraventricular bleeding. Recent treatments showed promising outcomes in the management of IVH patients such as intraventricular anti-inflammatory drugs, lumbar drainage, and endoscopic evacuation of IVH, however, their safety and efficacy are still in question. This literature review presents the epidemiology, physiopathology, risk factors, and outcomes of IVH in adults with an emphasis on recent treatment options.

Quantitative imaging for predicting hematoma expansion in intracerebral hemorrhage: A multimodel comparison

BACKGROUND

Several studies report that radiomics provides additional information for predicting hematoma expansion in intracerebral hemorrhage (ICH). However, the comparison of diagnostic performance of radiomics for predicting revised hematoma expansion (RHE) remains unclear.

METHODS

The cohort comprised 312 consecutive patients with ICH. A total of 1106 radiomics features from seven categories were extracted using Python software. Support vector machines achieved the best performance in both the training and validation datasets. Clinical factors models were constructed to predict RHE. Receiver operating characteristic curve analysis was used to assess the abilities of non-contrast computed tomography (NCCT) signs, radiomics features, and combined models to predict RHE.

RESULTS

We finally selected the top 21 features for predicting RHE. After univariate analysis, 4 clinical factors and 5 NCCT signs were selected for inclusion in the prediction models. In the training and validation dataset, radiomics features had a higher predictive value for RHE (AUC = 0.83) than a single NCCT sign and expansion-prone hematoma. The combined prediction model including radiomics features, clinical factors, and NCCT signs achieved higher predictive performances for RHE (AUC = 0.88) than other combined models.

CONCLUSIONS

NCCT radiomics features have a good degree of discrimination for predicting RHE in ICH patients. Combined prediction models that include quantitative imaging significantly improve the prediction of RHE, which may assist in the risk stratification of ICH patients for anti-expansion treatments.

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.

Prediction of Hematoma Expansion in Intracerebral Hemorrhage in 24 Hours by Machine Learning Algorithm

OBJECTIVE

The significance of noncontrast computer tomography (CT) image markers in predicting hematoma expansion (HE) following intracerebral hemorrhage (ICH) within different time intervals in the initial 24 hours after onset may be uncertain. Hence, our objective was to examine the predictive value of clinical factors and CT image markers for HE within the initial 24 hours using machine learning algorithms.

METHODS

Four machine learning algorithms, including extreme gradient boosting (XGBoost), support vector machine, random forest, and logistic regression, were employed to assess the predictive efficacy of HE within every 6-hour interval during the first 24 hours post-ICH. The area under the receiver operating characteristic curves was utilized to appraise predictive performance across various time periods within the initial 24 hours.

RESULTS

A total of 604 patients were included, with 326 being male, and 112 experiencing hematoma expansion (HE). The findings from machine learning algorithms revealed that computed tomography (CT) image markers, baseline hematoma volume, and other factors could accurately predict HE. Among these algorithms, XGBoost demonstrated the most robust predictive model results. XGBoost's accuracy at different time intervals was 0.89, 0.82, 0.87, and 0.94, accompanied by F1-scores of 0.89, 0.80, 0.87, and 0.93, respectively. The corresponding area under the curve was 0.96, affirming the precision of the predictive capability.

CONCLUSIONS

Computed tomography (CT) imaging markers and clinical factors could effectively predict HE within the initial 24 hours across various time periods by machine learning algorithms. In the expansive landscape of big data and multimodal cerebral hemorrhage, machine learning held significant potential within the realm of neuroscience.

Predicting the 90-day prognosis of stereotactic brain hemorrhage patients by multiple machine learning using radiomic features combined with clinical features

Hypertensive Intracerebral Hemorrhage (HICH) is one of the most common types of cerebral hemorrhage with a high mortality and disability rate. Currently, preoperative non-contrast computed tomography (NCCT) scanning-guided stereotactic hematoma removal has achieved good results in treating HICH, but some patients still have poor prognoses. This study collected relevant clinical and radiomic data by retrospectively collecting and analyzing 432 patients who underwent stereotactic hematoma removal for HICH from January 2017 to December 2020 at the Liuzhou Workers Hospital. The prognosis of patients after 90 days was judged by the modified Rankin Scale (mRS) scale and divided into the good prognosis group (mRS ≤ 3) and the poor prognosis group (mRS > 3). The 268 patients were randomly divided into training and test sets in the ratio of 8:2, with 214 patients in the training set and 54 patients in the test set. The least absolute shrinkage and selection operator (Lasso) was used to screen radiomics features. They were combining clinical features and radiomic features to build a joint prediction model of the nomogram. The AUCs of the clinical model for predicting different prognoses of patients undergoing stereotactic HICH were 0.957 and 0.922 in the training and test sets, respectively, while the AUCs of the radiomics model were 0.932 and 0.770, respectively, and the AUCs of the combined prediction model for building a nomogram were 0.987 and 0.932, respectively. Compared with a single clinical or radiological model, the nomogram constructed by fusing clinical variables and radiomic features could better identify the prognosis of HICH patients undergoing stereotactic hematoma removal after 90 days.

Radiomic-based nonlinear supervised learning classifiers on non-contrast CT to predict functional prognosis in patients with spontaneous intracerebral hematoma

PURPOSE

To evaluate if nonlinear supervised learning classifiers based on non-contrast CT can predict functional prognosis at discharge in patients with spontaneous intracerebral hematoma.

METHODS

Retrospective, single-center, observational analysis of patients with a diagnosis of spontaneous intracerebral hematoma confirmed by non-contrast CT between January 2016 and April 2018. Patients with HIE > 18 years and with TCCSC performed within the first 24 h of symptom onset were included. Patients with secondary spontaneous intracerebral hematoma and in whom radiomic variables were not available were excluded. Clinical, demographic and admission variables were collected. Patients were classified according to the Modified Rankin Scale (mRS) at discharge into good (mRS 0-2) and poor prognosis (mRS 3-6). After manual segmentation of each spontaneous intracerebral hematoma, the radiomics variables were obtained. The sample was divided into a training and testing cohort and a validation cohort (70-30% respectively). Different methods of variable selection and dimensionality reduction were used, and different algorithms were used for model construction. Stratified 10-fold cross-validation were performed on the training and testing cohort and the mean area under the curve (AUC) were calculated. Once the models were trained, the sensitivity of each was calculated to predict functional prognosis at discharge in the validation cohort.

RESULTS

105 patients with spontaneous intracerebral hematoma were analyzed. 105 radiomic variables were evaluated for each patient. P-SVM, KNN-E and RF-10 algorithms, in combination with the ANOVA variable selection method, were the best performing classifiers in the training and testing cohort (AUC 0.798, 0.752 and 0.742 respectively). The predictions of these models, in the validation cohort, had a sensitivity of 0.897 (0.778-1;95%CI), with a false-negative rate of 0% for predicting poor functional prognosis at discharge.

CONCLUSION

The use of radiomics-based nonlinear supervised learning classifiers are a promising diagnostic tool for predicting functional outcome at discharge in HIE patients, with a low false negative rate, although larger and balanced samples are still needed to develop and improve their performance.

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.

Difference of mean Hounsfield units (dHU) between follow-up and initial noncontrast CT scan predicts 90-day poor outcome in spontaneous supratentorial acute intracerebral hemorrhage with deep convolutional neural networks

OBJECTIVES

This study aimed to investigate the usefulness of a new non-contrast CT scan (NCCT) sign called the dHU, which represented the difference in mean Hounsfield unit values between follow-up and the initial NCCT for predicting 90-day poor functional outcomes in acute supratentorial spontaneous intracerebral hemorrhage(sICH) using deep convolutional neural networks.

METHODS

A total of 377 consecutive patients with sICH from center 1 and 91 patients from center 2 (external validation set) were included. A receiver operating characteristic (ROC) analysis was performed to determine the critical value of dHU for predicting poor outcome at 90 days. Modified Rankin score (mRS) >3 or >2 was defined as the primary and secondary poor outcome, respectively. Two multivariate models were developed to test whether dHU was an independent predictor of the two unfavorable functional outcomes.

RESULTS

The ROC analysis showed that a dHU >2.5 was a critical value to predict the poor outcomes (mRS >3) in sICH. The sensitivity, specificity, and accuracy of dHU >2.5 for poor outcome prediction were 37.5%, 86.0%, and 70.6%, respectively. In multivariate models developed after adjusting for all elements of the ICH score and hematoma expansion, dHU >2.5 was an independent predictor of both primary and secondary poor outcomes (OR = 2.61, 95% CI [1.32,5.13], P = 0.006; OR = 2.63, 95% CI [1.36,5.10], P = 0.004, respectively). After adjustment for all possible significant predictors (p < 0.05) by univariate analysis, dHU >2.5 had a positive association with primary and secondary poor outcomes (OR = 3.25, 95% CI [1.52,6.98], P = 0.002; OR = 3.42, 95% CI [1.64,7.15], P = 0.001).

CONCLUSIONS

The dHU of hematoma based on serial CT scans is independently associated with poor outcomes after acute sICH, which may help predict clinical evolution and guide therapy for sICH patients.

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.

The "SALPARE study" of spontaneous intracerebral haemorrhage-part 2-early CT predictors of outcome in ICH: keeping it simple

BACKGROUND

The aim of this study was to investigate the prognostic role of hematoma characteristics and hematoma expansion (HE) in patients with spontaneous intracerebral hemorrhage (ICH).

METHODS

This multicenter prospective cohort study enrolled consecutive adult patients with non-traumatic ICH admitted to three Italian academic hospitals (Salerno, Padova, Reggio Emilia) over a 2-year period. Early noncontrast CT (NCCT) features of the hematoma, including markers of HE, and 3-month outcome were recorded. Multivariable logistic regression analysis was performed to identify predictors of poor outcome.

RESULTS

A total of 682 patients were included in the study [mean age: 73 ± 14 years; 316 (46.3%) females]. Pontine and massive hemorrhage, intraventricular bleeding, baseline hematoma volume > 15 mL, blend sign, swirl sign, margin irregularity ≥ 4, density heterogeneity ≥ 3, hypodensity ≥ 1, island sign, satellite sign, and black hole sign were associated with a higher risk of mortality and disability. However, at multivariate analysis only initial hematoma volume (OR 29.71) proved to be an independent predictor of poor functional outcome at 3 months.

CONCLUSION

Simple hematoma volume measured on baseline CT best identifies patients with a worse outcome, while early NCCT markers of HE do not seem to add any clinically significant information.

The clinical value of neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and D-dimer-to-fibrinogen ratio for predicting pneumonia and poor outcomes in patients with acute intracerebral hemorrhage

Background

This study aimed to investigate the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and D-dimer-to-fibrinogen ratio (DFR) as predictors of pneumonia and poor outcomes in patients with acute intracerebral hemorrhage (ICH).

Methods

We retrospectively examined patients with acute ICH treated in our institution from May 2018 to July 2020. Patient characteristics, laboratory testing data, radiologic imaging data, and 90-day outcomes were recorded and analyzed.

Results

Among the 329 patients included for analysis, 183 (55.6%) developed pneumonia. Systolic blood pressure, initial hematoma volume, D-dimer concentration, NLR, PLR, DFR, and white blood cell, platelet, neutrophil, and lymphocyte counts at admission were significantly higher in patients who developed pneumonia than in those who did not; however, the Glasgow coma scale (GCS) score at admission was significantly lower in pneumonia patients compared with non-pneumonia patients (all P <0.05). Multivariate logistic regression showed that the NLR and PLR were independent predictors of pneumonia, and the NLR and DFR were independent predictors of poor 90-day outcomes (modified Rankin scale score 4–6).

Conclusion

The NLR and PLR were independent predictors of pneumonia and the NLR and DFR were independent predictors of poor 90-day outcomes. The NLR, PLR, and DFR can provide prognostic information about acute ICH patients.

Hyperacute prediction of functional outcome in spontaneous intracerebral haemorrhage: systematic review and meta-analysis

Purpose

To describe the association between factors routinely available in hyperacute care of spontaneous intracerebral haemorrhage (ICH) patients and functional outcome.

Methods

We searched Medline, Embase and CINAHL in February 2020 for original studies reporting associations between markers available within six hours of arrival in hospital and modified Rankin Scale (mRS) at least 6 weeks post-ICH. A random-effects meta-analysis was performed where three or more studies were included.

Findings

Thirty studies were included describing 40 markers. Ten markers underwent meta-analysis and age (OR = 1.06; 95%CI = 1.05 to 1.06; p < 0.001), pre-morbid dependence (mRS, OR = 1.73; 95%CI = 1.52 to 1.96; p < 0.001), level of consciousness (Glasgow Coma Scale, OR = 0.82; 95%CI = 0.76 to 0.88; p < 0.001), stroke severity (National Institutes of Health Stroke Scale, OR=1.19; 95%CI = 1.13 to 1.25; p < 0.001), haematoma volume (OR = 1.12; 95%CI=1.07 to 1.16; p < 0.001), intraventricular haemorrhage (OR = 2.05; 95%CI = 1.68 to 2.51; p < 0.001) and deep (vs. lobar) location (OR = 2.64; 95%CI = 1.65 to 4.24; p < 0.001) were predictive of outcome but systolic blood pressure, CT hypodensities and infratentorial location were not. Of the remaining markers, sex, medical history (diabetes, hypertension, prior stroke), prior statin, prior antiplatelet, admission blood results (glucose, cholesterol, estimated glomerular filtration rate) and other imaging features (midline shift, spot sign, sedimentation level, irregular haematoma shape, ultraearly haematoma growth, Graeb score and onset to CT time) were associated with outcome.

Conclusion

Multiple demographic, pre-morbid, clinical, imaging and laboratory factors should all be considered when prognosticating in hyperacute ICH. Incorporating these in to accurate and precise models will help to ensure appropriate levels of care for individual patients.

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.

Integrated Multiomics Analysis Identifies a Novel Biomarker Associated with Prognosis in Intracerebral Hemorrhage

Existing treatments for intracerebral hemorrhage (ICH) are unable to satisfactorily prevent development of secondary brain injury after ICH and multiple pathological mechanisms are involved in the development of the injury. In this study, we aimed to identify novel genes and proteins and integrated their molecular alternations to reveal key network modules involved in ICH pathology. A total of 30 C57BL/6 male mice were used for this study. The collagenase model of ICH was employed, 3 days after ICH animals were tested neurological. After it, animals were euthanized and perihematomal brain tissues were collected for transcriptome and TMT labeling-based quantitative proteome analyses. Protein-protein interaction (PPI) network, Gene Set Enrichment Analysis (GSEA), and regularized Canonical Correlation Analysis (rCCA) were performed to integrated multiomics data. For validation of hub genes and proteins, qRT-PCR and Western blot were carried out. The candidate biomarkers were further measured by ELISA in the plasma of ICH patients and the controls. A total of 2218 differentially expressed genes (DEGs) and 353 differentially expressed proteins (DEPs) between the ICH model group and control group were identified. GSEA revealed that immune-related gene sets were prominently upregulated and significantly enriched in pathways of inflammasome complex, negative regulation of interleukin-12 production, and pyroptosis during the ICH process. The rCCA network presented two highly connective clusters which were involved in the sphingolipid catabolic process and inflammatory response. Among ten hub genes screened out by integrative analysis, significantly upregulated Itgb2, Serpina3n, and Ctss were validated in the ICH group by qRT-PCR and Western blot. Plasma levels of human SERPINA3 (homologue of murine Serpina3n) were elevated in ICH patients compared with the healthy controls (SERPINA3: 13.3 ng/mL vs. 11.2 ng/mL, p = 0.015). Within the ICH group, higher plasma SERPINA3 levels with a predictive threshold of 14.31 ng/mL (sensitivity = 64.3%; specificity = 80.8%; AUC = 0.742, 95% CI: 0.567-0.916) were highly associated with poor outcome (mRS scores 4-6). Taken together, the results of our study exhibited molecular changes related to ICH-induced brain injury by multidimensional analysis and effectively identified three biomarker candidates in a mouse ICH model, as well as pointed out that Serpina3n/SERPINA3 was a potential biomarker associated with poor functional outcome in ICH patients.

Neuroprotective effects of adipose‐derived stem cells on ferrous sulfate‐induced neurotoxicity

Background:

Ferrous ion, a degradation product of hematomas, induces inflammatory reactions and other secondary injuries after intracerebral hemorrhage (ICH). Our study aimed to investigate the specific neuroprotective mechanism of adipose‐derived stem cells (ADSCs) on ferrous ion‐induced neural injury in vitro.

Methods:

ADSCs were co‐cultured with primary cortical neurons in a transwell system treated with ferrous sulfate to generate an in vitro ICH model. ADSCs and cortical neurons were cultured in the upper and lower chambers, respectively. Neuron apoptosis was determined by flow cytometry. The levels of insulin‐like growth factor‐1 (IGF‐1), malondialdehyde (MDA) and nitric oxide synthase (NOS) activity in neuron culture medium were detected with commercial kits. In neurons, protein expression in phosphatidylinositol‐3‐kinase (PI3K)/protein kinase B (Akt) signaling pathway, nuclear factor erythroid 2‐related factor 2 (Nrf2)/heme oxygenase‐1 (HO‐1) signaling pathway and apoptosis‐related proteins were detected by western blot.

Results:

ADSCs attenuated neural apoptosis, reduced MDA levels and NOS activity induced by ferrous sulfate. In neurons, IGF‐1 was increased, as were p‐PI3K, p‐Akt, Nrf2, HO‐1, and Bcl‐2 while cleaved caspase 3 was down‐regulated.

Conclusions:

ADSCs exert neuroprotective effects against ferrous iron‐induced neuronal damage by secreting IGF‐1 and increasing the levels of Akt‐dependent Nrf2/ARE signaling pathway.

Size-Related Differences in Computed Tomography Markers of Hematoma Expansion in Acute Intracerebral Hemorrhage

BACKGROUND

Noncontrast computed tomography (NCCT) markers for hematoma expansion (HE) in intracerebral hemorrhage (ICH) are difficult to be found in small ICHs, of which can also expand. We aimed to investigate whether there were size-related differences in the prevalence of NCCT markers and their association with HE.

METHODS

This retrospective analysis of prospectively collected stroke registry included 267 consecutive patients with ICH who underwent baseline NCCT within 12 h of onset. Qualitative NCCT markers, including heterogeneous density and irregular shape, were assessed. Hematoma density, defined as mean Hounsfield unit of hematoma, and hematoma volume were measured by semiautomated planimetry. Hematoma volume was categorized as small (≤ 10 ml) and large (> 10 ml). Associations of NCCT markers with HE were analyzed using multivariable logistic regression analyses. The model performances of NCCT markers and hematoma density were compared using receiver operating characteristic curves.

RESULTS

Hematoma expansion occurred in 29.9% of small ICHs and 35.5% of large ICHs. Qualitative NCCT markers were less frequently observed in small ICHs. Heterogeneous density, irregular shape, and hematoma density were associated with HE in small ICH (adjusted odds ratios [95% confidence interval] 3.94 [1.50-10.81], 4.23 [1.73-10.81], and 0.72 [0.60-0.84], respectively), and hematoma density was also related to HE in large ICH (0.84 [0.73-0.97). The model performance was significantly improved in small ICHs when hematoma density was added to the baseline model (DeLong's test, p = 0.02).

CONCLUSIONS

The prevalence of NCCT markers and their association with HE differed according to hematoma volume. Quantitative hematoma density was associated with HE, regardless of hematoma size.

Intraventricular Hemorrhage Growth: Definition, Prevalence and Association with Hematoma Expansion and Prognosis

BACKGROUND/OBJECTIVES

The objective of this study is to propose a definition of intraventricular hemorrhage (IVH) growth and to investigate whether IVH growth is associated with ICH expansion and functional outcome.

METHODS

We performed a prospective observational study of ICH patients between July 2011 and March 2017 in a tertiary hospital. Patients were included if they had a baseline CT scan within 6 h after onset of symptoms and a follow-up CT within 36 h. IVH growth was defined as either any newly occurring intraventricular bleeding on follow-up CT scan in patients without baseline IVH or an increase in IVH volume ≥ 1 mL on follow-up CT scan in patients with initial IVH. Poor outcome was defined as modified Rankin Scale score of 3-6 at 90 days. The association between IVH growth and functional outcome was assessed by using multivariable logistic regression analysis.

RESULTS

IVH growth was observed in 59 (19.5%) of 303 patients. Patients with IVH growth had larger baseline hematoma volume, higher NIHSS score and lower GCS score than those without. Of 44 patients who had concurrent IVH growth and hematoma growth, 41 (93.2%) had poor functional outcome at 3-month follow-up. IVH growth (adjusted OR 4.15, 95% CI 1.31-13.20; P = 0.016) was an independent predictor of poor functional outcome (mRS 3-6) at 3 months in multivariable analysis.

CONCLUSION

IVH growth is not uncommon and independently predicts poor outcome in ICH patients. It may serve as a promising therapeutic target for intervention.

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.

New Prediction Models of Functional Outcome in Acute Intracerebral Hemorrhage: The dICH Score and uICH Score

Objectives: The original intracerebral hemorrhage (oICH) score is the severity score most commonly used in clinical intracerebral hemorrhage (ICH) research but may be influenced by hematoma expansion or intraventricular hemorrhage (IVH) growth in acute ICH. Here, we aimed to develop new clinical scores to improve the prediction of functional outcomes in patients with ICH.

Methods: Patients admitted to the First Affiliated Hospital of Chongqing Medical University with primary ICH were prospectively enrolled in this study. Hematoma volume was measured using a semiautomated, computer-assisted technique. The dynamic ICH (dICH) score was developed by incorporating hematoma expansion and IVH growth into the oICH score. The ultra-early ICH (uICH) score was developed by adding the independent non-contrast CT markers to the oICH score. Receiver operating characteristic curve analysis was used to compare performance among the oICH score, dICH score, and uICH score.

Results: There were 310 patients in this study which included 72 patients (23.2%) with hematoma expansion and 58 patients (18.7%) with IVH growth. Of 31 patients with two or more non-contrast computed tomography markers, 61.3% died, and 96.8% had poor outcomes at 90 days. After adjustment for potential confounding variables, we found that age, baseline Glasgow Coma Scale score, presence of IVH on initial CT, baseline ICH volume, infratentorial hemorrhage, hematoma expansion, IVH growth, blend sign, black hole sign, and island sign could independently predict poor outcomes in multivariate analysis. In comparison with the oICH score, the dICH score and uICH score exhibited better performance in the prediction of poor functional outcomes.

Conclusions: The dICH score and uICH score were useful clinical assessment tools that could be used for risk stratification concerning functional outcomes and provide guidance in clinical decision-making in acute ICH.

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.

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

OBJECTIVES

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

MATERIALS AND METHODS

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

RESULTS

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

CONCLUSION

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

KEY POINTS

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

A clinical-radiomics nomogram may provide a personalized 90-day functional outcome assessment for spontaneous intracerebral hemorrhage

OBJECTIVES

To develop and validate a noncontrast computed tomography (NCCT)-based clinical-radiomics nomogram to identify spontaneous intracerebral hemorrhage (sICH) patients with a poor 90-day prognosis on admission.

METHODS

In this double-center retrospective study, data from 435 patients with sICH (training cohort: n = 244; internal validation cohort: n = 104; external validation cohort: n = 87) were reviewed. The radiomics score (Rad-score) was calculated based on the coefficients of the selected radiomics features. A clinical-radiomics nomogram was developed by using independent predictors of poor outcome at 90 days through multivariate logistic regression analysis in the training cohort and was validated in the internal and external cohorts.

RESULTS

At 90 days, 200 of 435 (46.0%) patients had a poor prognosis. The clinical-radiomics nomogram was developed by six independent predictors namely midline shift, NCCT time from sICH onset, Glasgow Coma Scale score, serum glucose, uric acid, and Rad-score. In identifying patients with poor prognosis, the clinical-radiomics nomogram showed an area under the receiver operating characteristic curve (AUC) of 0.81 in the training cohort, an AUC of 0.78 in the internal validation cohort, and an AUC of 0.73 in the external validation cohort. The calibration curve revealed that the clinical-radiomics nomogram showed satisfactory calibration in the training and internal validation cohorts (both p > 0.05), but slightly poor agreement in the external validation cohort (p < 0.05).

CONCLUSIONS

The clinical-radiomics nomogram is a valid computer-aided tool that may provide personalized risk assessment of 90-day functional outcome for sICH patients.

KEY POINTS

• The proposed Rad-score was significantly associated with 90-day poor functional outcome in patients with sICH. • The clinical-radiomics nomogram showed satisfactory calibration and the most net benefit for discriminating 90-day poor outcome. • The clinical-radiomics nomogram may provide personalized risk assessment of 90-day functional outcome for sICH patients.

Computed Tomography Imaging Predictors of Intracerebral Hemorrhage Expansion

PURPOSE OF REVIEW

Hematoma expansion (HE) is strongly associated with poor clinical outcome and is a compelling target for improving outcome after intracerebral hemorrhage (ICH). Non-contrast computed tomography (NCCT) is widely used in clinical practice due to its faster acquisition at the presence of acute stroke. Recently, imaging markers on NCCT are increasingly used for predicting HE. We comprehensively review the current evidence on HE prediction using NCCT and provide a summary for assessment of these markers in future research studies.

RECENT FINDINGS

Predictors of HE on NCCT have been described in reports of several studies. The proposed markers, including swirl sign, blend sign, black hole sign, island sign, satellite sign, and subarachnoid extension, were all significantly associated with HE and poor outcome in their small sample studies after ICH. In summary, the optimal management of ICH remains a therapeutic dilemma. Therefore, using NCCT markers to select patients at high risk of HE is urgently needed. These markers may allow rapid identification and provide potential targets for anti-HE treatments in patients with acute ICH.

Exploration of Multiparameter Hematoma 3D Image Analysis for Predicting Outcome After Intracerebral Hemorrhage

BACKGROUND

Rapid diagnosis and proper management of intracerebral hemorrhage (ICH) play a crucial role in the outcome. Prediction of the outcome with a high degree of accuracy based on admission data including imaging information can potentially influence clinical decision-making practice.

METHODS

We conducted a retrospective multicenter study of consecutive ICH patients admitted between 2012-2017. Medical history, admission data, and initial head computed tomography (CT) scan were collected. CT scans were semiautomatically segmented for hematoma volume, hematoma density histograms, and sphericity index (SI). Discharge unfavorable outcomes were defined as death or severe disability (modified Rankin Scores 4-6). We compared (1) hematoma volume alone; (2) multiparameter imaging data including hematoma volume, location, density heterogeneity, SI, and midline shift; and (3) multiparameter imaging data with clinical information available on admission for ICH outcome prediction. Multivariate analysis and predictive modeling were used to determine the significance of hematoma characteristics on the outcome.

RESULTS

We included 430 subjects in this analysis. Models using automated hematoma segmentation showed incremental predictive accuracies for in-hospital mortality using hematoma volume only: area under the curve (AUC): 0.85 [0.76-0.93], multiparameter imaging data (hematoma volume, location, CT density, SI, and midline shift): AUC: 0.91 [0.86-0.97], and multiparameter imaging data plus clinical information on admission (Glasgow Coma Scale (GCS) score and age): AUC: 0.94 [0.89-0.99]. Similarly, severe disability predictive accuracy varied from AUC: 0.84 [0.76-0.93] for volume-only model to AUC: 0.88 [0.80-0.95] for imaging data models and AUC: 0.92 [0.86-0.98] for imaging plus clinical predictors.

CONCLUSIONS

Multiparameter models combining imaging and admission clinical data show high accuracy for predicting discharge unfavorable outcome after ICH.

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.

Development and external validation of a novel multihematoma fuzzy sign on computed tomography for predicting traumatic intraparenchymal hematoma expansion

Acute traumatic intraparenchymal hematoma (tICH) expansion is a devastating neurological complication that is associated with poor outcome after cerebral contusion. This study aimed to develop and validate a novel noncontrast computed tomography (CT) (NCCT) multihematoma fuzzy sign to predict acute tICH expansion. In this multicenter, prospective cohort study, multihematoma fuzzy signs on baseline CT were found in 212 (43.89%) of total 482 patients. Patients with the multihematoma fuzzy sign had a higher frequency of tICH expansion than those without (90.79% (138) vs. 46.71% (71)). The presence of multihematoma fuzzy sign was associated with increased risk for acute tICH expansion in entire cohort (odds ratio [OR]: 16.15; 95% confidence interval (CI) 8.85-29.47; P < 0.001) and in the cohort after propensity-score matching (OR: 9.37; 95% CI 4.52-19.43; P < 0.001). Receiver operating characteristic analysis indicated a better discriminative ability of the presence of multihematoma fuzzy sign for acute tICH expansion (AUC = 0.79; 95% CI 0.76-0.83), as was also observed in an external validation cohort (AUC = 0.76; 95% CI 0.67-0.84). The novel NCCT marker of multihematoma fuzzy sign could be easily identified on baseline CT and is an easy-to-use predictive tool for tICH expansion in the early stage of cerebral contusion.

A Triage Model for Interhospital Transfers of Low Risk Intracerebral Hemorrhage Patients

OBJECTIVES

Intracerebral hemorrhage comprises a large proportion of inter-hospital transfers to comprehensive stroke centers from centers without comprehensive stroke center resources despite lack of mortality benefit and low comprehensive stroke center resource utilization. The subset of patients who derive the most benefit from inter-hospital transfers is unclear. Here, we create a triage model to identify patients who can safely avoid transfer to a comprehensive stroke center.

MATERIALS AND METHODS

A retrospective cohort of spontaneous intracerebral hemorrhage patients transferred to our comprehensive stroke center from surrounding centers was used. Patients with early discharge from the Neuroscience Intensive Care Unit without use of comprehensive stroke center resources were identified as low risk, non-utilizers. Variables associated with this designation were used to develop and validate a triage model.

RESULTS

The development and replication cohorts comprised 358 and 99 patients respectively, of whom 78 (22%) and 26 (26%) were low risk, non-utilizers. Initial Glasgow Coma Scale and baseline hemorrhage volume were associated with low risk, non-utilizers in multivariate analysis. Initial Glasgow Coma Scale >13, intracerebral hemorrhage volume <15ml, absence of intraventricular hemorrhage, and supratentorial location had an area under curve, specificity, and sensitivity of 0.72, 91.4%, 52.6%, respectively, for identifying low risk, non-utilizers, and 0.75, 84.9%, 65.4%, respectively, in the replication cohort.

CONCLUSIONS

Spontaneous intracerebral hemorrhage patients with Glasgow Coma Scale >13, intracerebral hemorrhage volume <15 ml, absence of intraventricular hemorrhage, and supratentorial location might safely avoid inter-hospital transfer to a comprehensive stroke center. Validation in a prospective, multicenter cohort is warranted.

Hydrocephalus Growth: Definition, Prevalence, Association with Poor Outcome in Acute Intracerebral Hemorrhage

BACKGROUND/OBJECTIVES

To propose a novel definition for hydrocephalus growth and to further describe the association between hydrocephalus growth and poor outcome among patients with intracerebral hemorrhage (ICH).

METHODS

We analyzed consecutive patients who presented within 6 h after ICH ictus between July 2011 and June 2017. Follow-up CT scans were performed within 36 h after initial CT scans. The degree of hydrocephalus were evaluated by the hydrocephalus score of Diringer et al. The optimal increase of the hydrocephalus scores between initial and follow-up CT scan was estimated to define hydrocephalus growth. Poor long-term outcome was defined as a modified Rankin Scale of 4-6 at 3 months. Multivariate logistic regression analysis was performed to investigate the hydrocephalus growth for predicting 30-day mortality, 90-day mortality, and poor long-term outcome.

RESULTS

A total of 321 patients with ICH were included in the study. Of 64 patients with hydrocephalus growth, 34 (53.1%) patients presented with both concurrent hematoma expansion and intraventricular hemorrhage (IVH) growth. After adjusting for potential confounding factors, hydrocephalus growth independently predicted 30-day mortality, 90-day mortality, and 90-day poor long-term outcome in multivariate logistic regression analysis. Hydrocephalus growth showed higher accuracy for predicting 30-day mortality, 90-day mortality, and poor long-term outcome than IVH growth or hematoma expansion, respectively.

CONCLUSIONS

Hydrocephalus growth is defined by strongly predictive of short- or long-term mortality and poor outcome at 90 days, and might be a potential indicator for assisting clinicians for clinical decision-making.

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.

Accuracy of imaging markers on noncontrast computed tomography in predicting intracerebral hemorrhage expansion

Objectives Hematoma expansion (HE) is an important factor of unfavorable outcome in patients with intracerebral hemorrhage (ICH). Imaging markers on noncontrast computed tomography (NCCT) provide increasing value in the prediction of HE due to fast and easy-to-use advantages; however, the accuracy of NCCT-based prediction of intracerebral HE remains unclear. We aimed to investigate the predictive accuracy of NCCT markers for the evaluation of HE using a well-characterized ICH cohort. Methods We retrospectively analyzed 414 patients with spontaneous ICH, who underwent baseline CT within 6 h after symptom onset and follow-up CT within 24 h after ICH. Hematoma volumes were measured on baseline and follow-up CT images, and imaging features that predicted HE were analyzed. The test characteristics for the NCCT predictors were calculated. Results Of the 414 patients investigated, 63 presented blend sign, 45 showed black hole sign, 36 had island sign and 34 had swirl sign. In the 414 patients, 88 presented HE, the incidence was 21.26%. Of the 88 patients with HE, 22 presented blend sign, 11 showed black hole sign, 8 had swirl sign and 7 had island sign. The blend sign showed highest sensitivity (25.00%) and swirl sign showed the highest specificity (92.02%) among the four predictors. We noted excellent interobserver agreement for the identification of HE. Conclusion The four NCCT markers can predict HE with limited sensitivity, high specificity and good accuracy. This may be useful for prompt identification of patients at high risk of active bleeding, and prevention of over-treatment associated with HE. Abbreviations HE, hematoma expansion; ICH, intracerebral hemorrhage; NCCT, noncontrast computed tomography.

Benign Intracerebral Hemorrhage: A Population at Low Risk for Hematoma Growth and Poor Outcome

Background

To define benign intracerebral hemorrhage (ICH) and to investigate the association between benign ICH, hematoma expansion, and functional outcome.

Methods and Results

We analyzed a prospectively collected cohort of patients with ICH, who presented within 6 hours of symptom onset between July 2011 and February 2017 to a tertiary teaching hospital. Follow‐up computed tomographic scanning was performed within 36 hours after initial computed tomographic scanning. Benign ICH was operationally defined as homogeneous and regularly shaped small ICH. The presence of benign ICH was judged by 2 independent reviewers (Q.L., W.Y.) on the basis of the admission computed tomographic scan. Functional independence was defined as a modified Rankin Scale score of 0 to 2 at 3 months. The associations between benign ICH, hematoma expansion, and functional outcome were assessed by using multivariable logistic regression analyses. A total of 288 patients with ICH were included. Benign ICH was found in 48 patients (16.7%). None of the patients with benign ICH had early hematoma expansion. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of benign ICH for predicting functional independence at 3 months were 30.7%, 96.6%, 90.0%, 60.0%, and 0.637, respectively.

Conclusions

Patients with benign ICH are at low risk of hematoma expansion and poor outcome. These patients may be safe for less intensive monitoring and are unlikely to benefit from therapies aimed at preventing ICH expansion.

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.

Minimal Computed Tomography Attenuation Value Within the Hematoma is Associated with Hematoma Expansion and Poor Outcome in Intracerebral Hemorrhage Patients

BACKGROUND

Early hematoma expansion in intracerebral hemorrhage (ICH) patients is associated with poor outcome. We aimed to investigate whether the minimal computed tomography (CT) attenuation value predicted hematoma expansion and poor outcome.

METHODS

This study involved spontaneous ICH patients of two cohorts who underwent baseline CT scan within 6 h after ICH onset and follow-up CT scan within 24 h after initial CT scan. We determined the critical value of the minimal CT attenuation value via retrospective analysis of the data from a derivation cohort. Then, a prospective study on the validation cohort of three clinical centers was performed for determining the association between the minimal CT attenuation value and hematoma expansion as well as poor outcome (modified Rankin Scale scores > 3) at 90 days by using univariate and multivariate logistic regression analyses.

RESULTS

One hundred and forty eight ICH patients were included in the derivation cohort. Minimal CT attenuation value ≤ 31 Hounsfield units (HU) was demonstrated as the critical value to predict hematoma expansion by using receiver operating characteristic analysis. A total of 311 ICH patients were enrolled in the validation cohort, 86 (27.7%) and 133 (42.8%) of which were found hematoma expansion and poor outcome. Minimal CT attenuation value ≤ 31 HU was positive in 73 patients (23.5%). The multivariate logistic regression analysis demonstrated minimal CT attenuation value and minimal CT attenuation value ≤ 31 HU independently predicted hematoma expansion (p < 0.001) and poor outcome (p < 0.001). The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of minimal CT attenuation value ≤ 31 HU for hematoma expansion and poor outcome prediction were 64.0, 92.0, 75.3, 87.0, 84.2 and 45.1%, 92.7%, 82.2%, 69.3%, 72.3%, respectively.

CONCLUSIONS

The minimal CT attenuation value independently predicts early hematoma expansion and poor outcome in patients with ICH.

Expansion-Prone Hematoma: Defining a Population at High Risk of Hematoma Growth and Poor Outcome

BACKGROUND

Noncontrast computed tomography (CT) markers are increasingly used for predicting hematoma expansion. The aim of our study was to investigate the predictive value of expansion-prone hematoma in predicting hematoma expansion and outcome in patients with intracerebral hemorrhage (ICH).

METHODS

Between July 2011 and January 2017, ICH patients who underwent baseline CT scan within 6 h of symptoms onset and follow-up CT scan were recruited into the study. Expansion-prone hematoma was defined as the presence of one or more of the following imaging markers: blend sign, black hole sign, or island sign. The diagnostic performance of blend sign, black hole sign, island sign, and expansion-prone hematoma in predicting hematoma expansion was assessed. Predictors of hematoma growth and poor outcome were analyzed using multivariable logistical regression analysis.

RESULTS

A total of 282 patients were included in our final analysis. Of 88 patients with early hematoma growth, 69 (78.4%) had expansion-prone hematoma. Expansion-prone hematoma had a higher sensitivity and accuracy for predicting hematoma expansion and poor outcome when compared with any single imaging marker. After adjustment for potential confounders, expansion-prone hematoma independently predicted hematoma expansion (OR 28.33; 95% CI 12.95-61.98) and poor outcome (OR 5.67; 95% CI 2.82-11.40) in multivariable logistic model.

CONCLUSION

Expansion-prone hematoma seems to be a better predictor than any single noncontrast CT marker for predicting hematoma expansion and poor outcome. Considering the high risk of hematoma expansion in these patients, expansion-prone hematoma may be a potential therapeutic target for anti-expansion treatment in future clinical studies.

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.

Comparison of Ultra-Early Hematoma Growth and Common Noncontrast Computed Tomography Features in Predicting Hematoma Enlargement in Patients with Spontaneous Intracerebral Hemorrhage

OBJECTIVE

Ultra-early hematoma growth (uHG), the black hole sign, and the blend sign are common predictors of hematoma enlargement (HE). This study aimed to develop a new diagnostic criterion for predicting HE using uHG and to compare the accuracy of uHG, the black hole sign, and the blend sign in predicting HE in patients with spontaneous intracerebral hemorrhage (sICH).

METHODS

We retrospectively analyzed data of 920 patients with sICH from August 2013 to January 2018. Receiver operating characteristic curves were plotted to determine the optimal threshold values of uHG to predict HE. The effects of the black hole sign, blend sign, and uHG on HE were assessed using univariate and multivariate logistic regression models, and their prediction accuracies were analyzed using receiver operator analyses.

RESULTS

The black hole sign was identified in 131 patients, the blend sign in 163 patients, and uHG >6.46 mL/h in 441 patients. Logistic analysis showed that the black hole sign, blend sign, and uHG >6.46 mL/h were independent predictors of HE. The sensitivity values of uHG >6.46 mL/h, the black hole sign, and the blend sign were 70.43%, 24.19%, and 36.56%, respectively, and specificity values were 57.77%, 88.28%, and 87.06%, respectively. uHG had the greatest area under the curve. The black hole and blend signs were more commonly found in patients with uHG >6.46 mL/h (P < 0.001).

CONCLUSIONS

uHG >6.46 mL/h was the optimal predictor used for identifying patients at high risk of developing HE. A greater uHG value was associated with an increased prevalence of the black hole and blend signs.

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

Background and Purpose- Besides the established spot sign (SS) in computed tomography angiography (CTA), there is growing evidence that different imaging markers in noncontrast CT offer great value for outcome prediction in patients with intracerebral hemorrhage (ICH). However, it is unclear how the concurrent presence of each sign independently contributes to the predictive power of poor outcome. We, therefore, aimed to clarify the predictive value of 5 recently published noncontrast CT parameters (blend sign, black hole sign, island sign, hematoma heterogeneity, and hypodensities) and the established SS in 1 consecutive series of patients with ICH. Methods- Retrospective study of patients with ICH at 2 German tertiary stroke centers; inclusion criteria were (1) spontaneous ICH and (2) noncontrast CT and CTA performed on admission within 6 hours after onset of symptoms. We defined a binary outcome (good outcome [modified Rankin Scale score of ≤3] versus poor outcome [modified Rankin Scale score of >3]) at discharge. The predictive value of each sign was assessed in univariate and multivariable logistic regression models. Results- Of 201 patients with spontaneous ICH, 28 (13.9%) presented with black hole sign, 38 (18.9%) with blend sign, 120 (59.7%) with hypodensities, 97 (48.3%) with heterogeneous densities, 53 with island sign (26.4%), and 45 (22.4%) with SS. In univariable logistic regression, higher hematoma volume ( P<0.001), intraventricular hemorrhage ( P=0.002), and the presence of black hole sign/blend sign/hypodensities/island sign/SS/heterogeneous density (all P<0.001) on admission CT were associated with poor outcome. Multivariable analysis confirmed intraventricular hemorrhage (odds ratio, 2.20; P=0.025), higher hematoma volume (odds ratio, 1.02 per mL; P<0.019), the presence of hypodensities (odds ratio, 2.47; P=0.018), and SS (odds ratio, 12.22; P<0.001) as independent predictors of poor outcome. Conclusions- This study demonstrates the degree of interaction between 5 recent noncontrast CT imaging markers and SS and their individual contribution for outcome prediction in patients with ICH. Of the CT variables indicating poor outcome SS on CTA and hypodensities were the most reliable outcome predictors.

Standards for Detecting, Interpreting, and Reporting Noncontrast Computed Tomographic Markers of Intracerebral Hemorrhage Expansion

Significant hematoma expansion (HE) affects one-fifth of people within 24 hours after acute intracerebral hemorrhage (ICH), and its prevention is an appealing treatment target. Although the computed tomography (CT)-angiography spot sign predicts HE, only a minority of ICH patients receive contrast injection. Conversely, noncontrast CT (NCCT) is used to diagnose nearly all ICH, so NCCT markers represent a widely available alternative for prediction of HE. However, different NCCT signs describe similar features, with lack of consensus on the optimal image acquisition protocol, assessment, terminology, and diagnostic criteria. In this review, we propose practical guidelines for detecting, interpreting, and reporting NCCT predictors of HE. ANN NEUROL 2019;86:480-492.

Hematoma location and morphology of anticoagulation-associated intracerebral hemorrhage

OBJECTIVE

To study hematoma location and morphology of intracerebral hemorrhage (ICH) associated with oral anticoagulants (OAC) and delineate causes and mechanism.

METHODS

We performed a systematic literature research and meta-analysis of studies comparing neuroimaging findings in patients with OAC-ICH compared to those with ICH not associated with OAC (non-OAC ICH). We calculated pooled risk ratios (RRs) for ICH location using the Mantel-Haenszel random-effects method and corresponding 95% confidence intervals (95% CI).

RESULTS

We identified 8 studies including 6,259 patients (OAC-ICH n = 1,107, pooled OAC-ICH population 17.7%). There was some evidence for deep ICH location (defined as ICH in the thalamus, basal ganglia, internal capsule, or brainstem) being less frequent in patients with OAC-ICH (OAC-ICH: 450 of 1,102/40.8% vs non-OAC ICH: 2,656 of 4,819/55.1%; RR 0.94, 95% CI 0.88-1.00, p = 0.05, I ² = 0%) while cerebellar ICH location was significantly more common in OAC-ICH (OAC-ICH: 111 of 1,069/10.4% vs non-OAC ICH: 326 of 4,787/6.8%; RR 1.45, 95% CI 1.12-1.89, p = 0.005, I ² = 21%) compared to non-OAC ICH. There was no statistically significant relationship to OAC use for lobar (OAC-ICH: 423 of 1,107/38.2% vs non-OAC ICH: 1,884 of 5,152/36.6%; RR 1.02, 95% CI 0.89-1.17, p = 0.75, I ² = 53%, p for heterogeneity = 0.04) or brainstem ICH (OAC-ICH: 36 of 546/6.6% vs non-OAC ICH: 172 of 2,626/6.5%; RR 1.04, 95% CI 0.58-1.87, p = 0.89, I ² = 59%, p for heterogeneity = 0.04). The risk for intraventricular extension (OAC-ICH: 436 of 840/51.9% vs non-OAC ICH: 1,429 of 3,508/40.7%; RR 1.26, 95% CI 1.16-1.36, p < 0.001, I ² = 0%) was significantly increased in patients with OAC-ICH. We found few data on ICH morphology in OAC-ICH vs non-OAC ICH.

CONCLUSION

The overrepresentation of cerebellar ICH location and intraventricular extension in OAC-ICH might have mechanistic relevance for the underlying arteriopathy, pathophysiology, or bleeding pattern of OAC-ICH, and should be investigated further.

Predictive Validity of Hypodensities on Noncontrast Computed Tomography for Hematoma Growth in Intracerebral Hemorrhage: a Meta-Analysis

OBJECTIVE

Intracerebral hemorrhage (ICH) is a type of stroke that leads to high mortality. Hematoma growth (HG) happens in about one third of all patients with ICH and is independently related to poor outcome. Previous studies have shown that an indicator on noncontrast computed tomography, called hypodensities, can predict HG in patients with ICH. Thus, this study was done to assess the predictive validity of this marker.

METHODS

Bibliographic databases were searched, without language restriction, for original investigation on hypodensities and HG in ICH. Data were extracted, and study quality was assessed by 2 reviewers independently. Pooled sensitivity, specificity, positive likelihood ratio (LR), negative LR, diagnostic odds ratio, and their 95% confidence intervals (CIs) were obtained. A summary receiver operating characteristic curve was depicted.

RESULTS

Five cohorts with 2157 patients in 4 studies were included in the present meta-analysis. The pooled sensitivity was 0.58 (95% CI 0.46-0.68) and the pooled specificity was 0.71 (95% CI 0.62-0.79). In addition, the pooled positive LR was 2.0 (95% CI 1.6-2.5) and the pooled negative LR was 0.60 (95% CI 0.49-0.73). The pooled diagnostic odds ratio was 3 (95% CI 2-5) and the area under summary receiver operating characteristic curve was 0.69 (95% CI 0.65-0.73).

CONCLUSIONS

This study suggests that hypodensities on noncontrast computed tomography can be helpful in HG prediction, although its pooled predictive values are not very satisfying in the current study. The role of hypodensities in predicting HG should be confirmed by further studies.