Early Hematoma Expansion Research Articles

A Five-Point Score Grading System for Predicting Early Hematoma Expansion in Patients with Spontaneous Intracerebral Hemorrhage

Background: Stroke is the second leading cause of mortality and morbidity worldwide, with spontaneous Intracranial Hemorrhage (ICH) accounting for 9%-27% of all strokes. The study evaluates clinical and NCCT markers to predict early Hematoma Expansion (HE) in patients with Intracranial Hemorrhage (ICH), focusing on directing management objectives. Methods: We prospectively evaluated 96 patients with spontaneous ICH who underwent a baseline NCCT within four hours of admission, followed by a follow-up scan within six hours or at the time of clinical deterioration, whichever was earlier. Hematoma volumes were determined using baseline and follow-up CT images and imaging characteristics that predicted HE were evaluated. A grading system score was created to predict HE. Results: Of the ninety-six patients studied, 29 displayed black hole signs, 31 had island signs and 22 had swirl signs on baseline NCCT. On follow-up scans, the total incidence of HE in ICH patients was 53 out of 96 (55.2%). The average baseline hematoma volume in HE patients was 44.1 ml, compared to 12.2 ml in non-hematoma expansion patients. Of the 53 patients with HE, 29 had black hole sign, 16 had swirl sign and 28 had island sign. A higher grading system score (P < 0.001) was associated with a higher likelihood of HE.

Combining 2.5D deep learning and conventional features in a joint model for the early detection of sICH expansion.

The study aims to investigate the potential of training efficient deep learning models by using 2.5D (2.5-Dimension) masks of sICH. Furthermore, it intends to evaluate and compare the predictive performance of a joint model incorporating four types of features with standalone 2.5D deep learning, radiomics, radiology, and clinical models for early expansion in sICH. A total of 254 sICH patients were enrolled retrospectively and divided into two groups according to whether the hematoma was enlarged or not. The 2.5D mask of sICH is constructed with the maximum axial, coronal and sagittal planes of the hematoma, which is used to train the deep learning model and extract deep learning features. Predictive models were built on clinic, radiology, radiomics and deep learning features separately and four type features jointly. The diagnostic performance of each model was measured using the receiver operating characteristic curve (AUC), Accuracy, Recall, F1 and decision curve analysis (DCA). The AUCs of the clinic model, radiology model, radiomics model, deep learning model, joint model, and nomogram model on the train set (training and Cross-validation) were 0.639, 0.682, 0.859, 0.807, 0.939, and 0.942, respectively, while the AUCs on the test set (external validation) were 0.680, 0.758, 0.802, 0.857, 0.929, and 0.926. Decision curve analysis showed that the joint model was superior to the other models and demonstrated good consistency between the predicted probability of early hematoma expansion and the actual occurrence probability. Our study demonstrates that the joint model is a more efficient and robust prediction model, as verified by multicenter data. This finding highlights the potential clinical utility of a multifactorial prediction model that integrates various data sources for prognostication in patients with intracerebral hemorrhage. The Critical Relevance Statement: Combining 2.5D deep learning features with clinic features, radiology markers, and radiomics signatures to establish a joint model enabling physicians to conduct better-individualized assessments the risk of early expansion of sICH.

Hybrid clinical-radiomics model based on fully automatic segmentation for predicting the early expansion of spontaneous intracerebral hemorrhage: A multi-center study

BackgroundEarly prediction of hematoma expansion (HE) is important for the development of therapeutic strategies for spontaneous intracerebral hemorrhage (sICH). Radiomics can help to predict early hematoma expansion in intracerebral hemorrhage. However, complex image processing procedures, especially hematoma segmentation, are time-consuming and dependent on assessor experience. We provide a fully automated hematoma segmentation method, and construct a hybrid predictive model for risk stratification of hematoma expansion. PurposeTo propose an automatic approach for predicting early hemorrhage expansion after spontaneous intracerebral hemorrhage using deep-learning and radiomics methods. MethodsA total of 258 patients with sICH were retrospectively enrolled for model construction and internal validation, while another two cohorts (n=87, 149) were employed for independent validation. For hemorrhage segmentation, an iterative segmentation procedure was performed to delineate the area using an nnU-Net framework. Radiomics models of intra-hemorrhage and multiscale peri-hemorrhage were established and evaluated, and the best discriminative-scale peri-hemorrhage radiomics model was selected for further analysis. Combining clinical factors and intra- and peri-hemorrhage radiomics signatures, a hybrid nomogram was constructed for the early HE prediction using multivariate logistic regression. For model validation, the receiver operating characteristic (ROC) curve analyses and DeLong test were used to evaluate the performances of the constructed models, and the calibration curve and decision curve analysis were performed for clinical application. ResultsOur iterative auto-segmentation model showed satisfactory results for hematoma segmentation in all four cohorts. The Dice similarity coefficient of this hematoma segmentation model reached 0.90, showing an expert-level accuracy in hematoma segmentation. The consumed time of the efficient delineation was significantly decreased, from 18 min to less than 2 min, with the assistance of the auto-segmentation model. The radiomics model of 2-mm peri-hemorrhage had a preferable area under ROC curve (AUC) of 0.840 (95 % confidence interval [CI]: 0.768, 0.912) compared with the original (0-mm dilatation) model with an AUC of 0.796 (95 % CI: 0.717, 0.875). The clinical–radiomics hybrid model showed better performances for HE prediction, with AUC of 0.853, 0.852, 0.772, and 0.818 in the training, internal validation, and independent validation cohorts 1 and 2, respectively. ConclusionsThe fully automatic clinical–radiomics model based on deep learning and radiomics exhibits a good ability for hematoma segmentation and a favorable performance in stratifying HE risks.

Advancements in prognostic markers and predictive models for intracerebral hemorrhage: from serum biomarkers to artificial intelligence models.

Intracerebral hemorrhage (ICH) is a severe form of stroke with high morbidity and mortality, accounting for 10-15% of all strokes globally. Recent advancements in prognostic biomarkers and predictive models have shown promise in enhancing the prediction and management of ICH outcomes. Serum sestrin2, a stress-responsive protein, has been identified as a significant prognostic marker, correlating with severity indicators such as NIHSS scores and hematoma volume. Its levels predict early neurological deterioration and poor prognosis, offering predictive capabilities comparable to traditional measures. Furthermore, a deep learning-based AI model demonstrated superior performance in predicting early hematoma enlargement, with higher sensitivity and specificity than conventional methods. Additionally, long-term outcome prediction models using CT radiomics and machine learning have achieved high accuracy, particularly with the Random Forest algorithm. These advancements underscore the potential of integrating novel biomarkers and advanced computational techniques to improve prognostication and management of ICH, aiming to enhance patient care and survival rates. The incorporation of serum sestrin2, AI, and machine learning in predictive models represents a significant step forward in the clinical management of ICH, offering new avenues for research and clinical application.

Continuous arterial blood pressure indices and early hematoma expansion in patients with spontaneous intracerebral hemorrhage

BackgroundBlood pressure variability (BPV) and its potential association with early hematoma expansion (HE) in intracerebral hemorrhage (ICH) remains to be fully elucidated. Our study explores the potential link between BPV within the first 24 h after admission and HE in ICH. MethodsIn a prospective cohort single-center study, we analyzed consecutive patients with spontaneous ICH. Continuous BP data via an arterial line extracted from the Intellispace Critical Care and Anesthesia information system (Philips Healthcare) were analyzed over 0–2, 0–8, 0–12, and 0–24 h intervals post-admission. BPV was assessed through successive variability (SV), standard deviation (SD), and coefficient of variation (CV) using all available BP measurements. Early HE was defined as an absolute [≥ 6 ml] or relative [≥ 33 %] increase in ICH volume on 24-hours follow-up brain imaging. Secondary endpoints were the influence of BP on admission and other potential risk factors for HE. ResultsAmong 305 ICH-patients (mean age ± SD 70.1 ± 14.9 years, 47.9 % female, median NIHSS 6 (3, 13), median ICH score 1 (1, 2)), 41 (13.4 %) experienced HE. HE-patients had higher NIHSS (p = 0.015), ICH-score (p = 0.005), ICH volume (p < 0.001) and higher pre-anticoagulation treatment (p = 0.004) on admission. There was no difference in BPV comparing ICH-patients with HE to those without. However, patients with HE had significantly lower diastolic BP (76.6 ± 14.8 vs. 86.3 ± 19.7 mmHg, p = 0.005) and MAP (103.2 ± 22.4 vs. 112.2 ± 22.6, p = 0.027) on admission. This pattern of lower diastolic BP persisted across the first 24 h. Logistic regression revealed larger ICH volume and pre-existing anticoagulation as significant predictors of HE, with higher initial diastolic BP reducing HE risk. Hemorrhages ≥ 30 cm3 showed significantly lower initial diastolic BP, MAP, and BPV across all time frames compared to ICH < 30 cm3. ConclusionsBPV within the first 24 h was not associated with HE. Lower diastolic BP on admission, anticoagulation use, and larger ICH volume are potential predictors for HE. However, larger hemorrhage size (>30 cm3) experienced lower absolute BP and BPV indices and worse clinical outcomes. These findings suggest a nuanced relationship between BP dynamics and ICH severity, underscoring the need for individualized BP management in acute ICH care. Further research is necessary to explore these relationships and optimize treatment strategies.

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

The relationship between early perihematomal edema (PHE) and hematoma expansion (HE) is unclear. We investigated this relationship in patients with acute spontaneous intracerebral hemorrhage (ICH), using radiomics. In this multicenter retrospective study, we analyzed 490 patients with spontaneous ICH who underwent non-contrast computed tomography within 6 h of symptom onset, with follow-up imaging at 24 h. We performed HE and PHE image segmentation, and feature extraction and selection to identify HE-associated optimal radiomics features. We calculated radiomics scores of hematoma (Radscores_HEA) and PHE (Radscores_PHE) and constructed a combined model (Radscore_HEA_PHE). Relationships of the PHE radiomics features or Radscores_PHE with clinical variables, hematoma imaging signs, Radscores_HEA, and HE were assessed by univariate, correlation, and multivariate analyses. We compared predictive performances in the training (n = 296) and validation (n = 194) cohorts. Shape_VoxelVolume and Shape_MinorAxisLength of PHE were identified as optimal radiomics features associated with HE. Radscore_PHE (odds ratio = 1.039, p = 0.032) was an independent HE risk factor after adjusting for the ICH onset time, Glasgow Coma Scale score, baseline hematoma volume, hematoma shape, hematoma density, midline shift, and Radscore_HEA. The areas under the receiver operating characteristic curve of Radscore_PHE in the training and validation cohorts were 0.808 and 0.739, respectively. After incorporating Radscore_PHE, the integrated discrimination improvements of Radscore_HEA_PHE in the training and validation cohorts were 0.009 (p = 0.086) and -0.011 (p < 0.001), respectively. Radscore_PHE, based on Shape_VoxelVolume and Shape_MinorAxisLength of PHE, independently predicts HE, while Radscore_PHE did not add significant incremental value to Radscore_HEA.

Application of deep learning and radiomics in the prediction of hematoma expansion in intracerebral hemorrhage: a fully automated hybrid approach

Spontaneous intracerebral hemorrhage (ICH) is the most severe form of stroke. The timely assessment of early hematoma enlargement and its proper treatment are of great significance in curbing the deterioration and improving the prognosis of patients with ICH. This study aimed to develop an automated hybrid approach to predict hematoma expansion in ICH. The transfer learning method was applied to build a hybrid model based on a convolutional neural network (CNN) to predict the expansion of hematoma. The model integrated (1) a CNN for automated hematoma segmentation and (2) a CNN-based classifier for hematoma expansion prediction that incorporated both 2-dimensional images and the radiomics features of the 3-dimensional hematoma shape. The radiomics feature module had the highest area under the receiver operating characteristic curve (AUC) of 0.58, a precision of 0, a recall of 0, and an average precision (AP) of 0.26. The ResNet50 and Inception_v3 modules had AUCs of 0.79 and 0.93, a precision of 0.56 and 0.86, a recall of 0.42 and 0.75, and an AP of 0.51 and 0.85, respectively. Radiomic with Inception_v3 and Radiomic with ResNet50 had AUCs of 0.95 and 0.81, a precision of 0.90 and 0.57, a recall of 0.79 and 0.17, and an AP of 0.87 and 0.69, respectively. A model using deep learning and radiomics was successfully developed. This model can reliably predict the hematoma expansion of ICH with a fully automated process based on non-contrast computed tomography imaging. Furthermore, the radiomics fusion with the Inception_v3 model had the highest accuracy.

Association between white blood cells and ultra-early hematoma growth in patients with spontaneous intracerebral hemorrhage

BackgroundUltra-early inflammatory reaction after spontaneous intracerebral hemorrhage (sICH) plays an important role in the coagulation process and is closely related to early hematoma expansion. However, the relationship between ultra-early hematoma growth (uHG) and ultra-early inflammatory reaction remains unknown. ObjectiveTo evaluate the association between ultra-early inflammatory indicators and uHG in patients with sICH. MethodsWe retrospectively included 225 patients with acute sICH who were divided into the uHG ≤4.7 ml/h group and the uHG >4.7 ml/h group, respectively. The uHG was defined as hematoma volume (milliliter) at the primary computed tomography (CT) scan divided by time (hour) from onset to the performance of primary CT within 6 h after onset. The white blood cells (WBC), blood hypersensitive C-reactive protein, National Institutes of Health Stroke Scale (NIHSS) score and other related baseline data were collected and compared between the two groups. The multivariate regression analysis and receiver operating characteristic (ROC) curve were used to evaluate the independent risk factors for uHG >4.7 ml/h. ResultsNIHSS score and WBC were independent risk factors for uHG in patients with acute sICH (OR 1.188, 95% CI: 1.111–1.271, p < 0.001; OR 1.151, 95% CI: 1.018–1.300, p = 0.024; respectively). The area under curve of ROC for WBC and NIHSS score was 0.658 and 0.754, respectively (all p < 0.001), while the WBC combined with NIHSS score was 0.773 (p < 0.001). ConclusionWBC count within 6h after onset might be an independent risk factor for the increase of uHG in patients with sICH.

Research on predicting hematoma expansion in spontaneous intracerebral hemorrhage based on deep features of the VGG-19 network.

To construct a clinical noncontrastive computed tomography (NCCT) deep learning joint model for predicting early hematoma expansion (HE) after cerebral hemorrhage (sICH) and evaluate its predictive performance. All 254 patients with primary cerebral hemorrhage from January 2017 to December 2022 in the General Hospital of the Western Theater Command were included. According to the criteria of hematoma enlargement exceeding 33% or the volume exceeding 6ml, the patients were divided into the HE group and the hematoma non-enlargement (NHE) group. Multiple models and the 10-fold cross-validation method were used to screen the most valuable features and model the probability of predicting HE. The area under the curve (AUC) was used to analyze the prediction efficiency of each model for HE. They were randomly divided into a training set of 204 cases in an 8:2 ratio and 50 cases of the test set. The clinical imaging deep feature joint model (22 features) predicted the area under the curve of HE as follows: clinical Navie Bayes model AUC 0.779, traditional radiology logistic regression (LR) model AUC 0.818, deep learning LR model AUC 0.873, and clinical NCCT deep learning multilayer perceptron model AUC 0.921. The combined clinical imaging deep learning model has a high predictive effect for early HE in sICH patients, which is helpful for clinical individualized assessment of the risk of early HE in sICH patients.

CT-based radiomics models predict spontaneous intracerebral hemorrhage expansion and are comparable with CT angiography spot sign.

This study aimed to investigate the efficacy of radiomics, based on non-contrast computed tomography (NCCT) and computed tomography angiography (CTA) images, in predicting early hematoma expansion (HE) in patients with spontaneous intracerebral hemorrhage (SICH). Additionally, the predictive performance of these models was compared with that of the established CTA spot sign. A retrospective analysis was conducted using CT images from 182 patients with SICH. Data from the patients were divided into a training set (145 cases) and a testing set (37 cases) using random stratified sampling. Two radiomics models were constructed by combining quantitative features extracted from NCCT images (the NCCT model) and CTA images (the CTA model) using a logistic regression (LR) classifier. Additionally, a univariate LR model based on the CTA spot sign (the spot sign model) was established. The predictive performance of the two radiomics models and the spot sign model was compared according to the area under the receiver operating characteristic (ROC) curve (AUC). For the training set, the AUCs of the NCCT, CTA, and spot sign models were 0.938, 0.904, and 0.726, respectively. Both the NCCT and CTA models demonstrated superior predictive performance compared to the spot sign model (all P < 0.001), with the performance of the two radiomics models being comparable (P = 0.068). For the testing set, the AUCs of the NCCT, CTA, and spot sign models were 0.925, 0.873, and 0.720, respectively, with only the NCCT model exhibiting significantly greater predictive value than the spot sign model (P = 0.041). Radiomics models based on NCCT and CTA images effectively predicted HE in patients with SICH. The predictive performances of the NCCT and CTA models were similar, with the NCCT model outperforming the spot sign model. These findings suggest that this approach has the potential to reduce the need for CTA examinations, thereby reducing radiation exposure and the use of contrast agents in future practice for the purpose of predicting hematoma expansion.

Value of Image Biomarkers Based on Dual-Energy Computed Tomography Angiography Material Separation Technique in Predicting Early Hematoma Expansion in Spontaneous Intracerebral Hemorrhage

ObjectiveThis study aims to investigate the connection between the leakage sign (LS) and hematoma expansion (HE) in cases of spontaneous intracerebral hemorrhage (ICH). The investigation employs dual-energy computed tomography angiography (DECTA). MethodsA prospective cohort study was conducted, in which clinical and DECTA imaging data were collected from ICH patients within 6 hours of onset between January 2021 and June 2023. Exposure factors included DE-LS and traditional imaging biomarkers. The occurrence of HE on CT rescanned within 24 hours was the observed outcome. Exposed and confounding factors were considered in both univariate and multivariate regression analyses based on the results. Logistic and adjusted Poisson regressions were employed, and odds ratios (OR) and relative risks (RR) were calculated with 95% confidence intervals (CI). ResultsThe study enrolled a total of 90 patients, of whom 32 cases manifested HE, while 58 cases did not exhibit HE. Univariate analysis revealed statistically significant differences in parameters such as admission SBP, CRP, GCS, baseline hematoma volume, and imaging biomarkers likeDE-SS, and DE-LS. The OR value of DE-LS was determined as 48.21, with an RR value of 7.51. Multivariate adjusted Poisson regression analysis demonstrated that DE-LS was a robust independent predictor(RR = 4.11, 95% CI: 1.49 - 11.35; P < 0.001). ConclusionsDECTA-based DE-LS stands out as an independent predictor of HE. The utilization of RR values over OR values is endorsed when assessing the risk of HE prediction.

Glibenclamide pretreatment attenuates early hematoma expansion of warfarin-associated intracerebral hemorrhage in rats by alleviating perihematomal blood–brain barrier dysfunction

BackgroundHematoma expansion is a determinant of poor outcome of intracerebral hemorrhage but occurs frequently, especially in warfarin-associated intracerebral hemorrhage (W-ICH). In the present study, we employ the warfarin-associated intracerebral hemorrhage (W-ICH) rat model, to explore the efficacy and potential mechanism of glibenclamide pretreatment on hematoma expansion after intracerebral hemorrhage, hoping to provide proof of concept that glibenclamide in stroke primary and secondary prevention is also potentially beneficial for intracerebral hemorrhage patients at early stage.MethodsIn the present study, we tested whether glibenclamide, a common hypoglycemic drug, could attenuate hematoma expansion in a rat model of W-ICH. Hematoma expansion was evaluated using magnetic resonance imaging; brain injury was evaluated by brain edema and neuronal death; and functional outcome was evaluated by neurological scores. Then blood–brain barrier integrity was assessed using Evans blue extravasation and tight junction-related protein.ResultsThe data indicated that glibenclamide pretreatment significantly attenuated hematoma expansion at 24 h after W-ICH, thus mitigating brain edema and neuronal death and promoting neurological function recovery, which may benefit from alleviating blood–brain barrier disruption by suppressing matrix metallopeptidase-9.ConclusionsThe results indicate that glibenclamide pretreatment in stroke primary and secondary prevention might be a promising therapy for hematoma expansion at the early stage of W-ICH.

Prediction of early hematoma expansion of spontaneous intracerebral hemorrhage based on deep learning radiomics features of noncontrast computed tomography.

Aimed to develop a nomogram model based on deep learning features and radiomics features for the prediction of early hematoma expansion. A total of 561 cases of spontaneous intracerebral hemorrhage (sICH) with baseline Noncontrast Computed Tomography (NCCT) were included. The metrics of hematoma detection were evaluated by Intersection over Union (IoU), Dice coefficient (Dice), and accuracy (ACC). The semantic features of sICH were judged by EfficientNet-B0 classification model. Radiomics analysis was performed based on the region of interest which was automatically segmented by deep learning. A combined model was constructed in order to predict the early expansion of hematoma using multivariate binary logistic regression, and a nomogram and calibration curve were drawn to verify its predictive efficacy by ROC analysis. The accuracy of hematoma detection by segmentation model was 98.2% for IoU greater than 0.6 and 76.5% for IoU greater than 0.8 in the training cohort. In the validation cohort, the accuracy was 86.6% for IoU greater than 0.6 and 70.0% for IoU greater than 0.8. The AUCs of the deep learning model to judge semantic features were 0.95 to 0.99 in the training cohort, while in the validation cohort, the values were 0.71 to 0.83. The deep learning radiomics model showed a better performance with higher AUC in training cohort (0.87), internal validation cohort (0.83), and external validation cohort (0.82) than either semantic features or Radscore. The combined model based on deep learning features and radiomics features has certain efficiency for judging the risk grade of hematoma. Our study revealed that the deep learning model can significantly improve the work efficiency of segmentation and semantic feature classification of spontaneous intracerebral hemorrhage. The combined model has a good prediction efficiency for early hematoma expansion. • We employ a deep learning algorithm to perform segmentation and semantic feature classification of spontaneous intracerebral hemorrhage and construct a prediction model for early hematoma expansion. • The deep learning radiomics model shows a favorable performance for the prediction of early hematoma expansion. • The combined model holds the potential to be used as a tool in judging the risk grade of hematoma.

Prognostic Value of Red Blood Cell Distribution Width and Hemoglobin in Patients with Spontaneous Intracerebral Hemorrhage.

The association between baseline red blood cell distribution width (RDW) and hemoglobin levels and outcomes after acute intracerebral hemorrhage (ICH) is not well studied. We aimed to investigate the association between baseline RDW and hemoglobin levels with early hematoma expansion (HE) and mortality at 3 months and 1 year in acute ICH patients. A total of 393 ICH patients from January 2014 to February 2019 were included. Patients were divided into four groups based on quartiles of RDW and hemoglobin levels at admission, respectively. Logistic regression models were used to estimate the effect of the levels of RDW and hemoglobin on early HE (absolute hematoma growth >6 mL from baseline to follow-up) and allcaused mortality at 3 months and 1 year. There were no significant associations between baseline RDW and hemoglobin levels and early HE. The 3-month mortality (adjusted odds ratio [OR] 2.88; 95% confidence intervals [CI] 0.96-8.64) and 1-year mortality (adjusted OR 3.16, 95% CI 1.08-9.21) was significantly higher in patients with the highest RDW level (Q4) compared to those with the lowest RDW level (Q1). Moreover, patients with the lowest hemoglobin level were significantly associated with increased odds of all-cause mortality at 3-month (adjusted OR 3.95, 95% CI 1.26-12.4) and 1-year (adjusted OR 4.42, 95% CI 1.56-12.5) compared to those with highest hemoglobin level. In patients with acute ICH, a higher level of RDW at admission significantly increased the risk of all-cause mortality at 1 year. Moreover, a decreased hemoglobin level at admission was also associated with a higher risk of all-cause mortality at 3 months and 1 year.

Predictive value of hyperglycemia on prognosis in spontaneous intracerebral hemorrhage patients

BackgroundSpontaneous intracerebral hemorrhage (ICH) is the second most common cause of stroke and accounts for approximately 15–20% of all new stroke cases. Hematoma expansion is a potentially important therapeutic target that is amenable to treatment and independently predict outcome. Hyperglycemia is frequent in ICH patients, and affects cerebrovascular function, increasing the risk of cerebral vascular rupture. We recruited 170 ICH patients to explore the high risk factors of mortality and the association between hyperglycemia and early hematoma expansion. MethodsA retrospective analysis of 170 patients with ICH who were grouped by survival and blood glucose level, death group (35 cases) and survival group (135 cases); 77 cases in the hyperglycemic group and 93 cases in the normoglycemic group. Recorded parameters, such as age, gender, past medical history, blood glucose, serum calcium, hematoma volume, and hematoma expansion. Group comparison used t-test, rank sum test and Fisher exact test. After these, logistic regression analysis and receiver operating characteristic (ROC) curves were performed. ResultsAmong 170 ICH subjects(130 males and 40 females)，35 died and 77 exhibited hyperglycaemia. Compared with the survival group, the death group presented with higher Original Intracerebral Hemorrhage Scale (OICH) score, greater blood glucose, larger hemorrhage volume and lower Glasgow Coma Scale (GCS) score. The occurrence of hematoma expansion and massive hemorrhage volume in the hyperglycemic group were higher than in the normoglycemic group(P < 0.05). After adjustment for confounders variables, multivariate logistic analysis showed that blood glucose was an independent predictor of hematoma expansion (adjusted odd ratio:8.04, 95%CI:3.89–16.63, P < 0.01). Fasting blood glucose had better predictive value for hematoma expansion (AUC:0.95, 95%CI:0.92–0.99, P < 0.01). ConclusionHyperglycemia is associated with higher mortality risk and could be a potential marker in the prediction of hematoma expansion.

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

This study aimed to develop a prediction score named inflammatory score based on proper integration of several inflammatory markers and investigate whether it was associated with hematoma expansion and poor outcomes in patients with intracerebral hemorrhage (ICH). This study involved a consecutive series of spontaneous ICH patients of two cohorts admitted within 24 hours after symptom onset. Inflammatory score (0-9) was developed with the combination of neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, monocyte-to-lymphocyte ratio, systemic immune-inflammation index, lactate dehydrogenase, and C-reactive protein. The authors investigated the association between inflammatory score and hematoma expansion and poor outcomes by using univariate and multivariate logistic regression analyses. The optimal cutoff point of inflammatory score was determined by receiver operating characteristic analysis in the development cohort and then validated. A total of 301 and 154 ICH patients were enrolled in the development and validation cohorts. Inflammatory score was significantly higher in patients with hematoma expansion and poor outcomes. The multivariate logistic regression analysis revealed inflammatory score was independently associated with hematoma expansion, secondary neurological deterioration within 48 hours, 30-day mortality, and 3-month poor modified Rankin scale (4-6). The diagnostic accuracy of inflammatory score exhibited by area under the curve showed numerically or statistically higher than most of the individual indicators. Moreover, inflammatory score greater than or equal to 5 was selected as the optimal cutoff point, which was further prospectively validated with high diagnostic accuracy. The inflammatory score is a reliable predictor for early hematoma expansion and short-term and long-term poor outcomes with good diagnostic accuracies in ICH patients.

Initial investigation of predicting hematoma expansion for intracerebral hemorrhage using imaging biomarkers and machine learning.

Intracerebral Hemorrhage (ICH) is one of the most devastating types of strokes with mortality and morbidity rates ranging from about 51%-65% one year after diagnosis. Early hematoma expansion (HE) is a known cause of worsening neurological status of ICH patients. The goal of this study was to investigate whether non-contrast computed tomography imaging biomarkers (NCCT-IB) acquired at initial presentation can predict ICH growth in the acute stage. We retrospectively collected NCCT data from 200 patients with acute (<6 hours) ICH. Four NCCT-IBs (blending region, dark hole, island, and edema) were identified for each hematoma, respectively. HE status was recorded based on the clinical observation reported in the patient chart. Supervised machine learning models were developed, trained, and tested for 15 different input combinations of the NCCT-IBs to predict HE. Model performance was assessed using area under the receiver operating characteristic curve and probability for accurate diagnosis (PAD) was calculated. A 20-fold Monte-Carlo cross validation was implemented to ensure model reliability on a limited sample size of data, by running a myriad of random training/testing splits. The developed algorithm was able to predict expansion utilizing all four inputs with an accuracy of 70.17%. Further testing of all biomarker combinations yielded P AD ranging from 0.57, to 0.70. Specific attributes of ICHs may influence the likelihood of HE and can be evaluated via a machine learning algorithm. However, certain parameters may differ in importance to reach accurate conclusions about potential expansion.

Early Deterioration, Hematoma Expansion, and Outcomes in Deep Versus Lobar Intracerebral Hemorrhage: The FAST Trial.

In patients with intracerebral hemorrhage (ICH), it is unclear whether early neurological deterioration, hematoma expansion (HE), and outcome vary by supratentorial ICH location (deep versus lobar). Herein, we assessed these relationships in a clinical trial cohort that underwent brain imaging early after symptom onset. We hypothesized that HE would occur more frequently, and outcome would be worse in patients with deep ICH. We performed a post hoc analysis of the FAST (Factor-VII-for-Acute-Hemorrhagic-Stroke-Treatment) trial including all patients with supratentorial hemorrhage. Enrolled patients underwent brain imaging within 3 hours of symptom onset and 24 hours after randomization. Multivariable regression was used to test the association between ICH location and 3 outcomes: HE (increase of ≥33% or 6mL), early neurological deterioration (decrease in Glasgow Coma Scale score ≥2 points or increase in National Institutes of Health Stroke Scale ≥4 points within 24 hours of admission), and 90-day outcome (modified Rankin Scale). Of 841 FAST trial patients, we included 728 (mean age 64 years, 38% women) with supratentorial hemorrhages (deep n=623, lobar n=105). HE (44 versus 27%, P=0.001) and early neurological deterioration (31 versus 17%, P=0.001) were more common in lobar hemorrhages. Deep hemorrhages were smaller than lobar hemorrhages at baseline (12 versus 35mL, P<0.001) and 24 hours (14 versus 38mL, P<0.001). Unadjusted 90-day outcome was worse in lobar compared with deep ICH (median modified Rankin Scale score 5 versus 4, P=0.03). However, when adjusting for variables included in the ICH score including ICH volume, deep location was associated with worse and lobar location with better outcome (odds ratio lobar location, 0.58 [95% CI, 0.38-0.89]; P=0.01). In this secondary analysis of randomized trial patients, lobar ICH location was associated with larger ICH volume, more HE and early neurological deterioration, and worse outcome than deep ICH. After adjustment for prognostic variables, however, deep ICH was associated with worse outcome, likely due to their proximity to eloquent brain structures.

Establishment of a Scale for Predicting Early Hematoma Enlargement of Spontaneous Intracerebral Hemorrhage Based on Non-Contrast CT Signs.

This study aimed to optimize the Spontaneous intracerebral hemorrhage (sICH) early hematoma expansion prediction scoring table based on the clinical data of sICH patients to adopt appropriate clinical treatment plans and improve the prognosis of sICH patients. A total of 150 patients with sICH were enrolled, and 44 had early hematoma expansion. According to the selection and exclusion criteria, the study subjects were screened, their NCCT characteristic signs and clinical data were analyzed statistically, and the prediction score table was established. The established prediction score was applied to the follow-up study cohort to conduct a pilot study, and the t-test and ROC curve were used to evaluate its predictive ability. Statistical analysis found that initial hematoma volume, GCS score, and NCCT special signs were independent risk factors for early hematoma expansion after sICH (P 0.05). Thus, a score table was established. Subjects with ≥10 were divided into high-risk group, 6-8 comprised the medium-risk group, and ≤4 were divided into low-risk group. Among 17 patients with acute sICH, 7 developed early hematoma enlargement. The prediction accuracy was 92.41% in the low-risk group, 98.06% in the medium-risk group, and 84.61% in the high-risk group. It shows the high prediction accuracy of the prediction rating table. This study established a sICH early hematoma expansion prediction score table based on the special signs of NCCT. This table has highly sensitive and accurate.

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

Background:Different from diabetic hyperglycemia, stress-induced hyperglycemia (SIH) can better reflect elevated blood glucose owing to intracerebral hemorrhage (ICH). However, studies about the outcome of ICH patients with SIH are still very limited.Aims:This study aimed to investigate whether SIH measured by stress-induced hyperglycemia ratio (SHR) was associated with hematoma expansion and poor outcomes in patients with ICH.Methods:A consecutive series of patients with spontaneous ICH from two clinical centers admitted within 24 h after symptom onset were enrolled for prospective analysis. SHR was defined as admission fasting blood glucose divided by estimated average glucose [1.59 × Hemoglobin A1c (%) − 2.59]. This study investigated the association between SHR and hematoma expansion, and short-term and long-term poor outcomes using univariate and multivariate logistic regression analyses.Results:A total of 313 ICH patients were enrolled in the study. SHR was markedly higher in patients with hematoma expansion and poor outcomes (p < 0.001). The multivariate logistic regression analysis demonstrated SHR independently associated with hematoma expansion (p < 0.001) and poor outcomes, including secondary neurological deterioration within 48 h, 30-day mortality, and 3-month poor modified Rankin Scale (mRS 4–6) (p < 0.001), while the blood glucose only predicted 30-day mortality. Meanwhile, the diagnostic accuracy of SHR exhibited by area under the curve in receiver operating characteristic analysis was statistically equal to or higher than the well-known predictors.Conclusion:SHR is a reliable predictor for early hematoma expansion and poor outcomes in patients with ICH.