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

OBJECTIVES: This study aimed to establish a hematoma expansion (HE) prediction model for hypertensive intracerebral hemorrhage (HICH) patients by combining CT radiomics, clinical information, and conventional imaging signs. METHODS: A retrospective continuous collection of HICH patients from three medical centers was divided into a training set (n = 555), a validation set (n = 239), and a test set (n = 77). Extract radiomics features from baseline CT plain scan images and combine them with clinical information and conventional imaging signs to construct radiomics models, clinical imaging sign models, and hybrid models, respectively. The models will be evaluated using the area under the curve (AUC), clinical decision curve analysis (DCA), net reclassification index (NRI), and integrated discrimination improvement (IDI). RESULTS: In the training, validation, and testing sets, the radiomics model predicts an AUC of HE of 0.885, 0.827, and 0.894, respectively, while the clinical imaging sign model predicts an AUC of HE of 0.759, 0.725, and 0.765, respectively. Glasgow coma scale score at admission, first CT hematoma volume, irregular hematoma shape, and radiomics score were used to construct a hybrid model, with AUCs of 0.901, 0.838, and 0.917, respectively. The DCA shows that the hybrid model had the highest net profit rate. Compared with the radiomics model and the clinical imaging sign model, the hybrid model showed an increase in NRI and IDI. CONCLUSION: The hybrid model based on CT radiomics combined with clinical and radiological factors can effectively individualize the evaluation of the risk of HE in patients with HICH. CLINICAL RELEVANCE STATEMENT: CT radiomics combined with clinical information and conventional imaging signs can identify HICH patients with a high risk of HE and provide a basis for clinical-targeted treatment. KEY POINTS: HE is an important prognostic factor in patients with HICH. The hybrid model predicted HE with training, validation, and test AUCs of 0.901, 0.838, and 0.917, respectively. This model provides a tool for a personalized clinical assessment of early HE risk.

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

INTRODUCTION: Accurate prediction of hematoma expansion (HE) in spontaneous intracerebral hemorrhage (sICH) is crucial for tailoring patient-specific treatments and improving outcomes. Recent advancements have yielded numerous HE risk factors and predictive models. This study aims to evaluate the characteristics and efficacy of existing HE prediction models, offering insights for performance enhancement. METHODS: A comprehensive search was conducted in PubMed for observational studies and randomized controlled trials focusing on HE prediction, written in English. The prediction models were categorized based on their incorporated features and modeling methodology. Rigorous quality and bias assessments were performed. A meta-analysis of studies reporting C-statistics was executed to assess and compare the performance of current HE prediction models. Meta-regression was utilized to explore heterogeneity sources. RESULTS: From 358 initial records, 22 studies were deemed eligible, encompassing traditional models, hematoma imaging feature models, and models based on artificial intelligence or radiomics. Meta-analysis of 11 studies, involving 12,087 sICH patients, revealed an aggregated C-statistic of 0.74 (95% CI: 0.69-0.78) across seven HE prediction models. Eight characteristics related to development cohorts were identified as key factors contributing to performance variability among these models. CONCLUSION: The findings indicate that the current predictive capacity for HE risk remains suboptimal. Enhanced accuracy in HE prediction is vital for effectively targeting patient populations most likely to benefit from tailored treatment strategies.

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

BACKGROUND: Hematoma expansion is a critical factor associated with increased mortality and adverse outcomes in patients with intracerebral hemorrhage (ICH). Identifying and preventing hematoma expansion early on is crucial for effective therapeutic intervention. This study aimed to investigate the potential association between the Red cell distribution width to lymphocyte ratio (RDWLR) and hematoma expansion in ICH patients. METHODS: We conducted a retrospective analysis of clinical data from 303 ICH patients treated at our department between May 2018 and May 2023. Demographic, clinical, radiological, and laboratory data, including RDWLR upon admission, were assessed. Binary logistic regression analysis was employed to determine independent associations between various variables and hematoma expansion. RESULTS: The study included 303 ICH patients, comprising 167 (55.1%) males and 136 (44.9%) females, with a mean age of 65.25 ± 7.32 years at admission. Hematoma expansion occurred in 73 (24.1%) cases. Multivariate analysis revealed correlations between hematoma volume at baseline (OR, 2.73; 95% CI: 1.45 -4,78; P < 0.001), admission systolic blood pressure (OR, 2.98 ; 95% CI: 1.54-4.98; P < 0.001), Glasgow Coma Scale (GCS) (OR, 1.58; 95% CI: 1.25-2.46; P = 0.017), and RDWLR (OR, 1.58; 95% CI: 1.13-2.85; P = 0.022) and hematoma expansion in these patients. CONCLUSIONS: Our findings suggest that RDWLR could serve as a new inflammatory biomarker for hematoma expansion in ICH patients. This cost-effective and readily available biomarker has the potential for early prediction of hematoma expansion in these patients.

Code-ICH: A New Paradigm for Emergency Intervention.

PURPOSE OF REVIEW: Intracerebral hemorrhage (ICH) is the most devastating type of stroke, causing widespread disability and mortality. Unfortunately, the acute care of ICH has lagged behind that of ischemic stroke. There is an increasing body of evidence supporting the importance of early interventions including aggressive control of blood pressure and reversal of anticoagulation in the initial minutes to hours of presentation. This review highlights scientific evidence behind a new paradigm to care for these patients called Code-ICH. RECENT FINDINGS: While numerous trials aimed at decreasing hematoma expansion through single interventions had failed to show statistically significant effects on primary outcomes, time-sensitive, multifaceted, bundled care approaches have recently shown substantial promise in improving functional outcomes in patients with ICH. The concept of Code-ICH can serve as a structural platform for the practice of acute care neurology to continuously measure its performance, reflect on best practices, advance care, and address disparities.

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

PURPOSE: To predict hematoma growth in intracerebral hemorrhage patients by combining clinical findings with non-contrast CT imaging features analyzed through deep learning. METHODS: Three models were developed to predict hematoma expansion (HE) in 572 patients. We utilized multi-task learning for both hematoma segmentation and prediction of expansion: the Image-to-HE model processed hematoma slices, extracting features and computing a normalized DL score for HE prediction. The Clinical-to-HE model utilized multivariate logistic regression on clinical variables. The Integrated-to-HE model combined image-derived and clinical data. Significant clinical variables were selected using forward selection in logistic regression. The two models incorporating clinical variables were statistically validated. RESULTS: For hematoma detection, the diagnostic performance of the developed multi-task model was excellent (AUC, 0.99). For expansion prediction, three models were evaluated for predicting HE. The Image-to-HE model achieved an accuracy of 67.3%, sensitivity of 81.0%, specificity of 64.0%, and an AUC of 0.76. The Clinical-to-HE model registered an accuracy of 74.8%, sensitivity of 81.0%, specificity of 73.3%, and an AUC of 0.81. The Integrated-to-HE model, merging both image and clinical data, excelled with an accuracy of 81.3%, sensitivity of 76.2%, specificity of 82.6%, and an AUC of 0.83. The Integrated-to-HE model, aligning closest to the diagonal line and indicating the highest level of calibration, showcases superior performance in predicting HE outcomes among the three models. CONCLUSION: The integration of clinical findings with non-contrast CT imaging features analyzed through deep learning showed the potential for improving the prediction of HE in acute spontaneous intracerebral hemorrhage patients.

Prothrombin complex concentrate administration timing in warfarin-associated intracranial hemorrhage.

INTRODUCTION: Guidelines recommend "rapid" and "urgent" reversal of anticoagulation for warfarin-associated intracranial hemorrhage (ICH) treatment; however, they do not specify goals for time-to-administration. There are limited studies evaluating time to reversal, or international normalized ratio (INR) correction, on hematoma expansion and outcomes in intervals of <4 h. The purpose of this study was to evaluate the association of 4-factor prothrombin concentrate (4F-PCC) time-to-administration on rates of achieving effective hemostasis, determined by hematoma expansion, for treatment of warfarin-associated ICH. METHODS: This was a retrospective, observational, single center study performed at a large community teaching hospital. Patients were stratified into three groups based on time of CT diagnosis of ICH to administration of 4F-PCC: <45 min, 45-90 min, and >90 min. The primary outcome was rates of achieving effective hemostasis in each group defined as a ≤20% increase in hematoma volume as estimated by a radiologist. RESULTS: A total of 227 patients were screened for inclusion with ultimately 39 being included. Baseline characteristics were similar between groups. The primary outcome was not significantly different among groups stratified by time to 4F-PCC administration of <45 min, 45-90 min, and >90 min (85.7% vs 73.3% vs 90%, p value 0.514). There was no difference among secondary outcomes between groups including in-hospital mortality, hospital length of stay (LOS), and intensive care unit LOS. CONCLUSION: There was no association with time-to-administration of 4F-PCC on rates of hemostasis achievement, defined as hematoma expansion of ≤20%, identified in this study.

Association Between Neutrophil-Lymphocyte Ratio and 30-Day Infection and Thrombotic Outcomes After Intraventricular Hemorrhage: A CLEAR III Analysis.

BACKGROUND: Serum neutrophil-lymphocyte ratio (NLR) is a surrogate marker for the inflammatory response after intracerebral hemorrhage (ICH) and is associated with perihematomal edema and long-term functional outcomes. Whether NLR is associated with short-term ICH complications is poorly understood. We hypothesized that NLR is associated with 30-day infection and thrombotic events after ICH. METHODS: We performed a post hoc exploratory analysis of the Clot Lysis: Evaluating Accelerated Resolution of Intraventricular Hemorrhage III trial. The study exposure was the serum NLR obtained at baseline and on days 3 and 5. The coprimary outcomes, ascertained at 30 days, were any infection and a thrombotic event, defined as composite of cerebral infarction, myocardial infarction, or venous thromboembolism; both infection and thrombotic event were determined through adjudicated adverse event reporting. Binary logistic regression was used to study the relationship between NLR and outcomes, after adjustment for demographics, ICH severity and location, and treatment randomization. RESULTS: Among the 500 patients enrolled in the Clot Lysis: Evaluating Accelerated Resolution of Intraventricular Hemorrhage III trial, we included 303 (60.6%) without missing data on differential white blood cell counts at baseline. There were no differences in demographics, comorbidities, or ICH severity between patients with and without data on NLR. In adjusted logistic regression models, NLR ascertained at baseline (odds ratio [OR] 1.03; 95% confidence interval [CI] 1.01-1.07, p = 0.03) and NLR ascertained at day 3 were associated with infection (OR 1.15; 95% CI 1.05-1.20, p = 0.001) but not with thrombotic events. Conversely, NLR at day 5 was associated with thrombotic events (OR 1.07, 95% CI 1.01-1.13, p = 0.03) but not with infection (OR 1.13; 95% CI 0.76-1.70, p = 0.56). NLR at baseline was not associated with either outcome. CONCLUSIONS: Serum NLR ascertained at baseline and on day 3 after randomization was associated with 30-day infection, whereas NLR obtained on day 5 was associated with thrombotic events after ICH, suggesting that NLR could be a potential early biomarker for ICH-related complications.

Hematoma expansion prediction based on SMOTE and XGBoost algorithm.

Hematoma expansion (HE) is a high risky symptom with high rate of occurrence for patients who have undergone spontaneous intracerebral hemorrhage (ICH) after a major accident or illness. Correct prediction of the occurrence of HE in advance is critical to help the doctors to determine the next step medical treatment. Most existing studies focus only on the occurrence of HE within 6 h after the occurrence of ICH, while in reality a considerable number of patients have HE after the first 6 h but within 24 h. In this study, based on the medical doctors recommendation, we focus on prediction of the occurrence of HE within 24 h, as well as the occurrence of HE every 6 h within 24 h. Based on the demographics and computer tomography (CT) image extraction information, we used the XGBoost method to predict the occurrence of HE within 24 h. In this study, to solve the issue of highly imbalanced data set, which is a frequent case in medical data analysis, we used the SMOTE algorithm for data augmentation. To evaluate our method, we used a data set consisting of 582 patients records, and compared the results of proposed method as well as few machine learning methods. Our experiments show that XGBoost achieved the best prediction performance on the balanced dataset processed by the SMOTE algorithm with an accuracy of 0.82 and F1-score of 0.82. Moreover, our proposed method predicts the occurrence of HE within 6, 12, 18 and 24 h at the accuracy of 0.89, 0.82, 0.87 and 0.94, indicating that the HE occurrence within 24 h can be predicted accurately by the proposed method.

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.

Time Course of Early Hematoma Expansion in Acute Spot-Sign Positive Intracerebral Hemorrhage: Prespecified Analysis of the SPOTLIGHT Randomized Clinical Trial.

BACKGROUND: In the SPOTLIGHT trial (Spot Sign Selection of Intracerebral Hemorrhage to Guide Hemostatic Therapy), patients with a computed tomography (CT) angiography spot-sign positive acute intracerebral hemorrhage were randomized to rFVIIa (recombinant activated factor VIIa; 80 µg/kg) or placebo within 6 hours of onset, aiming to limit hematoma expansion. Administration of rFVIIa did not significantly reduce hematoma expansion. In this prespecified analysis, we aimed to investigate the impact of delays from baseline imaging to study drug administration on hematoma expansion. METHODS: Hematoma volumes were measured on the baseline CT, early post-dose CT, and 24 hours CT scans. Total hematoma volume (intracerebral hemorrhage+intraventricular hemorrhage) change between the 3 scans was calculated as an estimate of how much hematoma expansion occurred before and after studying drug administration. RESULTS: Of the 50 patients included in the trial, 44 had an early post-dose CT scan. Median time (interquartile range) from onset to baseline CT was 1.4 hours (1.2-2.6). Median time from baseline CT to study drug was 62.5 (55-80) minutes, and from study drug to early post-dose CT was 19 (14.5-30) minutes. Median (interquartile range) total hematoma volume increased from baseline CT to early post-dose CT by 10.0 mL (-0.7 to 18.5) in the rFVIIa arm and 5.4 mL (1.8-8.3) in the placebo arm (P=0.96). Median volume change between the early post-dose CT and follow-up scan was 0.6 mL (-2.6 to 8.3) in the rFVIIa arm and 0.7 mL (-1.6 to 2.1) in the placebo arm (P=0.98). Total hematoma volume decreased between the early post-dose CT and 24-hour scan in 44.2% of cases (rFVIIa 38.9% and placebo 48%). The adjusted hematoma growth in volume immediately post dose for FVIIa was 0.998 times that of placebo ([95% CI, 0.71-1.43]; P=0.99). The hourly growth in FFVIIa was 0.998 times that for placebo ([95% CI, 0.994-1.003]; P=0.50; Table 3). CONCLUSIONS: In the SPOTLIGHT trial, the adjusted hematoma volume growth was not associated with Factor VIIa treatment. Most hematoma expansion occurred between the baseline CT and the early post-dose CT, limiting any potential treatment effect of hemostatic therapy. Future hemostatic trials must treat intracerebral hemorrhage patients earlier from onset, with minimal delay between baseline CT and drug administration. REGISTRATION: URL: https://www. CLINICALTRIALS: gov; Unique identifier: NCT01359202.

Using Noncontrast Computed Tomography to Improve Prediction of Intracerebral Hemorrhage Expansion.

BACKGROUND: Noncontrast computed tomography hypodensities are a validated predictor of hematoma expansion (HE) in intracerebral hemorrhage and a possible alternative to the computed tomography angiography (CTA) spot sign but their added value to available prediction models remains unclear. We investigated whether the inclusion of hypodensities improves prediction of HE and compared their added value over the spot sign. METHODS: Retrospective analysis of patients admitted for primary spontaneous intracerebral hemorrhage at the following 8 university hospitals in Boston, US (1994-2015, prospective), Hamilton, Canada (2010-2016, retrospective), Berlin, Germany (2014-2019, retrospective), Chongqing, China (2011-2015, retrospective), Pavia, Italy (2017-2019, prospective), Ferrara, Italy (2010-2019, retrospective), Brescia, Italy (2020-2021, retrospective), and Bologna, Italy (2015-2019, retrospective). Predictors of HE (hematoma growth >6 mL and/or >33% from baseline to follow-up imaging) were explored with logistic regression. We compared the discrimination of a simple prediction model for HE based on 4 predictors (antitplatelet and anticoagulant treatment, baseline intracerebral hemorrhage volume, and onset-to-imaging time) before and after the inclusion of noncontrast computed tomography hypodensities, using receiver operating characteristic curve and De Long test for area under the curve comparison. RESULTS: A total of 2465 subjects were included, of whom 664 (26.9%) had HE and 1085 (44.0%) had hypodensities. Hypodensities were independently associated with HE after adjustment for confounders in logistic regression (odds ratio, 3.11 [95% CI, 2.55-3.80]; P<0.001). The inclusion of noncontrast computed tomography hypodensities improved the discrimination of the 4 predictors model (area under the curve, 0.67 [95% CI, 0.64-0.69] versus 0.71 [95% CI, 0.69-0.74]; P=0.025). In the subgroup of patients with a CTA available (n=895, 36.3%), the added value of hypodensities remained statistically significant (area under the curve, 0.68 [95% CI, 0.64-0.73] versus 0.74 [95% CI, 0.70-0.78]; P=0.041) whereas the addition of the CTA spot sign did not provide significant discrimination improvement (area under the curve, 0.74 [95% CI, 0.70-0.78]). CONCLUSIONS: Noncontrast computed tomography hypodensities provided a significant added value in the prediction of HE and appear a valuable alternative to the CTA spot sign. Our findings might inform future studies and suggest the possibility to stratify the risk of HE with good discrimination without CTA.

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

BACKGROUND: Early hematoma expansion (HE) occurs in 20 to 40% of spontaneous intracerebral hemorrhage (ICH) patients and is a primary determinant of early deterioration and poor prognosis. Previous studies have shown that inflammation is a major pathological feature of ICH, and the neutrophil-to-platelet ratio (NPR) is a marker of systemic inflammation. Therefore, we aimed to assess the association between the NPR and HE in ICH patients. METHODS: We retrospectively collected and analyzed data from ICH patients who received treatment at our institution from January 2018 to November 2019. The NPR was calculated from the admission blood test. Brain computed tomography (CT) scans were performed at admission and repeated within 24 h. Hematoma growth was defined as relative growth > 33% or absolute growth > 6 ml. RESULTS: A total of 317 patients were enrolled in our study. Multivariate logistic regression analysis indicated that the NPR was an independent predictor of HE [odds ratio (OR) = 1.742; 95% CI: 1.508-2.012, p < 0.001]. Receiver operating characteristic (ROC) curve analysis revealed that the NPR could predict HE, with an area under the curve of 0.838 (95% CI, 0.788-0.888, p < 0.001). The best predictive cut-off of the NPR for HE was 5.47 (sensitivity, 75.3%; specificity, 77.6%). CONCLUSIONS: A high NPR was associated with an increased risk of HE in patients with ICH.

Direct bilirubin: A predictor of hematoma expansion after intracerebral hemorrhage.

BACKGROUND: Previous evidence demonstrated that several biomarkers involved in the pathological process of coagulation/hemostasis dysfunction, impairment of brain vascular integrity and inflammation are associated with hematoma expansion (HE) after intracerebral hemorrhage (ICH). We aimed to explore whether there were unreported laboratory biomarkers associated with HE that were readily and commonly available in clinical practice. METHODS: We retrospectively analyzed consecutive acute ICH patients from 2012 to 2020 with admission laboratory tests and baseline and follow-up computed tomography (CT) scans. Univariate and multivariate regression analyses were used to evaluate associations between conventional laboratory indicators and HE. The results were verified in a prospective validation cohort. The relationship of candidate biomarker and 3-month outcomes was also investigated and mediation analysis was undertaken to determine causal associations among candidate biomarker, HE and outcome. RESULTS: Of 734 ICH patients, 163 (22.2%) presented HE. Among the included laboratory indicators, higher direct bilirubin (DBil) was associated with HE (adjusted odds ratio [OR] of per 1.0 µmol/L change 1.082; 95% confidence interval [CI] 1.011-1.158). DBil >5.65 µmol/L was a predictor of HE in validation cohort. Higher DBil was also associated with poor 3-month outcomes. The mediation analysis indicated that the association of higher DBil and poor outcomes was partially mediated by HE. CONCLUSIONS: DBil is a predictor of HE and poor 3-month outcomes after ICH. DBil's metabolic process and involvement in the pathological mechanism of HE are likely to contribute to the association between DBil and HE. Interventions targeting DBil to improve post-ICH prognosis may be meaningful and worthy of further exploration.

Association Between Hyperacute Blood Pressure Variability and Hematoma Expansion After Intracerebral Hemorrhage: Secondary Analysis of the FAST-MAG Database.

BACKGROUND: Blood pressure variability (BPV) has emerged as a significant factor associated with clinical outcomes after intracerebral hemorrhage (ICH). Although hematoma expansion (HE) is associated with clinical outcomes, the relationship between BPV that encompasses prehospital data and HE is unknown. We hypothesized that BPV was positively associated with HE. METHODS: We analyzed 268 patients with primary ICH enrolled in the National Institutes of Health-funded Field Administration of Stroke Therapy-Magnesium (FAST-MAG) study who received head computed tomography or magnetic resonance imaging on arrival to the emergency department (ED) and repeat imaging within 6-48 h. BPV was calculated by standard deviation (SD) and coefficient of variation (CV) from prehospital data as well as systolic blood pressure (SBP) measurements taken on ED arrival, 15 min post antihypertensive infusion start, 1 h post maintenance infusion start, and 4 h after ED arrival. HE was defined by hematoma volume expansion increase > 6 mL or by 33%. Univariate logistic regression was used for presence of HE in quintiles of SD and CV of SBP for demographics and clinical characteristics. RESULTS: Of the 268 patients analyzed from the FAST-MAG study, 116 (43%) had HE. Proportions of patients with HE were not statistically significant in the higher quintiles of the SD and CV of SBP for either the hyperacute or the acute period. Presence of HE was significantly more common in patients on anticoagulation. CONCLUSIONS: Higher BPV was not found to be associated with occurrence of HE in the hyperacute or the acute period of spontaneous ICH. Further study is needed to determine the relationship.

Time Course and Clinical Significance of Hematoma Expansion in Moderate-to-Severe Traumatic Brain Injury: An Observational Cohort Study.

BACKGROUND: Preventing intracranial hematoma expansion has been advertised as a possible treatment opportunity in traumatic brain injury (TBI). However, the time course of hematoma expansion, and whether the expansion affects outcome, remains poorly understood. In light of this, the aim of this study was to use 3D volume rendering to determine how traumatic intracranial hematomas expand over time and evaluate its impact on outcome. METHODS: Single-center, population-based, observational cohort study of adults with moderate-to-severe TBI. Hematoma expansion was defined as the change in hematoma volume from the baseline computed tomography scan until the lesion had stopped progressing. Volumes were calculated by using semiautomated volumetric segmentation. Functional outcome was measured by using the 12 month Glasgow outcome scale (GOS). RESULTS: In total, 643 patients were included. The mean baseline hematoma volume was 4.2 ml, and the subsequent mean hematoma expansion was 3.8 ml. Overall, 33% of hematomas had stopped progressing within 3 h, and 94% of hematomas had stopped progressing within 24 h of injury. Contusions expanded significantly more, and for a longer period of time, than extra-axial hematomas. There was a significant dose-response relationship between hematoma expansion and 12 month GOS, even after adjusting for known outcome predictors, with every 1-ml increase in hematoma volume associated with a 6% increased risk of 1-point GOS deduction. CONCLUSIONS: Hematoma expansion is a driver of unfavorable outcome in TBI, with small changes in hematoma volume also impacting functional outcome. This study also proposes a wider window of opportunity to prevent lesion progression than what has previously been suggested.

A reassessment of hemoglobin and hematoma expansion in intracerebral hemorrhage.

BACKGROUND: In patients with spontaneous intracerebral hemorrhage (ICH), prior studies identified an increased risk of hematoma expansion (HE) in those with lower admission hemoglobin (Hgb) levels. We aimed to reproduce these findings in an independent cohort. METHODS: We conducted a cohort study of patients admitted to a Comprehensive Stroke Center for acute ICH within 24 hours of onset. Admission laboratory and CT imaging data on ICH characteristics including HE (defined as >33% or >6 mL), and 3-month outcomes were collected. We compared laboratory data between patients with and without HE and used multivariable logistic regression to determine associations between Hgb, HE, and unfavorable 3-month outcomes (modified Rankin Scale 4-6) while adjusting for confounders including anticoagulant use, and laboratory markers of coagulopathy. RESULTS: Among 345 patients in our cohort (mean [SD] age 72.9 [13.7], 49% male), 71 (21%) had HE. Patients with HE had similar Hgb versus those without HE (mean [SD] 13.1 [1.8] g/dl vs. 13.1 [1.9] g/dl, p=0.92). In fully adjusted multivariable models, Hgb was not associated with HE (OR per 1g/dl 1.01, 95% CI 0.86 -1.17, p = 0.94), however higher admission Hgb levels were associated with lower odds of unfavorable 3-month outcome (OR 0.83 per 1 g/dl Hgb, 95% CI 0.72-0.96, p=0.01). CONCLUSION: We did not confirm a previously reported association between admission Hgb and HE in patients with ICH, although Hgb and HE were both associated with poor outcome. These findings suggest that the association between Hgb and poor outcome is mediated by other factors.

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

PURPOSE: Rapid identification of hematoma expansion (HE) risk at baseline is a priority in intracerebral hemorrhage (ICH) patients and may impact clinical decision making. Predictive scores using clinical features and Non-Contract Computed Tomography (NCCT)-based features exist, however, the extent to which each feature set contributes to identification is limited. This paper aims to investigate the relative value of clinical, radiological, and radiomics features in HE prediction. METHODS: Original data was retrospectively obtained from three major prospective clinical trials ["Spot Sign" Selection of Intracerebral Hemorrhage to Guide Hemostatic Therapy (SPOTLIGHT)NCT01359202; The Spot Sign for Predicting and Treating ICH Growth Study (STOP-IT)NCT00810888] Patients baseline and follow-up scans following ICH were included. Clinical, NCCT radiological, and radiomics features were extracted, and multivariate modeling was conducted on each feature set. RESULTS: 317 patients from 38 sites met inclusion criteria. Warfarin use (p=0.001) and GCS score (p=0.046) were significant clinical predictors of HE. The best performing model for HE prediction included clinical, radiological, and radiomic features with an area under the curve (AUC) of 87.7%. NCCT radiological features improved upon clinical benchmark model AUC by 6.5% and a clinical & radiomic combination model by 6.4%. Addition of radiomics features improved goodness of fit of both clinical (p=0.012) and clinical & NCCT radiological (p=0.007) models, with marginal improvements on AUC. Inclusion of NCCT radiological signs was best for ruling out HE whereas the radiomic features were best for ruling in HE. CONCLUSION: NCCT-based radiological and radiomics features can improve HE prediction when added to clinical features.

Prediction of Hematoma Expansion in Hypertensive Intracerebral Hemorrhage by a Radiomics Nomogram.

Objective: To develop and validate a radiomics-based nomogram model which aimed to predict hematoma expansion (HE) in hypertensive intracerebral hemorrhage (HICH). Methods: Patients with HICH (n=187) were included from October 2017 to March 2022 in the Yongchuan Affiliated Hospital of Chongqing Medical University. Patients were randomly divided into a training set (n=130) and a validation set (n=57) in a ratio of 7:3. The radiomic features were extracted from the regions of interest (including main hematoma, the surrounding small hematoma(s) and perihematomal edema) in the first CT scan images. The variance threshold, SelectKBest and LASSO (least absolute shrinkage and selection operator), features were selected and the radiomics signature was built. Multivariate logistic regression was used to establish a nomogram based on clinical risk factors and the Rad-score. A receiver operating characteristic (ROC) curve was used to evaluate the generalization of the models' performance. The calibration curve and the Hosmer-Lemeshow test were used to assess the calibration of the predictive nomogram. And decision curve analysis (DCA) was used to evaluate the prediction model. Results: Thirteen radiomics features were selected to construct the radiomics signature, which has a robust association with HE. The radiomics model found that blend sign was a predictive factor of HE. The radiomics model ROC in the training set was 0.89 (95%CI 0.82-0.96) and was 0.82 (95%CI 0.60-0.93) in the validation set. The nomogram model was built using the combined prediction model based on radiomics and blend sign, and worked well in both the training set (ROC: 0.90[95%CI 0.83-0.96]) and the validation set (ROC: 0.88[95%CI 0.71-0.93]). Conclusion: The radiomic signature based on CT of HICH has high accuracy for predicting HE. The combined prediction model of radiomics and blend sign improves the prediction performance.

Baseline neutrophil-lymphocyte ratio can be associated with hematoma expansion in patients with intracerebral hemorrhage: a retrospective observational study.

BACKGROUND: Hematoma expansion can be related to increased mortality and poor clinical outcomes in patients with intracerebral hemorrhage (ICH). So, early identification and prevention of hematoma expansion can be considered as an important therapeutic aim. This study aimed to evaluate the hypothesis that the neutrophil to lymphocyte ratio (NLR) is associated with hematoma expansion in ICH patients. METHODS: We retrospectively evaluated the clinical data of a total of 221 patients with ICH who were treated in our department between April 2018 and April 2021. The demographic, clinical, radiological, and laboratory test data including the NLR upon admission were investigated. A binary logistic regression analysis was used to assess the independent associations between different variables and hematoma expansion. RESULTS: A total of 221 patients with ICH were included. There were 122 (55.2%) males and 99 (44.8%) females. The mean age (years) at admission was 66.43 ± 8.28. The hematoma expansion occurred in 57 (25.8%) cases. The results of the multivariate analysis showed that hematoma volume at baseline (OR, 3.12; 95% CI 1.78-5.02; P < 0.001), admission systolic blood pressure (OR, 2.87; 95% CI 1.79-4.34; P = 0.013), Glasgow Coma Scale (GCS) (OR, 1.94; 95% CI 1.45-2.93; P = 0.020), and NLR (OR, 1.74; 95% CI 1.16-2.60; P = 0.032) were correlated with hematoma expansion in these patients. CONCLUSIONS: Our findings suggest that NLR can be a predictor of hematoma expansion in patients with ICH. This cost-effective and easily available biomarker could be used to early prediction of hematoma expansion in these patients.

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

BACKGROUND: Irregular hematoma is considered as a risk sign of hematoma expansion. The aim of this study was to quantify hematoma irregularity with computed tomography based on 3D Slicer. METHODS: Patients with spontaneous intracerebral hemorrhage who underwent an initial and subsequent non-contrast computed tomography (CT) at a single medical center between January 2019 to January 2020 were retrospectively identified. The Digital Imaging and Communication in Medicine (DICOM) standard images were loaded into the 3D Slicer, and the surface area (S) and volume (V) of hematoma were calculated. The hematoma irregularity index (HII) was defined as [Formula: see text]. Logistic regression analyses and receiver operating characteristic (ROC) curve analysis were performed to assess predictive performance of HII. RESULTS: The enrolled patients were divided into those with hematoma enlargement (n = 36) and those without the enlargement (n = 57). HII in hematoma expansion group was 130.4 (125.1-140.0), and the index in non-enlarged hematoma group was 118.6 (113.5-122.3). There was significant difference in HII between the two groups (P < 0.01). Multivariate logistic regression analysis revealed that the HII was significantly associated with hematoma expansion before (odds ratio = 1.203, 95% confidence interval [CI], 1.115-1.298; P < 0.001) and after adjustment for age, hematoma volume, Glasgow Coma Scale score (odds ratio = 1.196, 95% CI, 1.102-1.298, P < 0.001). The area under the ROC curve was 0.86 (CI, 0.78-0.93, P < 0.01), and the best cutoff of HII for predicting hematoma growth was 123.8. CONCLUSION: As a quantitative indicator of irregular hematoma, HII can be calculated using the 3D Slicer. And the HII was independently correlated with hematoma expansion.