Enhanced Prediction of Malignant Cerebral Edema in Large Vessel Occlusion with Successful Recanalization Through Automated Weighted Net Water Uptake.

BACKGROUND: Malignant cerebral edema (MCE) is associated with both net water uptake (NWU) and infarct volume. We hypothesized that NWU weighted by the affected Alberta Stroke Program Early Computed Tomography Score (ASPECTS) regions could serve as a quantitative imaging biomarker of aggravated edema development in acute ischemic stroke with large vessel occlusion (LVO). The aim of this study was to evaluate the performance of weighted NWU (wNWU) to predict MCE in patients with mechanical thrombectomy (MT). METHODS: We retrospectively analyzed consecutive patients who underwent MT due to LVO. NWU was computed from nonenhanced computed tomography scans upon admission using automated ASPECTS software. wNWU was derived by multiplying NWU with the number of affected ASPECTS regions in the ischemic hemisphere. Predictors of MCE were assessed through multivariate logistic regression analysis and receiver operating characteristic curves. RESULTS: NWU and wNWU were significantly higher in MCE patients than in non-MCE patients. Vessel recanalization status influenced the performance of wNWU in predicting MCE. In patients with successful recanalization, wNWU was an independent predictor of MCE (adjusted odds ratio 1.61; 95% confidence interval [CI] 1.24-2.09; P < 0.001). The model integrating wNWU, National Institutes of Health Stroke Scale, and collateral score exhibited an excellent performance in predicting MCE (area under the curve 0.80; 95% CI 0.75-0.84). Among patients with unsuccessful recanalization, wNWU did not influence the development of MCE (adjusted odds ratio 0.99; 95% CI 0.60-1.62; P = 0.953). CONCLUSIONS: This study revealed that wNWU at admission can serve as a quantitative predictor of MCE in LVO with successful recanalization after MT and may contribute to the decision for early intervention.

Low blood flow ratio is associated with hemorrhagic transformation secondary to mechanical thrombectomy in patients with acute ischemic stroke.

BACKGROUND AND PURPOSE: A significant decrease of cerebral blood flow (CBF) is a risk factor for hemorrhagic transformation (HT) in acute ischemic stroke (AIS). This study aimed to ascertain whether the ratio of different CBF thresholds derived from computed tomography perfusion (CTP) is an independent risk factor for HT after mechanical thrombectomy (MT). METHODS: A retrospective single center cohort study was conducted on patients with AIS undergoing MT at the First Affiliated Hospital of Wenzhou Medical University from August 2018 to December 2023. The perfusion parameters before thrombectomy were obtained according to CTP automatic processing software. The low blood flow ratio (LFR) was defined as the ratio of brain volume with relative CBF <20 % over volume with relative CBF <30 %. HT was evaluated on the follow-up CT images. Binary logistic regression was used to analyze the correlation between parameters that differ between the two groups with regards to HT occurrence. The predictive efficacy was assessed utilizing the receiver operating characteristic curve. RESULTS: In total, 243 patients met the inclusion criteria. During the follow-up, 46.5 % of the patients (113/243) developed HT. Compared with the Non-HT group, the HT group had a higher LFR (0.47 (0.34-0.65) vs. 0.32 (0.07-0.56); P < 0.001). According to the binary logistic regression analysis, the LFR (aOR: 6.737; 95 % CI: 1.994-22.758; P = 0.002), Hypertension history (aOR: 2.231; 95 % CI: 1.201-4.142; P = 0.011), plasma FIB levels before MT (aOR: 0.641; 95 % CI: 0.456-0.902; P = 0.011), and the mismatch ratio (aOR: 0.990; 95 % CI: 0.980-0.999; P = 0.030) were independently associated with HT secondary to MT. The area under the curve of the regression model for predicting HT was 0.741. CONCLUSION: LFR, a ratio quantified via CTP, demonstrates potential as an independent risk factor of HT secondary to MT.

An Online Dynamic Radiomics-Clinical Nomogram to Predict Recurrence in Patients with Spontaneous Intracerebral Hemorrhage.

OBJECTIVE: Radiomics can reflect the heterogeneity within the focus. We aim to explore whether radiomics can predict recurrent intracerebral hemorrhage (RICH) and develop an online dynamic nomogram to predict it. METHODS: This retrospective study collected the clinical and radiomics features of patients with spontaneous intracerebral hemorrhage seen in our hospital from October 2013 to October 2016. We used the minimum redundancy maximum relevancy and the least absolute shrinkage and selection operator methods to screen radiomics features and calculate the Rad-score. We use the univariate and multivariate analyses to screen clinical predictors. Optimal clinical features and Rad-score were used to construct different logistics regression models called the clinical model, radiomics model, and combined-logistic regression model. DeLong testing was performed to compare performance among different models. The model with the best predictive performance was used to construct an online dynamic nomogram. RESULTS: Overall, 304 patients with intracerebral hemorrhage were enrolled in this study. Fourteen radiomics features were selected to calculate the Rad-score. The patients with RICH had a significantly higher Rad-score than those without (0.5 vs. -0.8; P< 0.001). The predictive performance of the combined-logistic regression model with Rad-score was better than that of the clinical model for both the training (area under the receiver operating curve, 0.81 vs. 0.71; P = 0.02) and testing (area under the receiver operating curve, 0.65 vs. 0.58; P = 0.04) cohorts statistically. CONCLUSIONS: Radiomics features were determined related to RICH. Adding Rad-score into conventional clinical models significantly improves the prediction efficiency. We developed an online dynamic nomogram to accurately and conveniently evaluate RICH.

Acute Phase Blood Glucose Levels and Functional Outcomes in Patients with Spontaneous Intracerebral Hemorrhage.

Objective: Post-stroke hyperglycemia as a common phenomenon is associated with unfavorable outcomes. Focusing on admission hyperglycemia, other markers of dysglycemia were overlooked. This study aimed to explore the contribution of acute phase blood glucose levels in combination with other radiological signs to the prognostication of functional outcomes in patients with spontaneous intracerebral hemorrhage (sICH). Methods: Consecutive patients with sICH with at least five random plasma glucose measurements and complete radiological data during hospitalization were included. We calculated the average, maximum, minimum, standard deviation, and coefficient of variation of blood glucose levels for each patient. Radiological data, including island, black hole, blend, and satellite signs were collected. Functional outcomes were evaluated using the Barthel index. Unfavorable outcomes were defined as a Barthel index score ≤ 60. Univariate and multivariate analyses were performed to identify independent predictors of unfavorable outcomes. Results: Two hundred and thirty-eight patients (mean age 58.5, 163 men and 75 women) were included, and 71 had a history of diabetes. Unfavorable outcomes occurred in 107 patients (45.0%) at 3 months. Multivariate logistic regression analysis demonstrated that maximum blood glucose levels (odds ratio, 1.256; 95% confidence interval, 1.124‒1.404; p < 0.001) and island sign (odds ratio, 2.701; 95% confidence interval, 1.322‒5.521; p = 0.006) were independent predictors of unfavorable outcomes in the nondiabetic group. Meanwhile, patients without diabetes who experienced hematoma expansion had higher average (p = 0.036) and maximum blood glucose levels (p = 0.014). Interpretation: Maximum blood glucose levels and island sign were independently associated with unfavorable outcomes in patients without diabetes, whereas no glycemic variability indices were associated with unfavorable outcomes. Glucose levels influenced hematoma expansion and functional outcomes, particularly in patients without diabetes with sICH. Thus, clinical management of blood glucose levels should be strengthened for patients with sICH with or without a history of diabetes.

Development and validation of a novel radiomics-clinical model for predicting post-stroke epilepsy after first-ever intracerebral haemorrhage.

OBJECTIVES: Post-stroke epilepsy (PSE) is associated with increased morbidity and mortality. This study aimed to develop and validate a novel prediction model combining clinical factors and radiomics features to accurately identify patients at high risk of developing PSE after intracerebral haemorrhage (ICH). METHODS: Researchers performed a retrospective medical chart review to extract derivation and validation cohorts of patients with first-ever ICH that attended two tertiary hospitals in China between 2010 and 2020. Clinical data were extracted from electronic medical records and supplemented by tele-interview. Predictive clinical variables were selected by multivariable logistic regression to build the clinical model. Predictive radiomics features were identified, and a Rad-score was calculated according to the coefficient of the selected feature. Both clinical variables and radiomic features were combined to build the radiomics-clinical model. Performances of the clinical, Rad-score, and combined models were compared. RESULTS: A total of 1571 patients were included in the analysis. Cortical involvement, early seizures within 7 days of ICH, NIHSS score, and ICH volume were included in the clinical model. Rad-score, instead of ICH volume, was included in the combined model. The combined model exhibited better discrimination ability and achieved an overall better benefit against threshold probability than the clinical model in the decision curve analysis (DCA). CONCLUSIONS: The combined radiomics-clinical model was better able to predict ICH-associated PSE compared to the clinical model. This can help clinicians better predict an individual patient's risk of PSE following a first-ever ICH and facilitate earlier PSE diagnosis and treatment. KEY POINTS: • Radiomics has not been used in predicting the risk of developing PSE. • Higher Rad-scores were associated with higher risk of developing PSE. • The combined model showed better performance of PSE prediction ability.

Multivariate and spatio-temporal groundwater pollution risk assessment: A new long-time serial groundwater environmental impact assessment system.

Groundwater pollution risk assessment is an important part of environmental assessment. Although it has been developed for many years, there has not yet been a multi-dimensional method that takes into account long time series and spatial factors. We proposed a new method combines the advantages of remote sensing cloud computing, long-term groundwater modeling simulation and GIS technology to solve it efficiently. A coastal industrial park in Hainan was used as the study area. The depth of groundwater level, rainfall, topography and geomorphology, soil moisture, pollution source, pollution toxicity and other more than 10 parameters were used as the indexes. A comprehensive model with remote sensing cloud computing, DRASTIC model and Modflow + MT3DMS was established to assess the pollution risk from 2014 to 2021. The multi-year results indicated that the risk assessment of groundwater pollution was usually on the vertical coastal direction, and the risk increased from far away to near coast. With the discharge of pollutants in the industrial park, the pollution risk in the area 5 km away from the centre increased year by year until it became stable in 2019, and the risk in the centre of the park reached 1 level, covered an area of up to 145400 square metres, accounted for 0.012% of the whole study area. The assessment results in 2020 and 2021 fluctuate slightly compared with those in 2019. Therefore, in terms of groundwater resource protection and resource management, it is necessary to focus on the detection of pollution in the coastal zone and the pollution within 5 km of the centre to strictly control pollution discharge. In this study, the comprehensive assessment includes surface indicators, subsurface indicators, and pollutant indicators. Finally, we achieve a multivariate, spatial and long time series groundwater pollution risk assessment system, which is a new groundwater environmental impact assessment (GEIA) system.

Moderate-Severe White Matter Lesion Predicts Delayed Intraventricular Hemorrhage in Intracerebral Hemorrhage.

BACKGROUND: Most existing studies have focused on the correlation between white matter lesion (WML) and baseline intraventricular hemorrhage (IVH) in patients with intracerebral hemorrhage (ICH), whereas few studies have investigated the relationship between WML severity and delayed IVH after admission. This study aimed to investigate the correlation between WML severity and delayed IVH and to verify the association between WML and baseline IVH. METHODS: A total of 480 patients with spontaneous ICH from February 2018 to October 2020 were selected. WML was scored using the Van Swieten Scale, with scores of 0-2 representing nonslight WML and scores of 3-4 representing moderate-severe WML. We determined the presence of IVH on baseline (< 6 h) and follow-up computed tomography (< 72 h) images. Univariate analysis and multiple logistic regression were used to analyze the influencing factors of baseline and delayed IVH. RESULTS: Among 480 patients with ICH, 172 (35.8%) had baseline IVH, and there was a higher proportion of moderate-severe WML in patients with baseline IVH (20.3%) than in those without baseline IVH (12.7%) (P = 0.025). Among 308 patients without baseline IVH, delayed IVH was found in 40 patients (12.9%), whose proportion of moderate-severe WML (25.0%) was higher than that in patients without delayed IVH (10.8%) (P = 0.012). Multiple logistic regression results showed that moderate-severe WML was independently correlated with baseline IVH (P = 0.006, odds ratio = 2.266, 95% confidence interval = 1.270-4.042) and delayed IVH (P = 0.002, odds ratio = 7.009, 95% confidence interval = 12.086-23.552). CONCLUSIONS: Moderate-severe WML was an independent risk factor for delayed IVH as well as baseline IVH.

Groundwater pollution risk assessment based on groundwater vulnerability and pollution load on an isolated island.

Prolonged leakage of surface pollutants has an impact on groundwater quality, especially in shallow groundwater. This paper proposes a new method combining Modflow and geographic information system data for groundwater pollution risk assessment. First, hydrogeological information of the study area was obtained through a field investigation and data referencing, and the groundwater flow field in the study area was constructed. The actual drilling water level data were then used for verification. A groundwater solute transport model was constructed based on the groundwater flow field, combined with pollution source sampling and detection data. Finally, based on the simulation of the migration value of pollutants, the groundwater in the study area was evaluated by constructing a groundwater pollution risk assessment system that combined groundwater vulnerability and pollution load. The results showed that in the study area, the heavy-risk area accounted for 38%, the high-risk area accounted for 29%, the medium-risk area accounted for 22%, and the low-risk area accounted for 11%. Approximately 70% of the area was rated as high-risk or above, indicating that the groundwater in the study area was more susceptible to surface pollutants. Once groundwater is contaminated, recovery is difficult; therefore, groundwater pollution risk assessments are necessary. The groundwater pollution risk assessment system constructed by the groundwater numerical simulation and multi-index comprehensive evaluation method has significance as a reference for regional groundwater pollution risk identification and scientific management.

Dilated Optic Nerve Sheath Diameter Predicts Poor Outcome in Acute Spontaneous Intracerebral Hemorrhage.

BACKGROUND AND PURPOSE: Optic nerve sheath diameter (ONSD) enlargement occurs in patients with intracerebral hemorrhage (ICH). However, the relationship between ONSD and prognosis of ICH is uncertain. This study aimed to investigate the predictive value of ONSD on poor outcome of patients with acute spontaneous ICH. METHODS: We studied 529 consecutive patients with acute spontaneous ICH who underwent initial CT within 6 h of symptom onset between October 2016 and February 2019. The ONSDs were measured 3 mm behind the eyeball on initial CT images. Poor outcome was defined as having a Glasgow Outcome Scale (GOS) score of 1-3, and favorable outcome was defined as having a GOS score of 4-5 at discharge. RESULTS: The ONSD of the poor outcome group was significantly greater than that of the favorable outcome group (5.87 ± 0.86 vs. 5.21 ± 0.69 mm, p < 0.001). ONSD was related to hematoma volume (r = 0.475, p < 0.001). Adjusting other meaningful predictors, ONSD (OR: 2.83; 95% CI: 1.94-4.15) was associated with poor functional outcome by multivariable logistic regression analysis. Receiver operating characteristic curve showed that the ONSD improved the accuracy of ultraearly hematoma growth in the prediction of poor outcome (AUC: 0.790 vs. 0.755, p = 0.016). The multivariable logistic regression model with all the meaningful predictors showed a better predictive performance than the model without ONSD (AUC: 0.862 vs. 0.831, p = 0.001). CONCLUSIONS: The dilated ONSD measured on initial CT indicated elevated intracranial pressure and poor outcome, so appropriate intervention should be taken in time.

Clinical-radiomics Nomogram for Risk Estimation of Early Hematoma Expansion after Acute Intracerebral Hemorrhage.

RATIONALE AND OBJECTIVES: Noncontrast CT-based radiomics signature has shown ability for detecting hematoma expansion (HE) in spontaneous intracerebral hemorrhage (ICH). We sought to compare its predictive performance with clinical risk factors and develop a clinical-radiomics nomogram to assess the risk of early HE. MATERIALS AND METHODS: In total, 1153 patients with ICH who underwent baseline cranial CT within 6 hours and follow-up scans within 72 hours of stroke onset were enrolled, of whom 864 (75%) were assigned to the derivation cohort and 289 (25%) to the validation cohort. Based on LASSO algorithm or stepwise logistic regression analysis, three models (clinical model, radiomics model, and hybrid model) were constructed to predict HE. The Akaike information criterion (AIC) and likelihood ratio test (LRT) were used for comparing the goodness of fit of the three models, and the AUC was used to evaluate their discrimination ability for HE. RESULTS: The hybrid model (AIC = 681.426; χ2= 128.779) was the optimal model with the lowest AIC and highest chi-square values compared to the radiomics model (AIC = 767.979; χ2 = 110.234) or the clinical model (AIC = 753.757; χ2 = 56.448). The radiomics model was superior in the prediction of HE to the clinical model in both derivation (p = 0.009) and validation (p = 0.022) cohorts. In both datasets, the clinical-radiomics nomogram showed satisfactory discrimination and calibration for detecting HE (AUC = 0.771, Sensitivity = 87.0%; AUC = 0.820, Sensitivity = 88.1%; respectively). CONCLUSION: Among patients with acute ICH, noncontrast CT-based radiomics model outperformed the clinical-only model in the prediction of HE, and the established clinical-radiomics nomogram with favorable performance can offer a noninvasive tool for the risk stratification of HE.

Short-term exposure to ambient particulate matter and outpatient visits for respiratory diseases among children: A time-series study in five Chinese cities.

There was limited evidence regarding the association between short-term exposure to ambient particulate matter (PM) and respiratory outpatient visits among children at a multicity level. In this study, a time-series study was conducted among children aged 0-14 years in five Chinese cities from 2013 to 2018. City-specific effects of fine particles (PM2.5), inhalable particles (PM10) and coarse particles (PM10-2.5) were estimated for time lags of zero up to seven previous days using the overdispersed generalized additive models after adjusting for time trends, meteorological variables, day of the week and holidays. Meta-analyses were applied to pool the overall effects, while the exposure-response (E-R) curves were evaluated using a cubic regression spline. The overall effects of PM were significantly associated with total and cause-specific respiratory outpatients among children, even at PM2.5 and PM10 levels below the current Chinese Ambient Air Quality Standards (CAAQS) Grade II. Each 10 µg/m3 increment in PM2.5, PM10 and PM10-2.5 at lag 07 was associated with a 1.39% (95% CI: 0.38%, 2.40%), 1.10% (95% CI: 0.38%, 1.83%) and 2.93% (95% CI: 1.05%, 4.84%) increase in total respiratory outpatients, respectively. An E-R relationship was observed except for PM2.5 in Beijing and PM10 and PM10-2.5 in Shanghai. The effects of PM were stronger in cold season in 3 southern cities, while it was stronger in transition season in 2 northern cities. In conclusion, short-term PM exposures were dose-responsive associated with increased respiratory outpatient visits among children, even for PM2.5 and PM10 levels below current CAAQS II in certain cities.

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.

Association Between Eosinophilic Leukocyte Count and Hematoma Expansion in Acute Spontaneous Intracerebral Hemorrhage.

Background/Objective: Hematoma expansion (HE) predicts poor outcome and is an appealing treatment target in spontaneous intracerebral hemorrhage (ICH). Clinical evidence has shown an association of HE with peripheral white blood cells (WBC) count, but the individual contributions of leukocyte subtypes between literatures are described inconsistently. Our aim was to determine the relationship between admission absolute and differential leukocyte counts and HE by using different growth definitions. Methods: We analyzed spontaneous ICH patients who underwent baseline cranial computed tomography and blood sampling within 6 h of stroke onset in our institution between September 2013 and August 2018. Hematoma volume was calculated using a semiautomated 3-dimensional reconstruction algorithm. According to commonly used absolute or relative growth definitions (>6 mL, >12.5 mL, or >33%), we defined 5 types of HE. A propensity score-matching analysis was performed to evaluate the influence of complete blood count components on HE across the various growth definitions. The receiver operating characteristic analysis assessed the predictive ability of leukocyte counts for HE. Results: A total of 1,066 patients were included, of whom 11-21% met the 5 HE definitions. After propensity score-matching, except using the definition of >12.5 mL growth or its combination with >33% growth, both WBC and neutrophil count were independently associated with reduced risk of HE (odds ratio [OR] for 103 cells increase; OR, 0.86-0.99; all p < 0.05) after adjusting confounders in multivariate analyses. However, monocyte count was correlated with increased risk of HE under the usage of >12.5 mL expansion definition only (OR, 1.43; p = 0.024). There was no association between lymphocyte count and HE (all p > 0.05). Regardless of the growth definition, admission eosinophil count was directly associated with the risk of HE (OR, 6.92-31.60; all p < 0.05), and was the best predictive subtype with area under the curve 0.64, sensitivity 69.5%, and specificity 58.9% at the optimal cut-off value of 45 cells/µL. Conclusions: Growth definition affects the relationship of HE with leukocyte subtypes counting. Eosinophil count robustly predicts HE, and may be a surrogate when using an inflammatory marker to help select acute ICH patients with high expansion risk for hemostasis treatment in clinical trial and practice.

Growth of HIV-uninfected children born to HIV-infected mothers in Guangdong, China: an 18-month longitudinal follow-up study.

BACKGROUND: To evaluate the early growth (weight and length) of HIV-exposed uninfected (HEU) children from the prevention of mother-to-child transmission (PMTCT) of human immunodeficiency virus (HIV) program in Guangdong Province, China. METHODS: A total of 731 HEU children were longitudinally followed up at 7 time points, with anthropometric measurement conducted of weight and length (supine) in the first 18 months. Z scores were calculated, with and without adjustment for gestational age. RESULTS: A total of 708 HEU children were included in the final follow-up cohort, and 105 (14.83%) children completed all 7 follow-up visits. The mean of adjusted weight-for-age Z scores in these children was above zero and showed a decreasing trend in 18 months. The mean of adjusted length-for-age Z scores showed a decreasing trend and was above zero in the first 12 months; this declined to under zero at age 18 months. The proportion of underweight was 0.28-2.19% and that of stunting was 0.71-4.63% at each follow-up month-age. Slower growth in HEU children was associated with no sustained food subside after 6 month, mothers' hemoglobin content less than 100 g/L during pregnancy, preterm birth, and low birth weight (p < 0.05). CONCLUSIONS: HEU children could catch up to WHO growth standards in first 18 months in Guangdong; however, growth declined after 12 months, and these children need sustained nutritional support.

Prediction of hematoma expansion in spontaneous intracerebral hemorrhage using support vector machine.

BACKGROUND: Spontaneous intracerebral hemorrhage (ICH) is a devastating disease with high mortality rate. This study aimed to predict hematoma expansion in spontaneous ICH from routinely available variables by using support vector machine (SVM) method. METHODS: We retrospectively reviewed 1157 patients with spontaneous ICH who underwent initial computed tomography (CT) scan within 6 h and follow-up CT scan within 72 h from symptom onset in our hospital between September 2013 and August 2018. Hematoma region was manually segmented at each slice to guarantee the measurement accuracy of hematoma volume. Hematoma expansion was defined as a proportional increase of hematoma volume > 33% or an absolute growth of hematoma volume > 6 mL from initial CT scan to follow-up CT scan. Univariate and multivariate analyses were performed to assess the association between clinical variables and hematoma expansion. SVM machine learning model was developed to predict hematoma expansion. FINDINGS: 246 of 1157 (21.3%) patients experienced hematoma expansion. Multivariate analyses revealed the following 6 independent factors associated with hematoma expansion: male patient (odds ratio [OR] = 1.82), time to initial CT scan (OR = 0.73), Glasgow Coma Scale (OR = 0.86), fibrinogen level (OR = 0.72), black hole sign (OR = 2.52), and blend sign (OR = 4.03). The SVM model achieved a mean sensitivity of 81.3%, specificity of 84.8%, overall accuracy of 83.3%, and area under receiver operating characteristic curve (AUC) of 0.89 in prediction of hematoma expansion. INTERPRETATION: The designed SVM model presented good performance in predicting hematoma expansion from routinely available variables. FUND: This work was supported by Health Foundation for Creative Talents in Zhejiang Province, China, Natural Science Foundation of Zhejiang Province, China (LQ15H180002), the Science and Technology Planning Projects of Wenzhou, China (Y20180112), Scientific Research Staring Foundation for the Returned Overseas Chinese Scholars of Ministry of Education of China, and Project Foundation for the College Young and Middle-aged Academic Leader of Zhejiang Province, China. The funders had no role in study design, data collection, data analysis, interpretation, writing of the report.

Quantitative assessment on blood-brain barrier permeability of acute spontaneous intracerebral hemorrhage in basal ganglia: a CT perfusion study.

PURPOSE: Blood-brain barrier (BBB) damage aggravates perihematomal edema, and edema volume predicts prognosis independently. But the BBB permeability at the late stage of acute intracerebral hemorrhage (ICH) patients is uncertain. We aimed to assess the BBB permeability of spontaneous basal ganglia ICH using computed tomographic perfusion (CTP) and investigates its relationship with hematoma and perihematomal edema volume. METHODS: We performed CTP on 54 consecutive ICH patients within 24 to 72 h after symptom onset. Permeability-surface area product (PS) derived from CTP imaging was measured in hematoma, "high-PS spot," perihematoma, normal-appearing, hemispheric, and contralateral regions. Hematoma and edema volumes were calculated from non-contrast CT. RESULTS: "High-PS spot" and perihematoma regions had higher PS than the contralateral regions (p < 0.001). Hematoma PS was lower than that in the contralateral regions (p < 0.001). Perihematoma PS of the large-hematoma group was higher than that of the small-hematoma group (p = 0.011). Perihematomal edema volume correlated positively with hematoma volume (ß = 0.864, p < 0.001) and perihematoma PS (ß = 0.478, p < 0.001). Perihematoma PS correlated positively with hematoma volume (ß = 0.373, p = 0.005). CONCLUSIONS: Locally elevated perihematoma PS was found in most spontaneous basal ganglia ICH patients within 24 to 72 h after symptom onset. Perihematoma PS was higher in larger hematomas and was associated with larger edema volume. At this period, BBB leakage is likely to be an important factor in edema formation.

Larger size ratio associated with the rupture of very small (≤3†mm) anterior communicating artery aneurysms.

BACKGROUND: Anterior communicating artery (AcoA) aneurysms have a high rupture risk, and ruptured AcoA aneurysms tend to be smaller than other intracranial aneurysms. We aimed to determine the incidence and morphologic predictors of aneurysm rupture of very small AcoA aneurysms. METHODS: We conducted a retrospective analysis of 519 consecutive patients with single AcoA aneurysms between December 2007 and February 2015 in our hospital. Aneurysm morphologies were re-measured using CT angiography images. Very small aneurysms were defined as those with a maximum size ≤3 mm, and small aneurysms were defined as those with a maximum size ≤5 mm. Multivariate regression analyses were used to determine the association between aneurysm morphology and aneurysm rupture status. RESULTS: Of the 474 ruptured AcoA aneurysms, 134 (28.3%) aneurysms were very small and 278 (58.6%) aneurysms were small. In the univariate analysis for very small aneurysms, larger aneurysm size (p=0.037), larger size ratio (p=0.002), higher aneurysm height (p=0.038), smaller vessel size (p=0.012), and dominant A1 segment configuration (p=0.011) were associated with aneurysm rupture. Multivariate analysis revealed that a larger size ratio was independently associated with the rupture status of the very small aneurysms (OR 3.69, 95% CI 1.5 to 9.0; p=0.004), and larger aneurysm size, larger size ratio, and dominant A1 segment configuration were associated with the rupture of small aneurysms. CONCLUSIONS: About one-third of ruptured AcoA aneurysms were very small. A larger size ratio, rather than other aneurysm morphologies, was independently associated with the rupture of very small AcoA aneurysms.

Computed Tomography Perfusion Deficits during the Baseline Period in Aneurysmal Subarachnoid Hemorrhage Are Predictive of Delayed Cerebral Ischemia.

BACKGROUND AND PURPOSE: Delayed cerebral ischemia (DCI) is a frequent and fearful complication following aneurysmal subarachnoid hemorrhage (aSAH). The aim of this study is to assess the diagnostic accuracy of computed tomography perfusion (CTP) during an admission baseline period for the prediction of DCI. METHODS: Fifty-four aSAH cases were screened by baseline CTP within 3 days after aSAH and were reexamined with CTP 7-17 days after aSAH. Relative cerebral blood volume, relative cerebral blood flow (CBF), and relative mean transit time were measured. DCI was confirmed by a combination of noncontrast CT, CTP reexamination, and clinical assessment of neurologic deficits. Quantitative baseline and reexamination CTP data for all patients were compared between DCI and without DCI groups using Student's t-tests. The quantitative baseline and reexamination CTP data of DCI patients were compared using paired Student's t-tests. The χ2 test was used to evaluate incidences of DCI between different baseline relative CBF levels. The optimal cutoff value for each parameter was established by receiver operating characteristic curve analysis. RESULTS: Of the patients included in this study, 33.3% (18 of 54) developed DCI. There was a significant difference in the incidence of DCI among different baseline relative CBF subsets (χ2 = 38.00, P < .05). A relative CBF of .84 had the highest specificity and sensitivity of predicting DCI. CONCLUSION: CTP parameters during the baseline period can be helpful for the early identification of aSAH patients who are at high risk for DCI.