Noncontrast Computed Tomography Markers of Cerebral Hemorrhage Expansion: Diagnostic Accuracy Meta-Analysis

2021 ◽  
pp. 174749302110616
Author(s):  
Arba Francesco ◽  
Rinaldi Chiara ◽  
Boulouis Gregoire ◽  
Fainardi Enrico ◽  
Charidimou Andreas ◽  
...  

Background and purpose Assess the diagnostic accuracy of noncontrast computed tomography (NCCT) markers of hematoma expansion in patients with primary intracerebral hemorrhage. Methods We performed a meta-analysis of observational studies and randomized controlled trials with available data for calculation of sensitivity and specificity of NCCT markers for hematoma expansion (absolute growth >6 or 12.5 mL and/or relative growth >33%). The following NCCT markers were analyzed: irregular shape, island sign (shape-related features); hypodensity, heterogeneous density, blend sign, black hole sign, and swirl sign (density-related features). Pooled accuracy values for each marker were derived from hierarchical logistic regression models. Results A total of 10,363 subjects from 23 eligible studies were included. Significant risk of bias of included studies was noted. Hematoma expansion frequency ranged from 7% to 40%, mean intracerebral hemorrhage volume from 9 to 27.8 ml, presence of NCCT markers from 9% (island sign) to 82% (irregular shape). Among shape features, sensitivity ranged from 0.32 (95%CI = 0.20–0.47) for island sign to 0.68 (95%CI = 0.57–0.77) for irregular shape, specificity ranged from 0.47 (95%CI = 0.36–0.59) for irregular shape to 0.92 (95%CI = 0.85–0.96) for island sign; among density features sensitivity ranged from 0.28 (95%CI = 0.21–0.35) for black hole sign to 0.63 (95%CI = 0.44–0.78) for hypodensity, specificity ranged from 0.65 (95%CI = 0.56–0.73) for heterogeneous density to 0.89 (95%CI = 0.85–0.92) for blend sign. Conclusion Diagnostic accuracy of NCCT markers remains suboptimal for implementation in clinical trials although density features performed better than shape-related features. This analysis may help in better tailoring patients’ selection for hematoma expansion targeted trials.

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Danfeng Zhang ◽  
Jigang Chen ◽  
Qiang Xue ◽  
Bingying Du ◽  
Ya Li ◽  
...  

Background and Purpose. Hematoma expansion (HE) is related to clinical deterioration after intracerebral hemorrhage (ICH) and noncontrast computed tomography (NCCT) signs are indicated as predictors for HE but with inconsistent conclusions. We aim to clarify the correlations of NCCT heterogeneity signs with HE by meta-analysis of related studies. Methods. PubMed, Embase, and Cochrane library were searched for eligible studies exploring the relationships between NCCT heterogeneity signs (hypodensity, mixed density, swirl sign, blend sign, and black hole sign) and HE. Poor outcome and mortality were considered as secondary outcomes. Odds ratio (OR) and its 95% confidence intervals (CIs) were selected as the effect size and combined using random effects model. Results. Fourteen studies were included, involving 3240 participants and 435 HEs. The summary results suggested statistically significant correlations of heterogeneity signs with HE (OR, 5.17; 95% CI, 3.72–7.19, P<0.001), poor outcome (OR, 3.60; 95% CI, 1.98–6.54, P<0.001), and mortality (OR, 4.64; 95%, 2.96–7.27, P<0.001). Conclusions. Our findings suggested that hematoma heterogeneity signs on NCCT were positively associated with the increased risk of HE, poor outcome, and mortality rate in ICH.


2018 ◽  
Vol 115 ◽  
pp. e711-e716 ◽  
Author(s):  
Jun Zheng ◽  
Zhiyuan Yu ◽  
Rui Guo ◽  
Hao Li ◽  
Chao You ◽  
...  

2021 ◽  
Vol 10 (3) ◽  
Author(s):  
Wen‐Song Yang ◽  
Shu‐Qiang Zhang ◽  
Yi‐Qing Shen ◽  
Xiao Wei ◽  
Li‐Bo Zhao ◽  
...  

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


2021 ◽  
Vol 13 ◽  
Author(s):  
Linyang Teng ◽  
Qianwei Ren ◽  
Pingye Zhang ◽  
Zhenzhou Wu ◽  
Wei Guo ◽  
...  

This study aims to develop and validate an artificial intelligence model based on deep learning to predict early hematoma enlargement (HE) in patients with intracerebral hemorrhage. A total of 1,899 noncontrast computed tomography (NCCT) images of cerebral hemorrhage patients were retrospectively analyzed to establish a predicting model and 1,117 to validate the model. And a total of 118 patients with intracerebral hemorrhage were selected based on inclusion and exclusion criteria so as to validate the value of the model for clinical prediction. The baseline noncontrast computed tomography images within 6 h of intracerebral hemorrhage onset and the second noncontrast computed tomography performed at 24 ± 3 h from the onset were used to evaluate the prediction of intracerebral hemorrhage growth. In validation dataset 1, the AUC was 0.778 (95% CI, 0.768–0.786), the sensitivity was 0.818 (95% CI, 0.790–0.843), and the specificity was 0.601 (95% CI, 0.565–0.632). In validation dataset 2, the AUC was 0.780 (95% CI, 0.761–0.798), the sensitivity was 0.732 (95% CI, 0.682–0.788), and the specificity was 0.709 (95% CI, 0.658–0.759). The sensitivity of intracerebral hemorrhage hematoma expansion as predicted by an artificial intelligence imaging system was 89.3%, with a specificity of 77.8%, a positive predictive value of 55.6%, a negative predictive value of 95.9%, and a Yoden index of 0.671, which were much higher than those based on the manually labeled noncontrast computed tomography signs. Compared with the existing prediction methods through computed tomographic angiography (CTA) image features and noncontrast computed tomography image features analysis, the artificial intelligence model has higher specificity and sensitivity in the prediction of early hematoma enlargement in patients with intracerebral hemorrhage.


2018 ◽  
Vol 114 ◽  
pp. e663-e676 ◽  
Author(s):  
Danfeng Zhang ◽  
Jigang Chen ◽  
Jiaming Guo ◽  
Ying Jiang ◽  
Yan Dong ◽  
...  

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