A reliable grading system for prediction of hematoma expansion in intracerebral hemorrhage in the basal ganglia

Huang, Yongle; Qiang, Zhang; Yang, Mei

doi:10.5582/bst.2018.01061

Cited by 24 publications

(17 citation statements)

References 25 publications

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“…Another new Hematoma Expansion Prediction (HEP) score added a history of dementia and smoking and showed satisfactory discrimination ability (C-statistics, 0.76) ( 98 ). Based on multivariable logistic regression analysis, a basal ganglia score adopted three NCCT markers (island sign, blend sign, and swirl sign) and demonstrated reliable accuracy in predicting HE ( P < 0.001) ( 99 ). Furthermore, to further simplify the prediction at the bedside, three new scales (the BAT score, NAG scale, and HEAVN scale) which added more practical predictors have been established from retrospective studies and have shown acceptable sensitivity and specificity ( 33 , 34 , 100 , 101 ), but prospective validations of these scores are warranted.…”

Section: Prediction Score Modelmentioning

confidence: 99%

Hematoma Expansion in Intracerebral Hemorrhage: An Update on Prediction and Treatment

You

Long

et al. 2020

Front. Neurol.

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Intracerebral hemorrhage (ICH) is the most lethal type of stroke, but there is no specific treatment. After years of effort, neurologists have found that hematoma expansion (HE) is a vital predictor of poor prognosis in ICH patients, with a not uncommon incidence ranging widely from 13 to 38%. Herein, the progress of studies on HE after ICH in recent years is updated, and the topics of definition, prevalence, risk factors, prediction score models, mechanisms, treatment, and prospects of HE are covered in this review. The risk factors and prediction score models, including clinical, imaging, and laboratory characteristics, are elaborated in detail, but limited by sensitivity, specificity, and inconvenience to clinical practice. The management of HE is also discussed from bench work to bed practice. However, the upmost problem at present is that there is no treatment for HE proven to definitely improve clinical outcomes. Further studies are needed to identify more accurate predictors and effective treatment to reduce HE.

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Section: Prediction Score Modelmentioning

confidence: 99%

Hematoma Expansion in Intracerebral Hemorrhage: An Update on Prediction and Treatment

You

Long

et al. 2020

Front. Neurol.

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“…Several prediction systems, independent of CTA spot sign, have been published for predicting HE ( 11 , 12 , 22 , 24 , 37 , 40 – 44 ) ( Supplementary Table S3 ). However, none has actually improved clinical or research decision-making.…”

Section: Discussionmentioning

confidence: 99%

“…However, CTA examination is expensive, inaccessible in poverty-stricken regions, and not suitable for patients with contraindications, such as contrast reaction or renal impairment. Researchers have also developed some models based on non-contrast CT (NCCT) markers, such as blend sign, swirl sign, island sign, and hypodensity ( 11 , 12 ). However, these markers show overlapped definitions, and a consensus of diagnostic criteria for HE lacks, all limiting the sensitivity of previous models in predicting the risk of HE ( 13 – 15 ).…”

Section: Introductionmentioning

confidence: 99%

A non-contrast computed tomography-based radiomics nomogram for the prediction of hematoma expansion in patients with deep ganglionic intracerebral hemorrhage

Guo

et al. 2022

Front. Neurol.

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Background and purposeHematoma expansion (HE) is a critical event following acute intracerebral hemorrhage (ICH). We aimed to construct a non-contrast computed tomography (NCCT) model combining clinical characteristics, radiological signs, and radiomics features to predict HE in patients with spontaneous ICH and to develop a nomogram to assess the risk of early HE.Materials and methodsWe retrospectively reviewed 388 patients with ICH who underwent initial NCCT within 6 h after onset and follow-up CT within 24 h after initial NCCT, between January 2015 and December 2021. Using the LASSO algorithm or stepwise logistic regression analysis, five models (clinical model, radiological model, clinical-radiological model, radiomics model, and combined model) were developed to predict HE in the training cohort (n = 235) and independently verified in the test cohort (n = 153). The Akaike information criterion (AIC) and the likelihood ratio test (LRT) were used for comparing the goodness of fit of the five models, and the AUC was used to evaluate their ability in discriminating HE. A nomogram was developed based on the model with the best performance.ResultsThe combined model (AIC = 202.599, χ2 = 80.6) was the best fitting model with the lowest AIC and the highest LRT chi-square value compared to the clinical model (AIC = 232.263, χ2 = 46.940), radiological model (AIC = 227.932, χ2 = 51.270), clinical-radiological model (AIC = 212.711, χ2 = 55.490) or radiomics model (AIC = 217.647, χ2 = 57.550). In both cohorts, the nomogram derived from the combined model showed satisfactory discrimination and calibration for predicting HE (AUC = 0.900, sensitivity = 83.87%; AUC = 0.850, sensitivity = 80.10%, respectively).ConclusionThe NCCT-based model combining clinical characteristics, radiological signs, and radiomics features could efficiently discriminate early HE, and the nomogram derived from the combined model, as a non-invasive tool, exhibited satisfactory performance in stratifying HE risks.

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“…Some models [9,13,[15][16][17][18][19] had been published, but there are following defects:1. Models are complex and user-unfriendly, which cannot apply to all patients.…”

Section: Discussionmentioning

confidence: 99%

“…5. Several studies [7][8][9][14][15][16] have confirmed that HE is an independent risk factor for early deterioration of neurological function and poor long-term prognosis.…”

Section: Discussionmentioning

confidence: 99%

Establishment of prediction score for hematoma expansion after intracerebral hemorrhage

Kong

Qian

Dong

et al. 2019

Preprint

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Background: Identification of intracerebral hemorrhage (ICH) patients at risk of hematoma expansion(HE) could facilitate the selection of candidates likely to benefit from therapies aiming to minimize ICH growth. We, therefore, aimed to develop a prediction score for HE that can be quickly used during the critical phase. Methods: A retrospective analysis of clinical features of 317 ICH patients in the Second Affiliated Hospital of Soochow University from January, 2016 to May, 2018 was conducted. Independent risk factors of HE were obtained according to multiple logistic regression, and a prediction score was established and preliminarily evaluated. Results: History of anticoagulants, ultraearly hematoma growth≥2.7ml/h, GCS≤8, and non-enhanced CT signs (island sign, black hole sign, blend sign, niveau formation) exist one or more, were independent risk factors for HE (P <0.05).The C statistics of score was 0.854 (95%CI, 0.803~0.904); P < 0.001); calibration was outstanding (c2=3.323, P = 0.344); decision curve analysis showed the score was safe and reliable, with high net benefit. After dichotomized, the sensitivity, specificity and accuracy of the high-risk group (score≥4.5) were 0.77, 0.85 and 0.83, respectively. Conclusion The score can accurately identify high-risk individuals with HE, swift guide treatment decisions, and can also be used in clinical trials. Keywords: Intracerebral hemorrhage; Hematoma expansion; Non-enhanced CT; Prediction; Score.

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A reliable grading system for prediction of hematoma expansion in intracerebral hemorrhage in the basal ganglia

Cited by 24 publications

References 25 publications

Hematoma Expansion in Intracerebral Hemorrhage: An Update on Prediction and Treatment

Hematoma Expansion in Intracerebral Hemorrhage: An Update on Prediction and Treatment

A non-contrast computed tomography-based radiomics nomogram for the prediction of hematoma expansion in patients with deep ganglionic intracerebral hemorrhage

Establishment of prediction score for hematoma expansion after intracerebral hemorrhage

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