Establishment and Verification of the Hematoma Expansion Prediction Score of Intracerebral Hemorrhage in the Qinghai-Tibetan Plateau

Factors affecting prognosis in traumatic cerebral contusions: A protocol for a systematic review and meta-analysis

BACKGROUND

Traumatic cerebral contusion (CC) is a severe type of injury among traumatic brain injury (TBI) patients. Individuals with traumatic CC typically exhibit rapid deterioration in their condition, leading to increased mortality rates. Despite this, there is a gap in evidence-based research. This study aims to identify the risk factors associated with adverse outcomes in patients with traumatic CC, with a particular focus on relevant biomarkers. Mortality will be the primary outcome, while the Glasgow Coma Scale (GCS) score will be considered as a secondary outcome.

METHODS AND ANALYSIS

We intend to conduct a comprehensive search through multiple Chinese and English repositories, covering the duration from the establishment of these databases up to the current era, in order to pinpoint appropriate studies. Additionally, a manual search of the references within the included literature and other pertinent works will be undertaken. The primary endpoint of this study will be the survival status of patients with traumatic brain contusion. Meta-analysis will be executed using STATA 16.0 (Stata Corporation, College Station, TX). Article selection and data extraction will be performed independently by two reviewers. The assessment of bias risks will be conducted via the Cochrane Collaboration's tool. Depending on the heterogeneity evaluation, either a fixed-effect model or a random-effects model will be applied. Subgroup and sensitivity analyses will be conducted as needed. The examination of publication bias will be carried out, and the quality of evidence for the primary outcomes will be graded. Trial registration number: CRD42023389456.

Machine learning for predicting hematoma expansion in spontaneous intracerebral hemorrhage: a systematic review and meta-analysis

PURPOSE

Early identification of hematoma enlargement and persistent hematoma expansion (HE) in patients with cerebral hemorrhage is increasingly crucial for determining clinical treatments. However, due to the lack of clinically effective tools, radiomics has been gradually introduced into the early identification of hematoma enlargement. Though, radiomics has limited predictive accuracy due to variations in procedures. Therefore, we conducted a systematic review and meta-analysis to explore the value of radiomics in the early detection of HE in patients with cerebral hemorrhage.

METHODS

Eligible studies were systematically searched in PubMed, Embase, Cochrane and Web of Science from inception to April 8, 2024. English articles are considered eligible. The radiomics quality scoring (RQS) tool was used to evaluate included studies.

RESULTS

A total of 34 studies were identified with sample sizes ranging from 108 to 3016. Eleven types of models were involved, and the types of modeling contained mainly clinical, radiomic, and radiomic plus clinical features. The radiomics models seem to have better performance (0.77 and 0.73 C-index in the training cohort and validation cohort, respectively) than the clinical models (0.69 C-index in the training cohort and 0.70 C-index in the validation cohort) in discriminating HE. However, the C-index was the highest for the combined model in both the training (0.82) and validation (0.79) cohorts.

CONCLUSIONS

Machine learning based on radiomic plus clinical features has the best predictive performance for HE, followed by machine learning based on radiomic features, and can be used as a potential tool to assist clinicians in early judgment.

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.

Prediction of Hematoma Expansion in Intracerebral Hemorrhage in 24 Hours by Machine Learning Algorithm

OBJECTIVE

The significance of noncontrast computer tomography (CT) image markers in predicting hematoma expansion (HE) following intracerebral hemorrhage (ICH) within different time intervals in the initial 24 hours after onset may be uncertain. Hence, our objective was to examine the predictive value of clinical factors and CT image markers for HE within the initial 24 hours using machine learning algorithms.

METHODS

Four machine learning algorithms, including extreme gradient boosting (XGBoost), support vector machine, random forest, and logistic regression, were employed to assess the predictive efficacy of HE within every 6-hour interval during the first 24 hours post-ICH. The area under the receiver operating characteristic curves was utilized to appraise predictive performance across various time periods within the initial 24 hours.

RESULTS

A total of 604 patients were included, with 326 being male, and 112 experiencing hematoma expansion (HE). The findings from machine learning algorithms revealed that computed tomography (CT) image markers, baseline hematoma volume, and other factors could accurately predict HE. Among these algorithms, XGBoost demonstrated the most robust predictive model results. XGBoost's accuracy at different time intervals was 0.89, 0.82, 0.87, and 0.94, accompanied by F1-scores of 0.89, 0.80, 0.87, and 0.93, respectively. The corresponding area under the curve was 0.96, affirming the precision of the predictive capability.

CONCLUSIONS

Computed tomography (CT) imaging markers and clinical factors could effectively predict HE within the initial 24 hours across various time periods by machine learning algorithms. In the expansive landscape of big data and multimodal cerebral hemorrhage, machine learning held significant potential within the realm of neuroscience.

More severe initial manifestations and worse short-term functional outcome of intracerebral hemorrhage in the plateau than in the plain

Intracerebral hemorrhage (ICH) is one of the most devastating forms of stroke. However, studies on ICH at high altitude are insufficient. We aimed to compare the initial manifestations, imaging features and short-term functional outcomes of ICH at different altitudes, and further explore the effect of altitude on the severity and prognosis of ICH. We retrospectively recruited ICH patients from January 2018 to July 2021 from two centers at different altitudes in China. Information regarding to clinical manifestations, neuroimages, and functional outcomes at discharge were collected and analyzed. Association between altitude and initial severity, neuroimages, and short-term prognosis of ICH were also investigated. A total of 724 patients with 400 lowlanders and 324 highlanders were enrolled. Compared with patients from the plain, those at high altitude were characterized by more severe preliminary manifestations (P < 0.0001), larger hematoma volume (P < 0.001) and poorer short-term functional outcome (P < 0.0001). High altitude was independently associated with dependency at discharge (adjusted P = 0.024), in-hospital mortality (adjusted P = 0.049) and gastrointestinal hemorrhage incidence (adjusted P = 0.017). ICH patients from high altitude suffered from more serious initial manifestations and worse short-term functional outcome than lowlanders. Control of blood pressure, oxygen supplementation and inhibition of inflammation may be critical for ICH at high altitude.

Clinical characteristics of spontaneous intracranial basal ganglia hemorrhage and risk factors for hematoma expansion in the plateaus of China

Background and purpose

The clinical features of intracranial cerebral hemorrhage (ICH) and the risk factors for hematoma expansion (HE) have been extensively studied. However, few studies have been performed in patients who live on a plateau. The natural habituation and genetic adaptation have resulted in differences in disease characteristics. The purpose of this study was to investigate the differences and consistency of clinical and imaging characteristics of patients in the plateaus of China compared with the plains, and to analyze the risk factors for HE of intracranial hemorrhage in the plateau patients.

Methods

From January 2020 to August 2022, we undertook a retrospective analysis of 479 patients with first-episode spontaneous intracranial basal ganglia hemorrhage in Tianjin and Xining City. The clinical and radiologic data during hospitalization were analyzed. Univariate and multivariate logistic regression analyzes were used to assess the risk factors for HE.

Results

HE occurred in 31 plateau (36.0%) and 53 plain (24.2%) ICH patients, and HE was more likely to occur in the plateau patients compared with the plain (p = 0.037). The NCCT images of plateau patients also showed heterogeneity of hematoma imaging signs, and the incidence of blend signs (23.3% vs. 11.0%, p = 0.043) and black hole signs (24.4% vs. 13.2%, p = 0.018) was significantly higher than in the plain. Baseline hematoma volume, black hole sign, island sign, blend sign, and PLT and HB level were associated with HE in the plateau. Baseline hematoma volume and the heterogeneity of hematoma imaging signs were independent predictors of HE in both the plain and plateau.

Conclusion

Compared with the plain, ICH patients in the plateau were more prone to HE. The patients showed the same heterogeneous signs on the NCCT images as in the plain, and also had predictive value for HE.

Establishment and validation of a prediction model for intraparenchymal hematoma expansion in patients with cerebral contusion: A reliable Nomogram

BACKGROUND AND OBJECTIVE

Cerebral Contusion (CC) is one of the most serious injury types in patients with traumatic brain injury (TBI). Traumatic intraparenchymal hematoma (TICH) expansion severely affects the patient's prognosis. In this study, the baseline data, imaging features, and laboratory examinations of patients with CC were summarized and analyzed to develop and validate a nomogram predictive model assessing the risk factors for TICH expansion.

METHODS

Totally 258 patients were included and retrospectively analyzed herein, who met the CC inclusion criteria, from July 2018 to July 2021. TICH expansion was defined as increased hematoma volume ≥ 30% relative to primary volume or an absolute hematoma increase ≥ 5 ml at CT review.

RESULTS

Univariate and binary logistic regression analyses were performed to screen out the independent predictors significantly correlated with TICH expansion: Age, subdural hematoma (SDH), contusion site, multihematoma fuzzy sign (MFS), contusion volume, and traumatic coagulation abnormalities (TCA). Based on these, the nomogram model was established. The differences between the contusion volume and glasgow outcome scale (GOS) were analyzed by the nonparametric tests. Larger contusion volume was associated with poor prognosis.

CONCLUSION

This study established a Nomogram model to predict TICH expansion in patients with CC. Meanwhile, the study found that the risk of bleeding tended to decrease when the hematoma volume was > 15 ml, but the larger initial hematoma volume would indicate worse prognosis. We advocate the use of predictive models for TICH expansion risk assessment in hospitalized CC patients, which is low-cost and easy-to-apply, especially in acute settings.

Establishment and validation of prognosis model for patients with cerebral contusion

Background And Objective

Cerebral Contusion (CC) is one of the most serious injury types in patients with traumatic brain injury (TBI). In this study, the baseline data, imaging features and laboratory examinations of patients with CC were summarized and analyzed to develop and validate a prediction model of nomogram to evaluate the clinical outcomes of patients.

Methods

A total of 426 patients with cerebral contusion (CC) admitted to the People’s Hospital of Qinghai Province and Affiliated Hospital of Qingdao University from January 2018 to January 2021 were included in this study, We randomly divided the cohort into a training cohort (n = 284) and a validation cohort (n = 142) with a ratio of 2:1.At Least absolute shrinkage and selection operator (Lasso) regression were used for screening high-risk factors affecting patient prognosis and development of the predictive model. The identification ability and clinical application value of the prediction model were analyzed through the analysis of receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA).

Results

Twelve independent prognostic factors, including age, Glasgow Coma Score (GCS), Basal cistern status, Midline shift (MLS), Third ventricle status, intracranial pressure (ICP) and CT grade of cerebral edema,etc., were selected by Lasso regression analysis and included in the nomogram. The model showed good predictive performance, with a C index of (0.87, 95% CI, 0.026–0.952) in the training cohort and (0.93, 95% CI, 0.032–0.965) in the validation cohort. Clinical decision curve analysis (DCA) also showed that the model brought high clinical benefits to patients.

Conclusion

This study established a high accuracy of nomogram model to predict the prognosis of patients with CC, its low cost, easy to promote, is especially applicable in the acute environment, at the same time, CSF-glucose/lactate ratio(C-G/L), volume of contusion, and mean CT values of edema zone, which were included for the first time in this study, were independent predictors of poor prognosis in patients with CC. However, this model still has some limitations and deficiencies, which require large sample and multi-center prospective studies to verify and improve our results.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12883-021-02482-4.

A model comprising the blend sign and black hole sign shows good performance for predicting early intracerebral haemorrhage expansion: a comprehensive evaluation of CT features

OBJECTIVE

To predict early intracerebral haemorrhage expansion (HE) by comprehensive evaluation of commonly used noncontrast computed tomography (NCCT) features.

METHODS

Two hundred eighty-eight patients who had a spontaneous intracerebral haemorrhage (ICH) were included. All of the patients had undergone baseline NCCT within 6 h after ICH symptom onset. Ten NCCT features were extracted. Univariate analysis and multivariable logistic regression analysis were used to select the features. Using the finally selected features, a logistic regression model was built with a training cohort (n = 202) and subsequently validated in an independent test cohort (n = 86). Additionally, stratification analysis was performed in cases with and without anticoagulant therapy.

RESULTS

HE was found in 78 patients (27.1%). The blend sign and black hole sign were finally selected. The logistic regression model built with the two features exhibited accuracies of 76.7% and 75.6%, specificities of 98.6% and 98.4%, and positive predictive values (PPVs) of 83.3% and 75.0% for the training and test cohorts, respectively. The model also showed specificities of 100% and 98.5% and PPVs of 100% and 76.9% for the anticoagulant and non-anticoagulant drug use groups, respectively. These performances were better than those of each of the separate features.

CONCLUSIONS

By comprehensive evaluation, the model comprising the blend sign and black hole sign showed good performance for predicting early intracerebral haemorrhage expansion, particularly for high specificity and PPV, regardless of the anticoagulant status.

KEY POINTS

• Early identification of patients who are more likely to have haematoma expansion is important for therapeutic intervention. • Many radiological features have been reported to correlate with intracerebral haemorrhage expansion. • By integrating only the blend sign and black hole sign, the logistic regression model showed good performance for predicting early intracerebral haemorrhage expansion.

A Meta-analysis of the Predictive Significance of the Island Sign for Hematoma Expansion in Intracerebral Hemorrhage

BACKGROUND

The island sign of non-contrast computed tomography is a risk factor for hematoma expansion (HE) after spontaneous intracerebral hemorrhage, but has inconsistent conclusions. A meta-analysis was performed to investigate the predictive accuracy of island sign for HE.

METHODS

A systematic review of published literature on island sign and hematoma expansion was conducted. The pooled sensitivity, specificity, and summary receiver operating characteristics curve (SROC) were generated. The publication bias was assessed by Deeks' funnel plot asymmetry test.

RESULTS

Nine studies with a total of 2939 patients were included in the present study. The pooled sensitivity and specificity of island sign for predicting hematoma expansion was 0.50 and 0.89, respectively. The area under the curve was 0.73 in the SROC curve. There was no significant publication bias.

CONCLUSIONS

This meta-analysis suggests that island sign of non-contrast computed tomography has a good predictive accuracy for hematoma enlargement in intracerebral hemorrhage.

The predictive accuracy of satellite sign for hematoma expansion in intracerebral hemorrhage: A meta-analysis

BACKGROUND AND PURPOSE

Satellite sign is a novel neuroimaging marker for predicting hematoma expansion (HE), which is closely related to unfavorable prognosis in patients with spontaneous intracerebral hemorrhage (ICH). However, the predictive value of satellite sign varied according to previous studies. Thus, we conduct this meta-analysis to systematically review the application value of satellite sign in related studies.

METHODS

We searched the literature in PubMed, Embase, and Web of Science from inception to April 10, 2020. Effect values, including sensitivity, specificity, and positive and negative likelihood ratio were pooled to assess the diagnostic value of satellite sign for HE in patients with ICH.

RESULTS

The meta-analysis included five studies with a total of 1493 patients. Results showed that the pooled diagnostic sensitivity and specificity were 0.50 (95 % CI, 0.31-0.70) and 0.71 (95 % CI, 0.56-0.83), respectively. In addition, the pooled positive and negative likelihood ratios were 1.7 (95 % CI, 1.5-2.1) and 0.70 (95 % CI, 0.54-0.89), respectively. No significant publication bias was found.

CONCLUSION

Satellite sign exhibited moderate sensitivity and specificity for predicting HE in patients with ICH. Further studies are needed to explore its value in clinical application.

A novel model for predicting the outcome of intracerebral hemorrhage: Based on 1186 Patients

OBJECTIVE

To establish a model for predicting the outcome according to the clinical and computed tomography(CT) image data of patients with intracerebral hemorrhage(ICH).

METHODS

The clinical and CT image data of the patients with ICH in Qinghai Provincial People's Hospital and Xuzhou Central Hospital were collected. The risk factors related to the poor outcome of the patients were determined by univariate and multivariate logistic regression analysis. To determine the effect of factors related to poor outcome, the nomogram model was made by software of R 3.5.2 and the support vector machine operation was completed by software of SPSS Modelor.

RESULTS

A total of 8265 patients were collected and 1186 patients met the criteria of the study. Age, hospitalization days, blend sign, intraventricular extension, subarachnoid hemorrhage, midline shift, diabetes and baseline hematoma volume were independent predictors of poor outcome. Among these factors, baseline hematoma volume๥20ml (odds ratio:13.706, 95% confidence interval:9.070-20.709, p < 0.001) was the most significant factor for poor outcome, followed by the volume among 10ml-20ml (odds ratio:11.834, 95% confidence interval:7.909-17.707, p < 0.001). It was concluded that the highest percentage of weight in outcome was baseline hematoma volume (25.0%), followed by intraventricular hemorrhage (23.0%).

CONCLUSION

This predictive model might accurately predict the outcome of patients with ICH. It might have a wide range of application prospects in clinical.

Island Sign Predicts Hematoma Expansion and Poor Outcome After Intracerebral Hemorrhage: A Systematic Review and Meta-Analysis

Background: Early hematoma expansion (HE) occurs in patients with intracerebral hemorrhage (ICH) within the first few hours from ICH onset. Hematoma expansion has been considered as an independent predictor of poor clinical outcome and mortality after ICH. Island sign (IS) on the non-contrast computed tomography (NCCT) appears to increase the rate of detection of HE. However, there is insufficient evidence to declare that IS is an independent predictor for ICH patients prognosis and classification.

Objectives: To investigate whether IS on NCCT could predict HE and functional outcome following ICH.

Methods: Major databases were systematically searched, including PubMed, EMBASE, Cochrane library, and the Chinese database (CNKI, VIP, and Wanfang databases). Studies about the associations between IS and HE or IS and clinical outcome were included. The pooled result used the odds ratio (OR) with a 95% confidence interval (CI) as effect size. Heterogeneity and publication bias were assessed. Subgroup analysis and meta-regression were applied to detect potential factors of heterogeneity.

Results: Eleven studies with 4,310 patients were included in the final analysis. The average incidence rate of IS and HE were 21.58 and 33%, respectively. The ideal timing for assessing HE was also not uniform or standardized. We separately performed two meta-analyses. First, 10 studies were included to estimate the association between IS and HE. The pooled OR was statistically significant (OR = 7.61, 95% CI = 3.10–18.67, P < 0.001). Second, four studies were included in the meta-analysis, and the pooled result showed that IS had a significantly positive relationship with poor outcome (OR = 3.83, 95% CI = 2.51–5.85, P < 0.001).

Conclusions: This meta-analysis showed that NCCT IS is of great importance and value for evaluation of HE and poor outcome in patients with ICH. Future studies should focus on developing consensus guidelines, and more studies with large sample size and longitudinal design are needed to validate the conclusions.