Hematoma Ventricle Distance on Computed Tomography Predicts Poor Outcome in Intracerebral Hemorrhage

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.

Optimal cut-off values of haematoma volume for predicting haematoma expansion at different intracerebral haemorrhage locations

BACKGROUND

Haematoma expansion (HE) is a frequent manifestation of acute intracerebral haemorrhage (ICH) and is associated with early disease progression and poor functional status. Approximately 30 % of patients with ICH experience substantial HE within the first few hours of onset.

OBJECTIVES

This study aimed to investigate the relationship between HE and initial volume at different locations in patients with ICH.

METHODS

We investigated consecutive patients with ICH admitted to the emergency room at Xiangyang No. 1 People's Hospital between January 2018 and June 2022. Haematoma volume was calculated using a three-dimensional slicer platform. Prediction models were assessed using a logistic regression model. The Youden index was used to assess the haematoma volume cut-off values for predicting HE.

RESULTS

This study included 306 patients: 161 had basal ganglia ICH, 41 lobar ICH, and 104 thalamic ICH. The area under the ROC curve (AUC) for the thalamic ICH score in predicting intraventricular haemorrhage (IVH) expansion ≥ 1 mL or delayed IVH expansion was 0.786, and the best cut-off value was 7.05 mL (specificity, 85.3 %; sensitivity, 62.8 %; and accuracy, 76.0 %). The AUC for the thalamic ICH and lobar ICH scores in predicting haematoma or IVH expansion were 0.756 and 0.653, respectively; the best cut-offs were 7.05 mL for the thalamus (specificity, 84.8 %; sensitivity, 60.0 %; and accuracy, 74.0 %) and 31.89 mL in the lobar area (specificity, 81.8 %; sensitivity, 52.3 %; and accuracy, 68.3 %).

CONCLUSIONS

Initial ICH volume predicted haematoma or IVH expansion at different locations. Moreover, it can assist clinicians in determining whether patients are suitable for future surgical interventions or other procedures.

Development and Validation of a Clinical-Based Signature to Predict the 90-Day Functional Outcome for Spontaneous Intracerebral Hemorrhage

We aimed to develop and validate an objective and easy-to-use model for identifying patients with spontaneous intracerebral hemorrhage (ICH) who have a poor 90-day prognosis. This three-center retrospective study included a large cohort of 1,122 patients with ICH who presented within 6 h of symptom onset [training cohort, n = 835; internal validation cohort, n = 201; external validation cohort (center 2 and 3), n = 86]. We collected the patients’ baseline clinical, radiological, and laboratory data as well as the 90-day functional outcomes. Independent risk factors for prognosis were identified through univariate analysis and multivariate logistic regression analysis. A nomogram was developed to visualize the model results while a calibration curve was used to verify whether the predictive performance was satisfactorily consistent with the ideal curve. Finally, we used decision curves to assess the clinical utility of the model. At 90 days, 714 (63.6%) patients had a poor prognosis. Factors associated with prognosis included age, midline shift, intraventricular hemorrhage (IVH), subarachnoid hemorrhage (SAH), hypodensities, ICH volume, perihematomal edema (PHE) volume, temperature, systolic blood pressure, Glasgow Coma Scale (GCS) score, white blood cell (WBC), neutrophil, and neutrophil-lymphocyte ratio (NLR) (p < 0.05). Moreover, age, ICH volume, and GCS were identified as independent risk factors for prognosis. For identifying patients with poor prognosis, the model showed an area under the receiver operating characteristic curve of 0.874, 0.822, and 0.868 in the training cohort, internal validation, and external validation cohorts, respectively. The calibration curve revealed that the nomogram showed satisfactory calibration in the training and validation cohorts. Decision curve analysis showed the clinical utility of the nomogram. Taken together, the nomogram developed in this study could facilitate the individualized outcome prediction in patients with ICH.

Association Between Anatomical Location and Hematoma Expansion in Deep Intracerebral Hemorrhage

Objective

To establish the relationship between hematoma sites of involvement and hematoma expansion (HE) in patients with deep intracerebral hemorrhage (ICH).

Methods

Eligible patients with deep ICH admitted to hospital within 6 hours of onset between 2018 and 2020 were included in this retrospective multi-center study. Individuals with secondary ICH were excluded. The volume of HE was evaluated based on admission and follow-up computed tomography scans. Associations between deep ICH sites of involvement and HE were examined using multivariable logistic regression analysis while adjusting for confounding covariates of HE.

Results

We enrolled 583 individuals from three stroke centers. Data from a final total of 460 patients were used in the analysis; of these patients, 159 (34.6%) had HE. In the crude model without adjustment, external capsule, anterior limb of the internal capsule, and posterior limb of the internal capsule (PLIC) involvement were correlated with HE. After fully adjusted models for sex, age, intraventricular hemorrhage, Glasgow Coma Scale admission score, baseline ICH volume, and time from onset to initial computed tomography, multivariable logistic regression revealed that the PLIC is a robust predictor of HE in patients with deep ICH (adjusted odds ratio = 2.73; 95% confidence interval = 1.75–4.26; p < 0.001).

Conclusion

Involvement of the posterior limb of the internal capsule in deep hemorrhage could be a promising predictor of HE.

From Spot Sign to Bleeding on the Spot: Classic and Original Signs of Expanding Primary Spontaneous Intracerebral Hematoma

Expansion of a primary spontaneous intracranial hemorrhage (PSICH) has become lately of increasing interest, especially after the emergence of its early predictors. However, these signs lacked sensitivity and specificity. The flood phenomenon, defined as a drastic increase in the size of a PSICH during the same magnetic resonance study, was first described in this paper based on the data of a university medical center in Lebanon. Moreover, further review of this data resulted in 205 studies with presumed diagnosis of primary spontaneous intracranial hemorrhage within the last 10 years, of which 29 exams showed typical predictors of hematoma expansion on computed tomography. The intended benefit of this observation is to draw the radiologists' attention towards minimal variations in the volume of the hematoma between the two extreme sequences of the same MRI study, in order to detect inconspicuous flood phenomena—a direct sign of hematoma expansion.

Predicting intraventricular hemorrhage growth with a machine learning-based, radiomics-clinical model

We constructed a radiomics-clinical model to predict intraventricular hemorrhage (IVH) growth after spontaneous intracerebral hematoma. The model was developed using a training cohort (N=626) and validated with an independent testing cohort (N=270). Radiomics features and clinical predictors were selected using the least absolute shrinkage and selection operator (LASSO) method and multivariate analysis. The radiomics score (Rad-score) was calculated through linear combination of selected features multiplied by their respective LASSO coefficients. The support vector machine (SVM) method was used to construct the model. IVH growth was experienced by 13.4% and 13.7% of patients in the training and testing cohorts, respectively. The Rad-score was associated with severe IVH and poor outcome. Independent predictors of IVH growth included hypercholesterolemia (odds ratio [OR], 0.12 [95%CI, 0.02-0.90]; p=0.039), baseline Graeb score (OR, 1.26 [95%CI, 1.16-1.36]; p<0.001), time to initial CT (OR, 0.70 [95%CI, 0.58-0.86]; p<0.001), international normalized ratio (OR, 4.27 [95%CI, 1.40, 13.0]; p=0.011), and Rad-score (OR, 2.3 [95%CI, 1.6-3.3]; p<0.001). In the training cohort, the model achieved an AUC of 0.78, sensitivity of 0.83, and specificity of 0.66. In the testing cohort, AUC, sensitivity, and specificity were 0.71, 0.81, and 0.64, respectively. This radiomics-clinical model thus has the potential to predict IVH growth.