Net water uptake, a neuroimaging marker of early brain edema, as a predictor of symptomatic intracranial hemorrhage after acute ischemic stroke

Automated Prediction of Proximal Middle Cerebral Artery Occlusions in Noncontrast Brain Computed Tomography

BACKGROUND

Early identification of large vessel occlusion (LVO) in patients with ischemic stroke is crucial for timely interventions. We propose a machine learning-based algorithm (JLK-CTL) that uses handcrafted features from noncontrast computed tomography to predict LVO.

METHODS

We included patients with ischemic stroke who underwent concurrent noncontrast computed tomography and computed tomography angiography in seven hospitals. Patients from 5 of these hospitals, admitted between May 2011 and March 2015, were randomly divided into training and internal validation (9:1 ratio). Those from the remaining 2 hospitals, admitted between March 2021 and September 2021, were designated for external validation. From each noncontrast computed tomography scan, we extracted differences in volume, tissue density, and Hounsfield unit distribution between bihemispheric regions (striatocapsular, insula, M1-M3, and M4-M6, modified from the Alberta Stroke Program Early Computed Tomography Score). A deep learning algorithm was used to incorporate clot signs as an additional feature. Machine learning models, including ExtraTrees, random forest, extreme gradient boosting, support vector machine, and multilayer perceptron, as well as a deep learning model, were trained and evaluated. Additionally, we assessed the models' performance after incorporating the National Institutes of Health Stroke Scale scores as an additional feature.

RESULTS

Among 2919 patients, 83 were excluded. Across the training (n=2463), internal validation (n=275), and external validation (n=95) datasets, the mean ages were 68.5±12.4, 67.6±13.8, and 67.9±13.6 years, respectively. The proportions of men were 57%, 53%, and 59%, with LVO prevalences of 17.0%, 16.4%, and 26.3%, respectively. In the external validation, the ExtraTrees model achieved a robust area under the curve of 0.888 (95% CI, 0.850-0.925), with a sensitivity of 80.1% (95% CI, 72.0-88.1) and a specificity of 88.6% (95% CI, 84.7-92.5). Adding the National Institutes of Health Stroke Scale score to the ExtraTrees model increased sensitivity (from 80.1% to 92.1%) while maintaining specificity.

CONCLUSIONS

Our algorithm provides reliable predictions of LVO using noncontrast computed tomography. By enabling early LVO identification, our algorithm has the potential to expedite the stroke workflow.

Post-acute ischemic stroke hyperglycemia aggravates destruction of the blood-brain barrier

JOURNAL/nrgr/04.03/01300535-202406000-00039/inline-graphic1/v/2023-10-30T152229Z/r/image-tiff

Post-acute ischemic stroke hyperglycemia increases the risk of hemorrhagic transformation, which is associated with blood-brain barrier disruption. Brain microvascular endothelial cells are a major component of the blood-brain barrier. Intercellular mitochondrial transfer has emerged as a novel paradigm for repairing cells with mitochondrial dysfunction. In this study, we first investigated whether mitochondrial transfer exists between brain microvascular endothelial cells, and then investigated the effects of post-acute ischemic stroke hyperglycemia on mitochondrial transfer between brain microvascular endothelial cells. We found that healthy brain microvascular endothelial cells can transfer intact mitochondria to oxygen glucose deprivation-injured brain microvascular endothelial cells. However, post-oxygen glucose deprivation hyperglycemia hindered mitochondrial transfer and exacerbated mitochondrial dysfunction. We established an in vitro brain microvascular endothelial cell model of the blood-brain barrier. We found that post-acute ischemic stroke hyperglycemia reduced the overall energy metabolism levels of brain microvascular endothelial cells and increased permeability of the blood-brain barrier. In a clinical study, we retrospectively analyzed the relationship between post-acute ischemic stroke hyperglycemia and the severity of hemorrhagic transformation. We found that post-acute ischemic stroke hyperglycemia serves as an independent predictor of severe hemorrhagic transformation. These findings suggest that post-acute ischemic stroke hyperglycemia can aggravate disruption of the blood-brain barrier by inhibiting mitochondrial transfer.

Net water uptake as a predictive neuroimaging marker for acute ischemic stroke outcomes: a meta-analysis

OBJECTIVE

To assess the role of net water uptake (NWU) in predicting outcomes in acute ischemic stroke (AIS) patients.

METHODS

A systematic review and meta-analysis were performed, adhering to established guidelines. The search covered PubMed, Scopus, Web of Science, and Embase databases until July 1, 2023. Eligible studies reporting quantitative ischemic lesion NWU in admission CT scans of AIS patients, stratified based on outcomes, were included. Data analysis was performed using R software version 4.2.1.

RESULTS

Incorporating 17 original studies with 2217 AIS patients, NWU was significantly higher in patients with poor outcomes compared to those with good outcomes (difference of medians: 5.06, 95% CI: 3.00-7.13, p < 0.001). Despite excluding one outlier study, considerable heterogeneity persisted among the included studies (I2 = 90.8%). The meta-regression and subgroup meta-analyses demonstrated significantly higher NWU in patients with poor functional outcome, as assessed by modified Rankin Scale (difference of medians: 3.83, 95% CI: 1.98-5.68, p < 0.001, I2 = 72.9%), malignant edema/infarct (difference of medians: 8.30, 95% CI: 4.01-12.58, p < 0.001, I2 = 95.6%), and intracranial hemorrhage (difference of medians: 5.43, 95% CI: 0.44-10.43, p = 0.03, I2 = 91.1%).

CONCLUSION

NWU on admission CT scans shows promise as a predictive marker for outcomes in AIS patients. Prospective, multicenter trials with standardized, automated NWU measurement are crucial for robustly predicting diverse clinical outcomes.

CLINICAL RELEVANCE STATEMENT

The potential of net water uptake as a biomarker for predicting outcomes in acute ischemic stroke patients holds significant promise. Further validation through additional research could lead to its integration into clinical practice, potentially improving the accuracy of clinical decision-making and allowing for the development of more precise patient care strategies.

KEY POINTS

• Net water uptake, a CT-based biomarker, quantifies early brain edema after acute ischemic stroke. • Net water uptake is significantly higher in poor outcome acute ischemic stroke patients. • Net water uptake on CT scans holds promise in predicting diverse acute ischemic stroke outcomes.

Net water uptake within the ischemic penumbra predicts the presence of the midline shift in patients with acute ischemic stroke

Objective

The study aimed to explore the association between midline shift (MLS) and net water uptake (NWU) within the ischemic penumbra in acute ischemic stroke patients.

Methods

This was a retrospective cohort study that examined patients with anterior circulation stroke. Net water uptake within the acute ischemic core and penumbra was calculated using data from admission multimodal CT scans. The primary outcome was severe cerebral edema measured by the presence of MLS on 24 to 48 h follow-up CT scans. The presence of a significant MLS was defined by a deviation of the septum pellucidum from the midline on follow-up CT scans of at least 3 mm or greater due to the mass effect of ischemic edema. The net water uptake was compared between patients with and without MLS, followed by logistic regression analyses and receiver operating characteristics (ROCs) to assess the predictive power of net water uptake in MLS.

Results

A total of 133 patients were analyzed: 50 patients (37.6%) with MLS and 83 patients (62.4%) without. Compared to patients without MLS, patients with MLS had higher net water uptake within the core [6.8 (3.2-10.4) vs. 4.9 (2.2-8.1), P = 0.048] and higher net water uptake within the ischemic penumbra [2.9 (1.8-4.3) vs. 0.2 (-2.5-2.7), P < 0.001]. Penumbral net water uptake had higher predictive performance than net water uptake of the core in MLS [area under the curve: 0.708 vs. 0.603, p < 0.001]. Moreover, the penumbral net water uptake predicted MLS in the multivariate regression model, adjusting for age, sex, admission National Institutes of Health Stroke Scale (NIHSS), diabetes mellitus, atrial fibrillation, ischemic core volume, and poor collateral vessel status (OR = 1.165; 95% CI = 1.002-1.356; P = 0.047). No significant prediction was found for the net water uptake of the core in the multivariate regression model.

Conclusion

Net water uptake measured acutely within the ischemic penumbra could predict severe cerebral edema at 24-48 h.