DWI-Based Algorithm to Predict Disability in Patients Treated with Thrombectomy for Acute Stroke

Endovascular thrombectomy versus medical management on outcomes with infarct volumes more than 70 mL

OBJECTIVE

Endovascular thrombectomy (EVT) in patients with large infarct volume remains controversial. The aim of this study is to compare clinical outcomes between EVT and medical management in acute large vessel occlusion with infarct volumes larger than 70 mL on diffusion-weighted magnetic resonance imaging (DWI).

METHODS

A prospective observational cohort study was conducted, including patients with anterior cerebral circulation occlusion due to ischemic stroke with infarct volumes larger than 70 mL within 24 h of onset between July 2018 and June 2023. Eligible patients were divided into two groups: the EVT group and the medical management (non-EVT) group. The main outcomes were functional independence and mortality at 90 days. To assess clinical endpoints, we selected variables including age, NIHSS score, infarct volume, and occlusion location for 1:1 propensity score (PS) matching and PS adjustment using inverse probability of treatment weighting (IPTW).

RESULTS

Among the 131 identified patients (mean [SD] age, 69.9 [13.7] years; 58 female), the median infarct volume was 123.6 mL. Of these patients, 75 (57.3%) underwent EVT. After PS adjustment, EVT was not associated with functional independence (10.9% vs. 10.9%; p = 1.000) or mortality (43.5% vs. 47.8%; p = 0.675). Additionally, after PS adjustment using IPTW, EVT was also not associated with a functional independence (15.8% vs. 13.7%; p = 0.767) or mortality (46.8% vs. 44.0%; p = 0.762).

CONCLUSION

This study provides real-world evidence regarding infarct volumes larger than 70 mL, indicating that EVT does not provide benefits compared to medical management alone when considering age, NIHSS score, infarct volume, and occlusion location.

TICI-RANKIN mismatch: Poor clinical outcome despite complete endovascular reperfusion in the ETIS Registry

INTRODUCTION

Endovascular treatment (EVT) is a well-established technic for acute ischemic stroke, but despite a high recanalization rate of near 80%, at 3 months roughly 50% of patients have a poor functional outcome with a modified Rankin score (mRS) ≥3. The aim of this study was to determine predictive factors of poor functional outcomes in patients with complete recanalization after EVT, defined as modified thrombolysis in cerebral infarction (mTICI) 3.

PATIENTS AND METHODS

This retrospective analysis based on the prospective multicenter ETIS registry (endovascular treatment in ischemic stroke) in France included 795 patients from January 2015 and November 2019 with acute ischemic stroke due to anterior circulation occlusion and prestroke mRS 0-1, treated with EVT and who achieved complete recanalization. Univariate and multivariate logistic regression models were used to identify predictive factors of poor functional outcome.

RESULTS

365 patients (46%) showed a poor functional outcome (mRS>2). In backward-stepwise logistic regression analysis, poor functional outcome was independently associated with older age (OR per 10-year increase, 1.51; 95%CI, 1.30 to 1.75), higher admission NIHSS (OR per 1 point increase, 1.28; 95%CI, 1.21 to 1.34), absence of prior intravenous thrombolysis (OR, 0.59; 95%CI, 0.39 to 0.90), and an unfavorable 24-hour NIHSS change (24h-baseline) (OR, 0.82; 95%CI, 0.79 to 0.87). We calculated that patients whose 24h NIHSS decreased by less than 5 points are more at risk of a poor outcome, with a sensitivity and a specificity of 65.0%.

CONCLUSION

Despite complete reperfusion after EVT, half of patients had a poor clinical outcome. These patients, who were mainly older with a high initial NIHSS and an unfavorable post-EVT 24h NIHSS change, could represent a target population for early neurorepair and neurorestorative strategies.

In Mild and Moderate Acute Ischemic Stroke, Increased Lipid Peroxidation and Lowered Antioxidant Defenses Are Strongly Associated with Disabilities and Final Stroke Core Volume

In acute ischemic stroke (AIS), there are no data on whether oxidative stress biomarkers have effects above and beyond known risk factors and measurements of stroke volume. This study was conducted in 122 mild-moderate AIS patients and 40 controls and assessed the modified ranking scale (mRS) at baseline, and 3 and 6 months later. We measured lipid hydroperoxides (LOOH), malondialdehyde (MDA), advanced oxidation protein products, paraoxonase 1 (PON1) activities and PON1 Q192R genotypes, high density lipoprotein cholesterol (HDL), sulfhydryl (-SH) groups), and diffusion-weighted imaging (DWI) stroke volume and fluid-attenuated inversion recovery (FLAIR) signal intensity. We found that (a) AIS is characterized by lower chloromethyl acetate CMPAase PON1 activity, HDL and -SH groups and increased LOOH and neurotoxicity (a composite of LOOH, inflammatory markers and glycated hemoglobin); (b) oxidative and antioxidant biomarkers strongly and independently predict mRS scores 3 and 6 months later, DWI stroke volume and FLAIR signal intensity; and (c) the PON1 Q192R variant has multiple effects on stroke outcomes that are mediated by its effects on antioxidant defenses and lipid peroxidation. Lipid peroxidation and lowered -SH and PON1-HDL activity are drug targets to prevent AIS and consequent neurodegenerative processes and increased oxidative reperfusion mediators due to ischemia-reperfusion injury.

Combining machine learning with radiomics features in predicting outcomes after mechanical thrombectomy in patients with acute ischemic stroke

BACKGROUND AND OBJECTIVE

Some patients with mechanical thrombectomy will have a poor prognosis. This study establishes a model for predicting the prognosis after mechanical thrombectomy in acute stroke based on diffusion-weighted imaging (DWI) omics characteristics.

METHODS

A total of 260 stroke patients receiving mechanical thrombectomy in our hospital were randomly divided into a training set (n = 182) and a test set (n = 78) in a 7:3 ratio. The regions of interest (ROI) of the imaging features of the DWI infarct area were extracted, and the minimum absolute contraction and selection operator regression model were used to screen the best radiomics features. A support vector machine classifier established the prediction model of the prognosis after mechanical thrombectomy of acute stroke based on the selected features. The prediction efficiency of the model was evaluated by the receiver operating characteristic (ROC) curve.

RESULTS

A total of 1936 radiomic features were extracted, and six features highly correlated with prognosis were screened after dimensionality reduction. Based on the DWI model, the ROC analysis showed that the area under the curve (AUC) for correct prediction in the training and test sets was 0.945 and 0.920, respectively.

CONCLUSION

The model based on the characteristics of radiomics and machine learning has high predictive efficiency for the prognosis of acute stroke after mechanical thrombectomy, which can be used to guide personalized clinical treatment.

Time-Based Decision Making for Reperfusion in Acute Ischemic Stroke

Decision making in the extended time windows for acute ischemic stroke can be a complex and time-consuming process. The process of making the clinical decision to treat has been compounded by the availability of different imaging modalities. In the setting of acute ischemic stroke, time is of the essence and chances of a good outcome diminish by each passing minute. Navigating the plethora of advanced imaging modalities means that treatment in some cases can be inefficaciously delayed. Time delays and individually based non-programmed decision making can prove challenging for clinicians. Visual aids can assist such decision making aimed at simplifying the use of advanced imaging. Flow charts are one such visual tool that can expedite treatment in this setting. A systematic review of existing literature around imaging modalities based on site of occlusion and time from onset can be used to aid decision making; a more program-based thought process. The use of an acute reperfusion flow chart helping navigate the myriad of imaging modalities can aid the effective treatment of patients.

Nijeholt G, van Es A, van der Lugt A, Majoie C, Burke J, Roozenbeek B, Lingsma H, Dippel D. Outcome Prediction Models for Endovascular Treatment of Ischemic Stroke: Systematic Review and External Validation

Supplemental Digital Content is available in the text.

Prediction models for outcome of patients with acute ischemic stroke who will undergo endovascular treatment have been developed to improve patient management. The aim of the current study is to provide an overview of preintervention models for functional outcome after endovascular treatment and to validate these models with data from daily clinical practice.

The role of CT texture analysis in predicting the clinical outcomes of acute ischemic stroke patients undergoing mechanical thrombectomy

OBJECTIVES

To evaluate the performance of CT-based texture analysis (TA) for predicting clinical outcomes of mechanical thrombectomy (MT) in acute ischemic stroke (AIS).

METHODS

This single-center, retrospective study contained 64 consecutive patients with AIS who underwent MT for large anterior circulation occlusion between December 2016 and January 2020. Patients were divided into 2 groups according to the modified Rankin scale (mRS) scores at 3 months as good outcome (mRS ≤ 2) and bad outcome (mRS > 2). Two observers examined the early ischemic changes for TA on baseline non-contrast CT images independently. Demographic, clinical, periprocedural, and texture variables were compared between the groups and ROC curves were made. Logistic regression analysis was used and a model was created to determine the independent predictors of a bad outcome.

RESULTS

Sixty-four patients (32 female, 32 male; mean age 63.03 ± 14.42) were included in the study. Fourteen texture parameters were significantly different between patients with good and bad outcomes. The long-run high gray-level emphasis (LRHGE), which is a gray-level run-length matrix (GLRLM) feature, showed the highest sensitivity (80%) and specificity (70%) rates to predict disability. The GLRLM_LRHGE value of > 4885.0 and the time from onset to puncture of > 237.5 mi were found as independent predictors of the bad outcome. The diagnostic rate was 80.0% when using the combination of the GLRLM_LRHGE and the time from onset to puncture cutoff values.

CONCLUSION

CT-based TA might be a promising modality to predict clinical outcome after MT in patients with AIS.

KEY POINTS

• The gray-level run-length matrix parameters displayed higher diagnostic performance among the texture features. • The long-run high gray-level emphasis showed the highest sensitivity and specificity rates for predicting a bad outcome in stroke patients undergoing mechanical thrombectomy. • The gray-level run-length matrix_long-run high gray-level emphasis value of > 4885.0 (OR = 11.06; 95% CI = 2.51 - 48.77; p = 0.001) and the time from onset to puncture of > 237.5 min (OR = 8.55; 95% CI = 1.96 - 37.21; p = 0.004) were found as independent predictors of the bad outcome.