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IMPORTANCE: Outcome prediction after endovascular treatment (EVT) for ischemic stroke is important to patients, family members, and physicians. OBJECTIVE: To develop and validate a model based on preprocedural and postprocedural characteristics to predict functional outcome for individual patients after EVT. DESIGN, SETTING, AND PARTICIPANTS: A prediction model was developed using individual patient data from 7 randomized clinical trials, performed between December 2010 and December 2014. The model was developed within the Highly Effective Reperfusion Evaluated in Multiple Endovascular Stroke Trials (HERMES) collaboration and external validation in data from the Dutch Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands (MR CLEAN) Registry of patients treated in clinical practice between March 2014 and November 2017. Participants included patients from multiple centers throughout different countries in Europe, North America, East Asia, and Oceania (derivation cohort), and multiple centers in the Netherlands (validation cohort). Included were adult patients with a history of ischemic stroke from an intracranial large vessel occlusion in the anterior circulation who underwent EVT within 12 hours of symptom onset or last seen well. Data were last analyzed in July 2022. MAIN OUTCOME(S) AND MEASURE(S): A total of 19 variables were assessed by multivariable ordinal regression to predict functional outcome (modified Rankin Scale [mRS] score) 90 days after EVT. Variables were routinely available 1 day after EVT. Akaike information criterion (AIC) was used to optimize model fit vs model complexity. Probabilities for functional independence (mRS 0-2) and survival (mRS 0-5) were derived from the ordinal model. Model performance was expressed with discrimination (C statistic) and calibration. RESULTS: A total of 781 patients (median [IQR] age, 67 [57-76] years; 414 men [53%]) constituted the derivation cohort, and 3260 pat

BACKGROUND: Analyses of the effect of pre-stroke functional levels on the outcome of endovascular therapy (EVT) have focused on the course of patients with moderate to substantial pre-stroke disability. The effect of complete freedom from pre-existing disability (modified Rankin Scale (mRS) 0) versus predominantly mild pre-existing disability/symptoms (mRS 1-2) has not been well delineated. METHODS: The HERMES meta-analysis pooled data from seven randomized trials that tested the efficacy of EVT. We tested for a multiplicative interaction effect of pre-stroke mRS on the relationship between treatment and outcomes. Ordinal regression was used to assess the association between EVT and 90-day mRS (primary outcome) in the subgroup of patients with pre-stroke mRS 1-2. Multivariable regression modeling was then used to test the effect of mild pre-stroke disability/symptoms on the primary and secondary outcomes (delta-mRS, mRS 0-2/5-6) compared with patients with pre-stroke mRS 0. RESULTS: We included 1764 patients, of whom 199 (11.3%) had pre-stroke mRS 1-2. No interaction effect of pre-stroke mRS on the relationship between treatment and outcome was observed. Patients with pre-stroke mRS 1-2 had worse outcomes than those with pre-stroke mRS 0 (adjusted common OR (acOR) 0.53, 95% CI 0.40 to 0.70). Nonetheless, a significant benefit of EVT was observed within the mRS 1-2 subgroup (cOR 2.08, 95% CI 1.22 to 3.55). CONCLUSIONS: Patients asymptomatic/without disability prior to onset have better outcomes following EVT than patients with mild disability/symptoms. Patients with pre-stroke mRS 1-2, however, more often achieve good outcomes with EVT compared with conservative management. These findings indicate that mild pre-existing disability/symptoms influence patient prognosis after EVT but do not diminish the EVT treatment effect.

Background: Early neurological deterioration (END) after endovascular treatment (EVT) in patients with anterior circulation acute ischemic stroke (AIS) is associated with poor outcome. END may remain unexplained by parenchymal hemorrhage (UnEND). We aim to analyze the risk factors of UnEND in the medical management (MM) and EVT arms of the HERMES study. Methods: We conducted a post-hoc analysis of anterior AIS patients who underwent EVT for proximal anterior occlusions. Risk factors of UnEND, defined as a worsening of ≥4 points between baseline National Institutes of Health Stroke Scale (NIHSS) and NIHSS at 24 hours without hemorrhage, were compared between both arms using mixed logistic regression models adjusted for baseline characteristics. An interaction analysis between the EVT and MM arms for risk factors of UnEND was conducted. Results: Among 1723 patients assessable for UnEND, 160 patients experienced an UnEND (9.3%), including 9.1% (78/854) in the EVT arm and 9.4% (82/869) in the MM arm. There was no significant difference in the incidence of UnEND between the two study arms. In the EVT population, independent risk factors of UnEND were lower baseline NIHSS, higher baseline glucose, and lower collateral grade. In the MM population, the only independent predictor of UnEND was higher baseline glucose. However, we did not demonstrate an interaction between EVT and MM for baseline factors as risk factors of UnEND. UnEND was, similarly in both treatment groups, a significant predictor of unfavorable outcome in both the EVT (p<0.001) and MM (p<0.001) arms. Conclusions: UnEND is not an uncommon event, with a similar rate which ever treatment arm is considered. In the clinical scenario of AIS due to large vessel occlusion, no patient-related factor seems to increase the risk for UnEND when treated by EVT compared with MM.

PURPOSE: Follow-up infarct volume (FIV) is moderately associated with functional outcome. We hypothesized that accounting for infarct location would strengthen the association of FIV with functional outcome. METHODS: We included 252 patients from the HERMES collaboration with follow-up diffusion weighted imaging. Patients received endovascular treatment combined with best medical management (n = 52%) versus best medical management alone (n = 48%). FIV was quantified in low, moderate and high modified Rankin Scale (mRS)-relevant regions. We used binary logistic regression to study the relation between the total, high, moderate or low mRS-relevant FIVs and favorable outcome (mRS < 2) after 90 days. The strength of association was evaluated using the c-statistic. RESULTS: Small lesions only occupied high mRS-relevant brain regions. Lesions additionally occupied lower mRS-relevant brain regions if FIV expanded. Higher FIV was associated with a higher risk of unfavorable outcome, as were volumes of tissue with low, moderate and high mRS relevance. In multivariable modeling, only the volume of high mRS-relevant infarct was significantly associated with favorable outcome. The c-statistic was highest (0.76) for the models that included high mRS-relevant FIV or the combination of high, moderate and low mRS-relevant FIV but was not significantly different from the model that included only total FIV (0.75). CONCLUSION: This study confirms the association of FIV and unfavorable functional outcome but showed no strengthened association if lesion location was taken into account.

Item does not contain fulltext, Infarct volume (FIV) on follow-up diffusion-weighted imaging (FU-DWI) is only moderately associated with functional outcome in acute ischemic stroke patients. However, FU-DWI may contain other imaging biomarkers that could aid in improving outcome prediction models for acute ischemic stroke. We included FU-DWI data from the HERMES, ISLES, and MR CLEAN-NO IV databases. Lesions were segmented using a deep learning model trained on the HERMES and ISLES datasets. We assessed the performance of three classifiers in predicting functional independence for the MR CLEAN-NO IV trial cohort based on: (1) FIV alone, (2) the most important features obtained from a trained convolutional autoencoder (CAE), and (3) radiomics. Furthermore, we investigated feature importance in the radiomic-feature-based model. For outcome prediction, we included 206 patients: 144 scans were included in the training set, 21 in the validation set, and 41 in the test set. The classifiers that included the CAE and the radiomic features showed AUC values of 0.88 and 0.81, respectively, while the model based on FIV had an AUC of 0.79. This difference was not found to be statistically significant. Feature importance results showed that lesion intensity heterogeneity received more weight than lesion volume in outcome prediction. This study suggests that predictions of functional outcome should not be based on FIV alone and that FU-DWI images capture additional prognostic information.

Infarct volume (FIV) on follow-up diffusion-weighted imaging (FU-DWI) is only moderately associated with functional outcome in acute ischemic stroke patients. However, FU-DWI may contain other imaging biomarkers that could aid in improving outcome prediction models for acute ischemic stroke. We included FU-DWI data from the HERMES, ISLES, and MR CLEAN-NO IV databases. Lesions were segmented using a deep learning model trained on the HERMES and ISLES datasets. We assessed the performance of three classifiers in predicting functional independence for the MR CLEAN-NO IV trial cohort based on: (1) FIV alone, (2) the most important features obtained from a trained convolutional autoencoder (CAE), and (3) radiomics. Furthermore, we investigated feature importance in the radiomic-feature-based model. For outcome prediction, we included 206 patients: 144 scans were included in the training set, 21 in the validation set, and 41 in the test set. The classifiers that included the CAE and the radiomic features showed AUC values of 0.88 and 0.81, respectively, while the model based on FIV had an AUC of 0.79. This difference was not found to be statistically significant. Feature importance results showed that lesion intensity heterogeneity received more weight than lesion volume in outcome prediction. This study suggests that predictions of functional outcome should not be based on FIV alone and that FU-DWI images capture additional prognostic information.