Which patients with acute stroke due to proximal occlusion should not be treated with endovascular thrombectomy?

Efficacy of endovascular therapy for stroke in developing country: A single-centre retrospective observational study in Indonesia from 2017 to 2021

Background

Indonesia as a developing nation faces a plethora of challenges in applying endovascular therapy (EVT), mostly due to the lack of physicians specialized in neuro-intervention, high operational cost, and time limitation. The efficacy of EVT in improving functional outcomes of stroke in developing countries has not been previously studied.

Methods

This retrospective cohort study was conducted at Dr. Cipto Mangunkusumo Hospital (Jakarta, Indonesia) from January 2017 to December 2021. Large vessel occlusion (LVO) diagnosis was established based on a combination of clinical and imaging characteristics. We assessed patients' functional independence on day-90 based on modified Rankin Scale (mRS) between the endovascular treatment group and the conservative group (those receiving intravascular thrombolysis or medical treatment only). Functional independence was defined as mRS ≤2.

Results

Among 111 stroke patients with LVO, we included 32 patients in the EVT group and 50 patients in the conservative group for this study. Patients with younger age (p = 0.004), lower hypertension rate (p < 0.001), higher intubation rate (p = 0.014), and earlier onset of stroke were observed in the EVT group. The proportion of mRS ≤2 at day-90 in the EVT group was higher than the conservative group (28.1 % vs. 18.0 %; p = 0.280). Patients within mRS ≤2 group had earlier onset-to-puncture time (p = 0.198), onset-to-recanalization time (p = 0.341), lower NIHSS (p = 0.026) and higher ASPECTS (p = 0.001) on admission. In multivariate analysis, ASPECTS (aOR 2.43; 95%CI 1.26-4.70; p = 0.008) defined functional independence in the EVT group.

Conclusion

The endovascular therapy group had a higher proportion of mRS ≤2 at day-90 than the conservative group despite its statistical insignificance.

Comparing Poor and Favorable Outcome Prediction With Machine Learning After Mechanical Thrombectomy in Acute Ischemic Stroke

Background and Purpose

Outcome prediction after mechanical thrombectomy (MT) in patients with acute ischemic stroke (AIS) and large vessel occlusion (LVO) is commonly performed by focusing on favorable outcome (modified Rankin Scale, mRS 0–2) after 3 months but poor outcome representing severe disability and mortality (mRS 5 and 6) might be of equal importance for clinical decision-making.

Methods

We retrospectively analyzed patients with AIS and LVO undergoing MT from 2009 to 2018. Prognostic variables were grouped in baseline clinical (A), MRI-derived variables including mismatch [apparent diffusion coefficient (ADC) and time-to-maximum (Tmax) lesion volume] (B), and variables reflecting speed and extent of reperfusion (C) [modified treatment in cerebral ischemia (mTICI) score and time from onset to mTICI]. Three different scenarios were analyzed: (1) baseline clinical parameters only, (2) baseline clinical and MRI-derived parameters, and (3) all baseline clinical, imaging-derived, and reperfusion-associated parameters. For each scenario, we assessed prediction for favorable and poor outcome with seven different machine learning algorithms.

Results

In 210 patients, prediction of favorable outcome was improved after including speed and extent of recanalization [highest area under the curve (AUC) 0.73] compared to using baseline clinical variables only (highest AUC 0.67). Prediction of poor outcome remained stable by using baseline clinical variables only (highest AUC 0.71) and did not improve further by additional variables. Prediction of favorable and poor outcomes was not improved by adding MR-mismatch variables. Most important baseline clinical variables for both outcomes were age, National Institutes of Health Stroke Scale, and premorbid mRS.

Conclusions

Our results suggest that a prediction of poor outcome after AIS and MT could be made based on clinical baseline variables only. Speed and extent of MT did improve prediction for a favorable outcome but is not relevant for poor outcome. An MR mismatch with small ischemic core and larger penumbral tissue showed no predictive importance.

Endovascular Thrombectomy Treatment: Beyond Early Time Windows and Small Core

ABSTRACT

Tremendous advancements in the treatment of acute ischemic stroke in the last 25 years have been based on the principle of reperfusion in early time windows and identification of small core infarct for intravenous thrombolysis and mechanical thrombectomy. Advances in neuroimaging have made possible the safe treatment of patients with acute ischemic stroke in longer time windows and with more specific selection of patients with salvageable brain tissue. In this review, we discuss the history of endovascular stroke thrombectomy trials and highlight the neuroimaging-based trials that validated mechanical thrombectomy techniques in the extended time window with assessment of penumbral tissue. We conclude with a survey of currently open trials that seek to safely expand eligibility for this highly efficacious treatment.

Evolutionary algorithms and decision trees for predicting poor outcome after endovascular treatment for acute ischemic stroke

Despite the large overall beneficial effects of endovascular treatment in patients with acute ischemic stroke, severe disability or death still occurs in almost one-third of patients. These patients, who might not benefit from treatment, have been previously identified with traditional logistic regression models, which may oversimplify relations between characteristics and outcome, or machine learning techniques, which may be difficult to interpret. We developed and evaluated a novel evolutionary algorithm for fuzzy decision trees to accurately identify patients with poor outcome after endovascular treatment, which was defined as having a modified Rankin Scale score (mRS) higher or equal to 5. The created decision trees have the benefit of being comprehensible, easily interpretable models, making its predictions easy to explain to patients and practitioners. Insights in the reason for the predicted outcome can encourage acceptance and adaptation in practice and help manage expectations after treatment. We compared our proposed method to CART, the benchmark decision tree algorithm, on classification accuracy and interpretability. The fuzzy decision tree significantly outperformed CART: using 5-fold cross-validation with on average 1090 patients in the training set and 273 patients in the test set, the fuzzy decision tree misclassified on average 77 (standard deviation of 7) patients compared to 83 (±7) using CART. The mean number of nodes (decision and leaf nodes) in the fuzzy decision tree was 11 (±2) compared to 26 (±1) for CART decision trees. With an average accuracy of 72% and much fewer nodes than CART, the developed evolutionary algorithm for fuzzy decision trees might be used to gain insights into the predictive value of patient characteristics and can contribute to the development of more accurate medical outcome prediction methods with improved clarity for practitioners and patients.

Predicting Poor Outcome Before Endovascular Treatment in Patients With Acute Ischemic Stroke

Background: Although endovascular treatment (EVT) has greatly improved outcomes in acute ischemic stroke, still one third of patients die or remain severely disabled after stroke. If we could select patients with poor clinical outcome despite EVT, we could prevent futile treatment, avoid treatment complications, and further improve stroke care. We aimed to determine the accuracy of poor functional outcome prediction, defined as 90-day modified Rankin Scale (mRS) score ≥5, despite EVT treatment. Methods: We included 1,526 patients from the MR CLEAN Registry, a prospective, observational, multicenter registry of ischemic stroke patients treated with EVT. We developed machine learning prediction models using all variables available at baseline before treatment. We optimized the models for both maximizing the area under the curve (AUC), reducing the number of false positives. Results: From 1,526 patients included, 480 (31%) of patients showed poor outcome. The highest AUC was 0.81 for random forest. The highest area under the precision recall curve was 0.69 for the support vector machine. The highest achieved specificity was 95% with a sensitivity of 34% for neural networks, indicating that all models contained false positives in their predictions. From 921 mRS 0-4 patients, 27-61 (3-6%) were incorrectly classified as poor outcome. From 480 poor outcome patients in the registry, 99-163 (21-34%) were correctly identified by the models. Conclusions: All prediction models showed a high AUC. The best-performing models correctly identified 34% of the poor outcome patients at a cost of misclassifying 4% of non-poor outcome patients. Further studies are necessary to determine whether these accuracies are reproducible before implementation in clinical practice.

Automated Measurement of Cerebral Atrophy and Outcome in Endovascular Thrombectomy

Background and Purpose—

Methods of identifying ischemic stroke patients with a greater probability of poor outcome following endovascular thrombectomy (EVT) might improve shared treatment decision-making between patients, families, and physicians. We used an objective, automated method to measure cerebral atrophy and investigated whether this was associated with outcome in EVT patients.

Methods—

Consecutive EVT patients from a single-center registry were studied. CT brain scans were segmented with a combination of a validated U-Net and Hounsfield unit thresholding. Intracranial cerebrospinal fluid (CSF) volume was used as a marker of cerebral atrophy and calculated as a proportion of total intracranial volume. The primary outcome was functional independence, defined as a 3-month modified Rankin Scale score of 0 to 2.

Results—

Three-hundred sixty EVT patients were included. Functional independence was achieved in 204 (56.7%) patients. The mean±SD CSF volume was 9.0±4.7% of total intracranial volume. Multivariable regression demonstrated that increasing CSF volume was associated with reduced functional independence (OR=0.65 per 5% increase in CSF volume; 95% CI, 0.48–0.89; P =0.007) and higher 3-month modified Rankin Scale scores (common OR, 1.59 per 5% increase in CSF volume; 95% CI, 1.05–2.41; P =0.03).

Conclusions—

Cerebral atrophy determined by automated measurement of intracranial CSF volume is associated with functional outcome in patients undergoing EVT. If validated in future studies, this simple, objective, and automated imaging marker could potentially be incorporated into decision-support tools to improve shared treatment decision-making.

Influence of Guidelines in Endovascular Therapy Decision Making in Acute Ischemic Stroke: Insights From UNMASK EVT

Background and Purpose- The American Heart Association and the American Stroke Association guidelines for early management of patients with ischemic stroke offer guidance to physicians involved in acute stroke care and clarify endovascular treatment indications. The purpose of this study was to assess concordance of physicians' endovascular treatment decision-making with current American Heart Association and the American Stroke Association stroke treatment guidelines using a survey-approach and to explore how decision-making in the absence of guideline recommendations is approached. Methods- In an international cross-sectional survey (UNMASK-EVT), physicians were randomly assigned 10 of 22 case scenarios (8 constructed with level 1A and 11 with level 2B evidence for endovascular treatment and 3 scenarios without guideline coverage) and asked to declare their treatment approach (1) under their current local resources and (2) assuming there were no external constraints. The proportion of physicians offering endovascular therapy (EVT) was calculated. Subgroup analysis was performed for different specialties, geographic regions, with regard to physicians' age, endovascular, and general stroke treatment experience. Results- When facing level 1A evidence, participants decided in favor of EVT in 86.8% under current local resources and in 90.6% under assumed ideal conditions, that is, 9.4% decided against EVT even under assumed ideal conditions. In case scenarios with level 2B evidence, 66.3% decided to proceed with EVT under current local resources and 69.7% under assumed ideal conditions. Conclusions- There is potential for improving thinking around the decision to offer endovascular treatment, since physicians did not offer EVT even under assumed ideal conditions in 9.4% despite facing level 1A evidence. A majority of physicians would offer EVT even for level 2B evidence cases.

Perfusion Computed Tomography in Acute Ischemic Stroke

Occlusion of a cervical or cerebral artery may cause acute ischemic stroke (AIS). Recent advances in AIS treatment by endovascular thrombectomy have led to more widespread use of advanced computed tomography (CT) imaging, including perfusion CT (PCT). This article reviews PCT for the evaluation of AIS patients.

Glycated hemoglobin (HbA1c) and outcome following endovascular thrombectomy for ischemic stroke

BACKGROUND

In ischemic stroke, increased glycated hemoglobin (HbA1c) and glucose levels are associated with worse outcome following thrombolysis, and possibly, endovascular thrombectomy.

OBJECTIVE

To evaluate the association between admission HbA1c and glucose levels and outcome following endovascular thrombectomy.

METHODS

Consecutive patients treated with endovascular thrombectomy with admission HbA1c and glucose levels were included. The primary outcome was functional independence, defined as a modified Rankin Scale score of 0-2 at 3 months. Secondary outcomes included successful reperfusion (modified Thrombolysis in Cerebral Infarction 2b-3), early neurological improvement (reduction in National Institutes of Health Stroke Scale (NIHSS) score ≥8 points, or NIHSS score of 0-1 at 24 hours), symptomatic intracerebral hemorrhage (sICH), and mortality at 3 months.

RESULTS

223 patients (136 (61%) men; mean±SD age 64.5±14.6) were included. The median (IQR) HbA1c and glucose were 39 (36-45) mmol/mol and 6.9 (5.8-8.4) mmol/L, respectively. Multiple logistic regression analysis demonstrated that increasing HbA1c levels (per 10 mmol/mol) were associated with reduced functional independence (OR=0.76; 95% CI 0.60-0.96; p=0.02), increased sICH (OR=1.33; 95% CI 1.03 to 1.71; p=0.03), and increased mortality (OR=1.26; 95% CI 1.01 to 1.57; p=0.04). There were no significant associations between glucose levels and outcome measures (all p>0.05).

CONCLUSIONS

HbA1c levels are an independent predictor of worse outcome following endovascular thrombectomy. The addition of HbA1c to decision-support tools for endovascular thrombectomy should be evaluated in future studies.