Houston Intra-Arterial Therapy Research Articles

A tool to improve stroke outcome prediction: The charlotte large artery occlusion endovascular therapy outcome score

ObjectivesWe sought to optimize functional outcome prediction for large artery occlusion (LAO) patients treated with endovascular thrombectomy (EVT). Materials and MethodsPatients presenting with an anterior circulation LAO treated with EVT from November 2016-July 2020 were included from a health system's code stroke registry. Data were separated into training and validation cohorts using a simple random sampling method. Logistic regression analysis was used to identify pre-intervention prognostic factors independently associated with 90-day modified Rankin score 4–6 in the training cohort. The model was tested in the validation cohort and compared to previously reported scales using Area Under Curve (AUC) analyses. Results646 total patients were included. The Charlotte Large artery occlusion Endovascular therapy Outcome Score, CLEOS = (5 x Age) + (10 x NIHSS) + Glucose – (150 x Cerebral Blood Volume Index). CLEOS was associated with an increased odds of poor 90-day outcome (per 1-point increase, OR 1.008, 95% CI 1.006–1.010, p < 0.0001) and performed better than Stroke Prognostication using Age and National Institute of Health Stroke Scale – 100 (AUC 0.62, p < 0.0001) and Houston Intra-Arterial Therapy 2 (AUC 0.70, p < 0.0063), with a trend observed versus Pittsburgh Response to Endovascular therapy (AUC 0.72, p = 0.0884), in the combined analysis of the derivation and validation cohorts. CLEOS ≥ 700 was not associated with a lower risk of poor outcome despite excellent endovascular reperfusion. ConclusionsCLEOS can predict poor 90-day outcomes after thrombectomy and help risk stratify patients based on the degree of revascularization after EVT.

Abstract TMP71: Optimizing Stroke Outcome Prediction: Derivation And Validation Of The Charlotte Large Artery Occlusion Endovascular Therapy Outcome Score

Introduction: Prognostic scores for large artery occlusion patients treated with endovascular intervention have not incorporated CTP parameters. We derived the Charlotte Large artery occlusion Endovascular therapy Outcome Score (CLEOS) to optimize outcome prediction after mechanical thrombectomy. Methods: Patients presenting with an ICA or MCA occlusion treated with thrombectomy from November 2016-July 2020 were included from our health system’s prospectively collected, code stroke registry in the derivation analysis. Prognostic factors independently associated with poor outcomes (90-day mRS score 4-6) were combined with CTP parameters in a regression model. Internal cross-validation was performed. A separate, prospective validation cohort was included from the registry with patients from December 2020-April 2021. The final scale was compared to previously reported scales using Area Under the Curve-Receiver Operator Characteristic analysis. Results: 646 patients were included in the derivation analysis and 60 patients in the separate validation cohort. CLEOS = (6 x Age) + (13 x NIHSS) + Glucose - (162 x CBV Index). CLEOS predicted poor 90-day outcomes (AUC 0.74, 95% CI 0.71-0.78) better than the Stroke Prognostication using Age and National Institute of Health Stroke Scale-100 Index (AUC 0.63, p &lt; 0.0001), the Houston Intra-Arterial Therapy 2 score (AUC 0.69, p = 0.0029), and the Pittsburgh Response to Endovascular therapy score (AUC 0.71, p = 0.0240) in a combined analysis of the derivation and validation cohorts. Patients with CLEOS scores ≥ 850 did not have a lower risk of poor 90-day outcomes despite excellent revascularization (modified TICI 2c-3). Conclusions: CLEOS can predict poor 90-day outcomes after an ICA or MCA occlusion and help identify patients unlikely to benefit from endovascular thrombectomy despite excellent revascularization.

Optimizing Outcome Prediction Scores in Patients Undergoing Endovascular Thrombectomy for Large Vessel Occlusions Using Collateral Grade on Computed Tomography Angiography.

Although several outcome prediction scores incorporated with pretreatment variables have been developed for acute ischemic stroke (AIS) patients, there is not currently a prediction score that includes pretreatment imaging that can show salvageable brain tissue. To evaluate whether addition of the collateral grade on computed tomography angiography to previously published prediction scores could increase accuracy of clinical outcome prediction in endovascular thrombectomy (EVT) for AIS. This study used a retrospective multicenter registry for patients undergoing EVT for anterior circulation large vessel occlusion. Three previously published outcome prediction scores (Houston intra-arterial therapy 2, HIAT2; totaled health risks in vascular events, THRIVE; and Pittsburgh response to endovascular therapy, PRE scores) were tested in this study. Using 482 deprivation cohorts, areas under the receiver operating characteristic curves (AUC-ROCs) were compared between prediction scores with/without collateral grades in predicting the poor outcomes (modified Rankin Scale 4-6 at 3-mo follow-up) after EVT. We developed modified prediction scores by adding the collateral grade, and their advancement of outcome prediction was validated using 208 independent validation cohorts. AUC-ROCs of HIAT2, THRIVE, and PRE scores that incorporated with collateral grade were superior in predicting poor outcomes when compared to that of the unmodified scores (P<0.001). In modified prediction models, 3, 3, and 10 points were added for poor collateral grade to HIAT2, THRIVE, and PRE score. Modified models outperformed unmodified models in testing of the validation cohorts (P<0.001). The addition of the collateral grade to outcome prediction scores resulted in better prediction of poor outcome after EVT for AIS compared to the prediction scores alone.

Predicting Clinical Outcomes of Large Vessel Occlusion Before Mechanical Thrombectomy Using Machine Learning.

Background and Purpose- The clinical course of acute ischemic stroke with large vessel occlusion (LVO) is a multifactorial process with various prognostic factors. We aimed to model this process with machine learning and predict the long-term clinical outcome of LVO before endovascular treatment and to compare our method with previously developed pretreatment scoring methods. Methods- The derivation cohort included 387 LVO patients, and the external validation cohort included 115 LVO patients with anterior circulation who were treated with mechanical thrombectomy. The statistical model with logistic regression without regularization and machine learning algorithms, such as regularized logistic regression, linear support vector machine, and random forest, were used to predict good clinical outcome (modified Rankin Scale score of 0-2 at 90 days) with standard and multiple pretreatment clinical variables. Five previously reported pretreatment scoring methods (the Pittsburgh Response to Endovascular Therapy score, the Stroke Prognostication Using Age and National Institutes of Health Stroke Scale index, the Totaled Health Risks in Vascular Events score, the Houston Intra-Arterial Therapy score, and the Houston Intra-Arterial Therapy 2 score) were compared with these models for the area under the receiver operating characteristic curve. Results- The area under the receiver operating characteristic curve of random forest, which was the worst among the machine learning algorithms, was significantly higher than those of the standard statistical model and the best model among the previously reported pretreatment scoring methods in the derivation (the area under the receiver operating characteristic curve were 0.85±0.07 for random forest, 0.78±0.08 for logistic regression without regularization, and 0.77±0.09 for Stroke Prognostication using Age and National Institutes of Health Stroke Scale) and validation cohorts (the area under the receiver operating characteristic curve were 0.87±0.01 for random forest, 0.56±0.07 for logistic regression without regularization, and 0.83±0.00 for Pittsburgh Response to Endovascular Therapy). Conclusions- Machine learning methods with multiple pretreatment clinical variables can predict clinical outcomes of patients with anterior circulation LVO who undergo mechanical thrombectomy more accurately than previously developed pretreatment scoring methods.

Abstract W P16: Comparing Outcome Prediction Scoring Systems in the Interventional Management of Stroke (IMS) III Trial: No Clear Winner

Introduction: Several prognostic scores have been developed to predict patient outcome after ischemic stroke. We aimed to compare eight scores in the IMS -III dataset. Hypothesis: Prediction of good and poor outcome using different scores is possible in IMS-III. Methods: We retrospectively calculated area under the curve (AUC), sensitivity, and specificity for poor (mRS 4-6) and good (mRS 0-2) 90-day outcome in all IMS-III participants with complete data. The scoring systems analyzed were: Houston Intra-arterial Therapy 2 (HIAT2), Ischemic Stroke Predictive Risk Score (iScore), Totaled Health Risks in Vascular Events (THRIVE), DRAGON, Hemorrhage after Thrombolysis (HAT), Alberta Stroke Programme Early CT Score (ASPECTS), Stroke-Thrombolytic Predictive Instrument (TPI), and Simple Variables Model (SVM). Those receiving IV rtPA followed by endovascular therapy (treatment) and those receiving IV rtPA only (control) were analyzed separately. Results: Among 656 IMS III subjects, 651 had complete data, including 429 treatment and 222 control subjects. Overall, all scoring systems showed good prediction abilities (AUC range 0.60 to 0.78). AUC comparisons did not clearly favor a single scoring system. Treatment group: For predicting poor outcome, the TPI score outperformed ASPECTS (AUC 0.73 vs 0.61; P &lt;0.05). For predicting good outcome, the HAT score was significantly better than ASPECTS (0.73 vs. 0.64; P &lt;0.05). Control group: For poor outcome, the TPI score was significantly better than ASPECTS and THRIVE (0.78 vs. 0.62 and 0.71, respectively; P &lt;0.05). For good outcome, TPI performed better than ASPECTS and THRIVE (0.73 vs. 0.61 and 0.67, respectively; P &lt;0.05). Conclusions: Outcome prediction using different scores in IMS III is possible. HIAT2, iSCORE, DRAGON, HAT, TPI, and SVM all performed comparably. Prognostic performances were comparable in both treatment arms.

Pittsburgh Response to Endovascular therapy (PRE) score: optimizing patient selection for endovascular therapy for large vessel occlusion strokes

BackgroundEndovascular therapy seems to benefit a subset of patients with large vessel occlusion strokes. We aimed to develop a clinically useful tool to identify patients who are likely to benefit...

Investigating the Utility of Previously Developed Prediction Scores in Acute Ischemic Stroke Patients in the Stroke Belt

To assess the utility of previously developed scoring systems, we compared SEDAN, named after the components of the score (baseline blood Sugar, Early infarct signs and (hyper) Dense cerebral artery sign on admission computed tomography scan, Age, and National Institutes of Health Stroke Scale on admission), Totaled Health Risks in Vascular Events (THRIVE), Houston Intra-arterial Therapy (HIAT), and HIAT-2 scoring systems among patients receiving systemic (intravenous [IV] tissue plasminogen activator [tPA]) and endovascular (intra-arterial [IA]) treatments. We retrospectively reviewed all IV tPA and IA patients presenting to our center from 2008-2011. The scores were assessed in patients who were treated with IV tPA only, IA only, and a combination of IV tPA and IA (IV-IA). We tested the ability of THRIVE to predict discharge modified Rankin scale (mRS) 3-6, HIAT and HIAT-2 discharge mRS 4-6, and SEDAN symptomatic intracerebral hemorrhage (sICH). Of the 366 patients who were included in this study, 243 had IV tPA only, 89 had IA only, and 34 had IV-IA. THRIVE was predictive of mRS 3-6 in the IV-IA (odds ratio [OR], 1.95; 95% confidence interval [CI], 1.30-2.91) and the IV group (OR, 1.71; 95% CI, 1.43-2.04), but not in the IA group. HIAT was predictive of mRS 4-6 in the IA (OR, 3.55; 95% CI, 1.65-7.25), IV (OR, 3.47; 95% CI, 2.26-5.33), and IV-IA group (OR, 6.48; 95% CI, 1.41-29.71). HIAT-2 was predictive of mRS 4-6 in the IA (OR, 1.39; 95% CI, 1.03-1.87) and IV group (OR, 1.36; 95% CI, 1.18-1.57), but not in the IV-IA group. SEDAN was not predictive of sICH in the IA or the IV-IA group, but was predictive in the IV group (OR, 1.54; 95% CI, 1.01-2.36). Our study demonstrated that although highly predictive of outcome in the original study design treatment groups, prediction scores may not generalize to all patient samples, highlighting the importance of validating prediction scores in diverse samples.

Discharge disposition to skilled nursing facility after endovascular reperfusion therapy predicts a poor prognosis

ObjectiveWe explore the impact of discharge disposition (independent rehabilitation facility (IRF) vs skilled nursing facility (SNF)) on 90 day outcomes in persons with stroke who received acute endovascular treatment.MethodsUsing a...

A Score Based on Age and DWI Volume Predicts Poor Outcome following Endovascular Treatment for Acute Ischemic Stroke

The Houston Intra-Arterial Therapy score predicts poor functional outcome following endovascular treatment for acute ischemic stroke based on clinical variables. The present study sought to (a) create a predictive scoring system that included a neuroimaging variable and (b) determine if the scoring systems predict the clinical response to reperfusion. Separate datasets were used to derive (n = 110 from the Diffusion and Perfusion Imaging Evaluation for Understanding Stroke Evolution 2 study) and validate (n = 125 from Massachusetts General Hospital) scoring systems that predict poor functional outcome, defined as a modified Rankin Scale score of 4-6 at 90 days. Age (P < 0·001; β = 0·087) and diffusion-weighted imaging volume (P = 0·023; β = 0·025) were the independent predictors of poor functional outcome. The Stanford Age and Diffusion-Weighted Imaging score was created based on the patient's age (0-3 points) and diffusion-weighted imaging lesion volume (0-1 points). The percentage of patients with a poor functional outcome increased significantly with the number of points on the Stanford Age and Diffusion-Weighted Imaging score (P < 0·01 for trend). The area under the receiver operating characteristic curve for the Stanford Age and Diffusion-Weighted Imaging score was 0·82 in the derivation dataset. In the validation cohort, the area under the receiver operating characteristic curve was 0·69 for the Stanford Age and Diffusion-Weighted Imaging score and 0·66 for the Houston Intra-Arterial Therapy score (P = 0·45 for the difference). Reperfusion, but not the interactions between the prediction scores and reperfusion, were predictors of outcome (P > 0·5). The Stanford Age and Diffusion-Weighted Imaging and Houston Intra-Arterial Therapy scores can be used to predict poor functional outcome following endovascular therapy with good accuracy. However, these scores do not predict the clinical response to reperfusion. This limits their utility as tools to select patients for acute stroke interventions.

Optimizing Prediction Scores for Poor Outcome After Intra-Arterial Therapy in Anterior Circulation Acute Ischemic Stroke

Intra-arterial therapy (IAT) promotes recanalization of large artery occlusions in acute ischemic stroke. Despite high recanalization rates, poor clinical outcomes are common. We attempted to optimize a score that combines clinical and imaging variables to more accurately predict poor outcome after IAT in anterior circulation occlusions. Patients with acute ischemic stroke undergoing IAT at University of Texas (UT) Houston for large artery occlusions (middle cerebral artery or internal carotid artery) were reviewed. Independent predictors of poor outcome (modified Rankin Scale, 4-6) were studied. External validation was performed on IAT-treated patients at Emory University. A total of 163 patients were identified at UT Houston. Independent predictors of poor outcome (P≤0.2) were identified as score variables using sensitivity analysis and logistic regression. Houston Intra-Arterial Therapy 2 (HIAT2) score ranges 0 to 10: age (≤59=0, 60-79=2, ≥80 years=4), glucose (<150=0, ≥150=1), National Institute Health Stroke Scale (≤10=0, 11-20=1, ≥21=2), the Alberta Stroke Program Early CT Score (8-10=0, ≤7=3). Patients with HIAT2≥5 were more likely to have poor outcomes at discharge (odds ratio, 6.43; 95% confidence interval, 2.75-15.02; P<0.001). After adjusting for reperfusion (Thrombolysis in Cerebral Infarction score≥2b) and time from symptom onset to recanalization, HIAT2≥5 remained an independent predictor of poor outcome (odds ratio, 5.88; 95% confidence interval, 1.96-17.64; P=0.02). Results from the cohort of Emory (198 patients) were consistent; patients with HIAT2 score≥5 had 6× greater odds of poor outcome at discharge and at 90 days. HIAT2 outperformed other previously published predictive scores. The HIAT2 score, which combines clinical and imaging variables, performed better than all previous scores in predicting poor outcome after IAT for anterior circulation large artery occlusions.

Neurointerventional Treatment in Acute Stroke. Whom to Treat? (Endovascular Treatment for Acute Stroke: Utility of THRIVE Score and HIAT Score for Patient Selection)

Intra-arterial therapy (IAT) is used increasingly as a treatment option for acute stroke caused by central large vessel occlusions. Despite high rates of recanalization, the clinical outcome is highly variable. The authors evaluated the Houston IAT (HIAT) and the totaled health risks in vascular events (THRIVE) score, two predicting scores designed to identify patients likely to benefit from IAT. Fifty-two patients treated at the Stavanger University Hospital with IAT from May 2009 to June 2012 were included in this study. We combined the scores in an additional analysis. We also performed an additional analysis according to high age and evaluated the scores in respect of technical efficacy. Fifty-two patients were evaluated by the THRIVE score and 51 by the HIAT score. We found a strong correlation between the level of predicted risk and the actual clinical outcome (THRIVE p = 0.002, HIAT p = 0.003). The correlations were limited to patients successfully recanalized and to patients <80 years. By combining the scores additional 14.3 % of the patients could be identified as poor candidates for IAT. Both scores were insufficient to identify patients with a good clinical outcome. Both scores showed a strong correlation to poor clinical outcome in patients <80 years. The specificity of the scores could be enhanced by combining them. Both scores were insufficient to identify patients with a good clinical outcome and showed no association to clinical outcome in patients aged ≥80 years.

Abstract 205: Optimizing Prediction Scores for Poor Outcome After Intra-arterial Therapy for Anterior Circulation Acute Ischemic Stroke

Background: Intra-arterial therapy (IAT) is an approach to promote recanalization of large artery occlusions (LAO) in acute ischemic stroke (AIS) but is resource intensive. Previous studies evaluated different variables that affect clinical outcome after IAT. To better identify patients who have poor outcomes despite IAT, we compared the performance of previous predictive scoring systems that relied either on clinical or imaging variables in patients undergoing IAT. We then combined imaging and clinical variables to optimize a score that would better predict poor outcome after IAT for AIS. Methods: We studied consecutive AIS patients undergoing IAT at UT-Houston for LAO (MCA or ICA) from 01/03 to 05/11. We collected demographics and clinical variables and analyzed CT head scans using the ASPECTS scores by raters blinded to outcomes. Independent predictors of poor outcome (mRS 4–6) with p values ≤0.1 were evaluated as score variables using sensitivity analysis and logistic regression. Spearman’s correlation and ROC curves were used to evaluate the final score. Houston Intra-arterial Therapy 2 (HIAT2) score ranged from 0–10 with points for: age (≤59=0, 60–79=2, ≥80 years=4), Glucose (&lt;150=0, ≥150=1), NIHSS (≤10=0), 11–20=1, ≥21=2), ASPECTS (8–10=0, ≤ 7=3). The score was created using 75% of the dataset (build group) and tested on the remaining 25% (test group). Previously published prediction scores (HIAT, THRIVE, ASPECTS) were compared against HIAT2. Results: Table 1 shows the clinical features for the 163 patients collected. Fig 1 shows the proportion of poor outcome based on HIAT-2. Patients with HIAT2 ≥ 5 were more likely to have a poor outcome at discharge (OR 6.43, 95% CI 2.75–15.02, p&lt;.001). After adjusting for reperfusion (TICI≥2b, p=.7) and time from symptom onset to recanalization (p=.4), HIAT2 remained a significant independent predictor of poor outcome (OR 5.88, 95% CI 1.96–17.64, p=.02). Fig 2 shows the performance of HIAT2 against THRIVE, HIAT and ASPECTS as well as the validation of HIAT-2. Conclusions: Clinical prediction scores for patients undergoing IAT (HIAT, THRIVE) proved superior to the ASPECTS score. The HIAT2 score, which combines clinical and imaging variables, performed better than all previous scores in predicting the chance of poor outcome after IAT for anterior circulation LAO.

Improving patient selection for endovascular treatment of acute cerebral ischemia: a review of the literature and an external validation of the Houston IAT and THRIVE predictive scoring systems

Outcome after intraarterial therapy (IAT) for acute ischemic stroke remains variable, suggesting that improved patient selection is needed to better identify patients likely to benefit from treatment. The authors evaluate the predictive accuracies of the Houston IAT (HIAT) and the Totaled Health Risks in Vascular Events (THRIVE) scores in an independent cohort and review the existing literature detailing additional predictive factors to be used in patient selection for IAT. They reviewed their center's endovascular records from January 2004 to July 2010 and identified patients who had acute ischemic stroke and underwent IAT. They calculated individual HIAT and THRIVE scores using patient age, admission National Institutes of Health Stroke Scale (NIHSS) score, admission glucose level, and medical history. The scores' predictive accuracies for good outcome (discharge modified Rankin Scale score ≤ 3) were analyzed using receiver operating characteristics analysis. The THRIVE score predicts poor outcome after IAT with reasonable accuracy and may perform better than the HIAT score. Nevertheless, both measures may have significant clinical utility; further validation in larger cohorts that accounts for differences in patient demographic characteristics, variation in time-to-treatment, and center preferences with respect to IAT modalities is needed. Additional patient predictive factors have been reported but not yet incorporated into predictive scales; the authors suggest the need for additional data analysis to determine the independent predictive value of patient admission NIHSS score, age, admission hyperglycemia, patient comorbidities, thrombus burden, collateral flow, time to treatment, and baseline neuroimaging findings.

P-005 Anesthetic considerations and the role of blood pressure management in the endovascular treatment of acute ischemic stroke

IntroductionIncreasingly used in North America, endovascular thrombolysis for ischemic stroke results in high recanalization rates without a comparable increase in good clinical outcomes. Many neurointerventionalists prefer general anesthesia to keep...