Higher Baseline Cortical Score Predicts Good Outcome in Patients With Low Alberta Stroke Program Early Computed Tomography Score Treated with Endovascular Treatment

Neurosurgery ◽  
2020 ◽  
Author(s):  
Peng-fei Xing ◽  
Yong-wei Zhang ◽  
Lei Zhang ◽  
Zi-fu Li ◽  
Hong-jian Shen ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Endovascular Treatment ◽  
Receiver Operating Characteristics ◽  
Operating Characteristics ◽  
Good Outcome ◽  
Poor Outcome ◽  
Poor Outcome Group ◽  
Outcome Group ◽  
Receiver Operating Characteristics Curve ◽  
Receiver Operating

Abstract BACKGROUND Patients with large vessel occlusion and noncontrast computed tomography (CT) Alberta Stroke Program Early CT Score (ASPECTS) <6 may benefit from endovascular treatment (EVT). There is uncertainty about who will benefit from it. OBJECTIVE To explore the predicting factors for good outcome in patients with ASPECTS <6 treated with EVT. METHODS We retrospectively reviewed 60 patients with ASPECTS <6 treated with EVT in our center between March 2018 and June 2019. Patients were divided into 2 groups because of the modified Rankin Score (mRS) at 90 d: good outcome group (mRS 0-2) and poor outcome group (mRS ≥3). Baseline and procedural characteristics were collected for unilateral variate and multivariate regression analyses to explore the influent variates for good outcome. RESULTS Good outcome (mRS 0-2) was achieved in 24 (40%) patients after EVT and mortality was 20% for 90 d. Compared with the poor outcome group, higher baseline cortical ASPECTS (c-ASPECTS), lower intracranial hemorrhage, and malignant brain edema after thrombectomy were noted in the good outcome group (all P < .01). Multivariate logistic regression showed that only baseline c-ASPECTS (≥3) was positive factor for good outcome (odds ratio = 4.29; 95% CI, 1.21-15.20; P = .024). The receiver operating characteristics curve indicated a moderate value of c-ASPECTS for predicting good outcome, with the area under receiver operating characteristics curve 0.70 (95% CI, 0.56-0.83; P = .011). CONCLUSION Higher baseline c-ASPECTS was a predictor for good clinical outcome in patients with ASPECTS <6 treated with EVT, which could be helpful to treatment decision.

scholarly journals Multimodal Predictive Modeling of Endovascular Treatment Outcome for Acute Ischemic Stroke Using Machine-Learning

Stroke ◽  
2020 ◽  
Vol 51 (12) ◽  
pp. 3541-3551
Author(s):  
Gianluca Brugnara ◽  
Ulf Neuberger ◽  
Mustafa A. Mahmutoglu ◽  
Martha Foltyn ◽  
Christian Herweh ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Endovascular Treatment ◽  
Acute Ischemic Stroke ◽  
Ct Perfusion ◽  
Predictive Performance ◽  
Receiver Operating Characteristics ◽  
Operating Characteristics ◽  
Imaging Characteristics ◽  
Receiver Operating Characteristics Curve ◽  
Receiver Operating

Background and Purpose: This study assessed the predictive performance and relative importance of clinical, multimodal imaging, and angiographic characteristics for predicting the clinical outcome of endovascular treatment for acute ischemic stroke. Methods: A consecutive series of 246 patients with acute ischemic stroke and large vessel occlusion in the anterior circulation who underwent endovascular treatment between April 2014 and January 2018 was analyzed. Clinical, conventional imaging (electronic Alberta Stroke Program Early CT Score, acute ischemic volume, site of vessel occlusion, and collateral score), and advanced imaging characteristics (CT-perfusion with quantification of ischemic penumbra and infarct core volumes) before treatment as well as angiographic (interval groin puncture-recanalization, modified Thrombolysis in Cerebral Infarction score) and postinterventional clinical (National Institutes of Health Stroke Scale score after 24 hours) and imaging characteristics (electronic Alberta Stroke Program Early CT Score, final infarction volume after 18–36 hours) were assessed. The modified Rankin Scale (mRS) score at 90 days (mRS-90) was used to measure patient outcome (favorable outcome: mRS-90 ≤2 versus unfavorable outcome: mRS-90 >2). Machine-learning with gradient boosting classifiers was used to assess the performance and relative importance of the extracted characteristics for predicting mRS-90. Results: Baseline clinical and conventional imaging characteristics predicted mRS-90 with an area under the receiver operating characteristics curve of 0.740 (95% CI, 0.733–0.747) and an accuracy of 0.711 (95% CI, 0.705–0.717). Advanced imaging with CT-perfusion did not improved the predictive performance (area under the receiver operating characteristics curve, 0.747 [95% CI, 0.740–0.755]; accuracy, 0.720 [95% CI, 0.714–0.727]; P =0.150). Further inclusion of angiographic and postinterventional characteristics significantly improved the predictive performance (area under the receiver operating characteristics curve, 0.856 [95% CI, 0.850–0.861]; accuracy, 0.804 [95% CI, 0.799–0.810]; P <0.001). The most important parameters for predicting mRS 90 were National Institutes of Health Stroke Scale score after 24 hours (importance =100%), premorbid mRS score (importance =44%) and final infarction volume on postinterventional CT after 18 to 36 hours (importance =32%). Conclusions: Integrative assessment of clinical, multimodal imaging, and angiographic characteristics with machine-learning allowed to accurately predict the clinical outcome following endovascular treatment for acute ischemic stroke. Thereby, premorbid mRS was the most important clinical predictor for mRS-90, and the final infarction volume was the most important imaging predictor, while the extent of hemodynamic impairment on CT-perfusion before treatment had limited importance.

Abstract TP195: Pre-Stroke Cognitive Impairment is Associated With Poor Outcome in Acute Ischemic Stroke Patients Treated With Mechanical Thrombectomy

Stroke ◽  
2020 ◽  
Vol 51 (Suppl_1) ◽  
Author(s):  
Takuya Kanamaru ◽  
Satoshi Suda ◽  
Junya Aoki ◽  
Kentaro Suzuki ◽  
Yuki Sakamoto ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Cognitive Impairment ◽  
Acute Ischemic Stroke ◽  
Mechanical Thrombectomy ◽  
Modified Rankin Scale ◽  
Good Outcome ◽  
Poor Outcome ◽  
Poor Outcome Group ◽  
Outcome Group ◽  
Significant Difference

Background: It is reported that pre-stroke cognitive impairment is associated with poor functional outcome after stroke associated with small vessel disease. However, it is not clear that pre-stroke cognitive impairment is associated with poor outcome in patients treated with mechanical thrombectomy. Method: We enrolled 127 consecutive patients treated with mechanical thrombectomy for acute ischemic stroke from December 2016 to November 2018. Pre-stroke cognitive function was evaluated using the Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE). We retrospectively compared poor outcome (a score of 3 to 6 on the modified Rankin Scale at 90 days) group (n=75) with good outcome (a score of 0, 1, or 2 on the modified Rankin Scale at 90 days) group (n=52) and examined that IQCODE could be the predictor of PO. Result: IQCODE was significantly higher in poor outcome group than in good outcome group (89 vs. 82, P=0.0012). Moreover, age (77.2 years old vs. 71.6 years old, P= 0.0009), the percentage of female (42.7% vs. 17.3%, P= 0.0021), complication of hypertension (HT, 68.0% vs. 44.2%, P=0.0076), National Institutes of Health Stroke Scale (NIHSS) at admission (20 vs. 11, P<0.0001), the percentage of postoperative intracerebral hemorrhage (ICH, 33.3% vs. 15.4%, P=0.0233) were higher in poor outcome group than in good outcome group, too. However, there was no significant difference between poor outcome and good outcome groups in occlusion site (P= 0.1229), DWI-ASPECTS (P= 0.2839), the duration from onset to recanalization (P=0.4871) and other risk factors. Multivariable logistic regression analysis demonstrated that IQCODE, HT and NIHSS at admission were associated with poor outcome (P= 0.0128, P=0.0061 and P<0.0001, respectively). Conclusion: Cognitive impairment could be associated with poor outcome in patients treated with mechanical thrombectomy.

scholarly journals qTICI: Quantitative assessment of brain tissue reperfusion on digital subtraction angiograms of acute ischemic stroke patients

2020 ◽  
pp. 174749302090963
Author(s):  
Haryadi Prasetya ◽  
Lucas A Ramos ◽  
Thabiso Epema ◽  
Kilian M Treurniet ◽  
Bart J Emmer ◽  
...  
Keyword(s):  
Functional Outcome ◽  
Digital Subtraction Angiography ◽  
Quantitative Measure ◽  
Receiver Operating Characteristics ◽  
Binary Logistic Regression Analysis ◽  
Digital Subtraction ◽  
Operating Characteristics ◽  
Vessel Occlusion ◽  
Receiver Operating Characteristics Curve ◽  
Receiver Operating

Background The Thrombolysis in Cerebral Infarction (TICI) scale is an important outcome measure to evaluate the quality of endovascular stroke therapy. The TICI scale is ordinal and observer-dependent, which may result in suboptimal prediction of patient outcome and inconsistent reperfusion grading. Aims We present a semi-automated quantitative reperfusion measure (quantified TICI (qTICI)) using image processing techniques based on the TICI methodology. Methods We included patients with an intracranial proximal large vessel occlusion with complete, good quality runs of anteroposterior and lateral digital subtraction angiography from the MR CLEAN Registry. For each vessel occlusion, we identified the target downstream territory and automatically segmented the reperfused area in the target downstream territory on final digital subtraction angiography. qTICI was defined as the percentage of reperfused area in target downstream territory. The value of qTICI and extended TICI (eTICI) in predicting favorable functional outcome (modified Rankin Scale 0–2) was compared using area under receiver operating characteristics curve and binary logistic regression analysis unadjusted and adjusted for known prognostic factors. Results In total, 408 patients with M1 or internal carotid artery occlusion were included. The median qTICI was 78 (interquartile range 58–88) and 215 patients (53%) had an eTICI of 2C or higher. qTICI was comparable to eTICI in predicting favorable outcome with area under receiver operating characteristics curve of 0.63 vs. 0.62 (P = 0.8) and 0.87 vs. 0.86 (P = 0.87), for the unadjusted and adjusted analysis, respectively. In the adjusted regression analyses, both qTICI and eTICI were independently associated with functional outcome. Conclusion qTICI provides a quantitative measure of reperfusion with similar prognostic value for functional outcome to eTICI score.

scholarly journals Evaluating and comparing biomarkers with respect to the area under the receiver operating characteristics curve in two-phase case–control studies

Biostatistics ◽  
2016 ◽  
Vol 17 (3) ◽  
pp. 499-522 ◽  
Author(s):  
Ying Huang
Keyword(s):  
Case Control ◽  
Receiver Operating Characteristics ◽  
Biased Sampling ◽  
Second Phase ◽  
Operating Characteristics ◽  
Case Control Studies ◽  
Two Phase ◽  
Receiver Operating Characteristics Curve ◽  
The Impact ◽  
Receiver Operating

Abstract Two-phase sampling design, where biomarkers are subsampled from a phase-one cohort sample representative of the target population, has become the gold standard in biomarker evaluation. Many two-phase case–control studies involve biased sampling of cases and/or controls in the second phase. For example, controls are often frequency-matched to cases with respect to other covariates. Ignoring biased sampling of cases and/or controls can lead to biased inference regarding biomarkers' classification accuracy. Considering the problems of estimating and comparing the area under the receiver operating characteristics curve (AUC) for a binary disease outcome, the impact of biased sampling of cases and/or controls on inference and the strategy to efficiently account for the sampling scheme have not been well studied. In this project, we investigate the inverse-probability-weighted method to adjust for biased sampling in estimating and comparing AUC. Asymptotic properties of the estimator and its inference procedure are developed for both Bernoulli sampling and finite-population stratified sampling. In simulation studies, the weighted estimators provide valid inference for estimation and hypothesis testing, while the standard empirical estimators can generate invalid inference. We demonstrate the use of the analytical variance formula for optimizing sampling schemes in biomarker study design and the application of the proposed AUC estimators to examples in HIV vaccine research and prostate cancer research.

Abstract WP58: Correlating Stroke Volume with National Institute of Health Stroke Scale as a Means of Determining Patient Prognosis

Stroke ◽  
2017 ◽  
Vol 48 (suppl_1) ◽  
Author(s):  
Pamela J Zelnick ◽  
Liang Zhu ◽  
Louise D McCullough ◽  
Amrou Sarraj
Keyword(s):  
Patient Outcomes ◽  
Infarct Volume ◽  
Strong Predictor ◽  
Ratio Test ◽  
Vessel Occlusion ◽  
Good Outcome ◽  
Poor Outcome ◽  
The Poor ◽  
Poor Outcome Group ◽  
Outcome Group

Introduction: The NIH Stroke Scale (SS) is a widely used tool for directing treatment and predicting outcomes in Acute Ischemic Stroke (AIS). Severe strokes with high admission SS often correlate with long term disability, and as such, SS serves as a strong predictor of outcome. Final infarct volume (FIV) is also a pivotal predictor of stroke outcome. We aimed to evaluate the relationship between SS, FIV and outcome, and hypothesize that a combined approach evaluating both FIV and SS may more accurately correlate with patient outcomes. Methods: A single center, retrospective cohort study, examined AIS patients with large vessel occlusion (LVO) affecting the anterior circulation, between July 2004 and April 2013. Patients were stratified by treatment to 1) intra-arterial therapy, 2) IV tPA, 3) both or 4) neither. Primary outcomes measured were mRS at discharge and 90 days (good outcome mRS 0-2, poor 4-6). FIV was manually calculated from DWI obtained within the first 7 days of presentation. SS and FIV were compared against good and poor mRS outcomes using Wilcoxon rank sum test. Logistic regression analysis was used to evaluate the association between SS, FIV and mRS. Finally, likelihood ratio test was used to compare model fit between a model including SS alone and model including both SS and FIV. Results: In 332 patients, SS was significantly higher in the poor outcome group (17.3 ± 5.4) when compared to the good outcome group (13.0 ± 6.1) (p=0.0002). In the same analysis, FIVs were also larger in the poor outcome group (110.3 ± 113 cm3) when compared to the good outcome group (37.2 ± 68.3 cm3) (p<0.0001). A combined SS and FIV model correlated significantly better with discharge outcome than did SS alone (p=0.0015). Analysis of 182 patient outcomes at 90 days maintained similar findings, with SS (18 ± 5.9) and FIVs (115.4 ± 121.0 cm3) significantly higher in poor outcomes than in good outcomes; (13.0 ± 5.4) and (35.7 ± 38.2 cm3) respectively (p<0.0001). Combined SS and FIV model, again, was significantly better at modeling outcome at 90 days than was a model including SS alone (p=0.0044). Conclusions: A combined model including FIV and SS better correlates with clinical outcomes at discharge and 90 days in patients with AIS due to LVO, than does a model using SS alone.

Sign in / Sign up

Export Citation Format

Share Document