Multiple-Factor Analyses of Futile Recanalization in Acute Ischemic Stroke Patients Treated With Mechanical Thrombectomy

Pan, Hui; Lin, Changchun; Chen, Lina; Qiao, Ye; Huang, Pei-Sheng; Liu, Bin; Zhang, Donghui; Su, Jingjing; Liu, Jian-Ren

doi:10.3389/fneur.2021.704088

Cited by 34 publications

(57 citation statements)

References 39 publications

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“…Consistent with most previous studies ( 5 , 10 – 12 , 14 ), we found that advanced age was associated with functional dependence despite successful recanalization, which may be explained by the higher prevalence of underlying diseases, higher incidence of complications, and lower potential for rehabilitation in older age groups compared with younger ones. In addition, older adults are more likely to have leukoaraiosis and poor collateral status than younger individuals, which may be linked to the occurrence of intracranial hemorrhage and poor outcomes after successful reperfusion ( 15 ).…”

Section: Discussionsupporting

confidence: 91%

Predictors of futile recanalization in patients with acute ischemic stroke undergoing mechanical thrombectomy in late time windows

Liu

Hang

et al. 2022

Front. Neurol.

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Background and purposeFutile recanalization (FR), defined as functional dependence despite successful reperfusion, is common in patients who experience an acute stroke after thrombectomy. We aimed to determine the predictors of FR in patients who underwent thrombectomy in late time windows (6 h or more after symptom onset).MethodsThis retrospective review included patients who underwent thrombectomy for acute anterior circulation large vessel occlusion from October 2019 to June 2021. Successful reperfusion was defined as a modified Thrombolysis in Cerebral Infarction (mTICI) score of 2b/3. Functional dependence at 90 days was defined as a modified Rankin scale score of 3–6. Multivariate analysis and a receiver operating characteristic (ROC) curve were used to identify the predictors of FR in patients treated in delayed time windows.ResultsOf the 99 patients included, FR was observed in 51 (51.5%). In the multivariate analysis, older age (OR, 1.12; 95% CI, 1.04–1.22; P = 0.005), female sex (OR, 3.79; 95% CI, 1.08–13.40; P = 0.038), a higher National Institutes of Health Stroke Score (NIHSS) score upon admission (OR, 1.11; 95% CI, 1.02–1.22; P = 0.023), and an increased number of passes per procedure (OR, 2.07; 95% CI, 1.11–3.86; P = 0.023) were independently associated with FR after thrombectomy. The ROC curve indicated that the model that combined age, female sex, baseline NIHSS score, and the number of passes per procedure (area under the curve, 0.84; 95% CI, 0.75–0.90, P < 0.001) was able to predict FR accurately.ConclusionsOlder age, female sex, higher NIHSS score upon admission, and an increased number of passes per procedure were independent predictors of FR in patients who experienced acute ischemic strokes after thrombectomy in late time windows.

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Section: Discussionsupporting

confidence: 91%

Predictors of futile recanalization in patients with acute ischemic stroke undergoing mechanical thrombectomy in late time windows

Liu

Hang

et al. 2022

Front. Neurol.

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“…The final cohort included 2261 patients: median (IQR) age 75 years (64-83), 46% female, median NIHSS 9 (4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17). 34% received IVT alone, 41% MT alone and 25% both acute recanalization treatments (bridging approach).…”

Section: Baseline Characteristicsmentioning

confidence: 99%

Multivariable Prediction Model for Futile Recanalization Therapies in Patients With Acute Ischemic Stroke

et al. 2022

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Background and Objectives:Very poor outcome despite intravenous thrombolysis (IVT) and mechanical thrombectomy (MT) occurs in about 1 of 4 patients with ischemic stroke and is associated with a high logistic and economic burden. We aimed to develop and validate a multivariable prognostic model to identify futile recanalization therapies (FRT) in patients undergoing those therapies.Materials and Methods:Patients from a prospectively collected observational registry of a single academic stroke center treated with MT and/or IVT were included. The dataset was split into a training (N=1808, 80%) and internal validation (N=453, 20%) cohort. We used gradient boosted decision tree machine-learning models after k-NN imputation of 32 variables available at admission to predict FRT defined as modified Rankin-Scale (mRS) 5-6 at 3 months. We report feature importance, ability for discrimination, calibration and decision curve analysis.Results:2261 patients with a median (IQR) age 75 years (64-83), 46% female, median NIHSS 9 (4-17), 34% IVT alone, 41% MT alone, 25% bridging were included. Overall 539 (24%) had FRT, more often in MT alone (34%) as compared to IVT alone (11%). Feature importance identified clinical variables (stroke severity, age, active cancer, prestroke disability), laboratory values (glucose, CRP, creatinine), imaging biomarkers (white matter hyperintensities) and onset-to-admission time as the most important predictors. The final model was discriminatory for predicting 3-month FRT (AUC 0.87, 95% CI 0.87-0.88) and had good calibration (Brier 0.12, 0.11-0.12). Overall performance was moderate (F1-score 0.63 ± 0.004) and decision curve analyses suggested higher mean net benefit at lower thresholds of treatment (up to 0.8).Conclusions:This FRT prediction model can help inform shared decision making and identify the most relevant features in the emergency setting. While it might be particularly useful in low resource healthcare settings, incorporation of further multifaceted variables is necessary to further increase the predictive performance.

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“…All patients had a clinically confirmed diagnosis of AIS with LVO of the anterior circulation and underwent EVT according to the standard of care using stentretrievers and/or aspiration catheters at the Department of Neurology at Nanjing First Hospital. Patients were selected if they fulfilled the following criteria: (1) age 18 years or older; (2) LVO in the anterior circulation, including the internal carotid artery (ICA), M1/M2 segment of the middle cerebral artery (MCA); (3) successful recanalization [defined as modified Thrombolysis In Cerebral Infarction (mTICI) scale grades 2b or 3]; (4) premorbid modified Rankin Scale (mRS) score ≤ 2; (5) known National Institutes of Health Stroke Scale (NIHSS), ASPECTS, and mRS score at 90 days; (6) time from onset to puncture ≤ 6 or 6-24 h with evidence of perfusion mismatch. Patients who missed more than one data were to be excluded.…”

Section: Study Design and Populationmentioning

confidence: 99%

Machine learning to predict futile recanalization of large vessel occlusion before and after endovascular thrombectomy

Lin

Zheng²,

Zhang³

et al. 2022

Front. Neurol.

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Background and purposeFutile recanalization occurs when the endovascular thrombectomy (EVT) is a technical success but fails to achieve a favorable outcome. This study aimed to use machine learning (ML) algorithms to develop a pre-EVT model and a post-EVT model to predict the risk of futile recanalization and to provide meaningful insights to assess the prognostic factors associated with futile recanalization.MethodsConsecutive acute ischemic stroke patients with large vessel occlusion (LVO) undergoing EVT at the National Advanced Stroke Center of Nanjing First Hospital (China) between April 2017 and May 2021 were analyzed. The baseline characteristics and peri-interventional characteristics were assessed using four ML algorithms. The predictive performance was evaluated by the area under curve (AUC) of receiver operating characteristic and calibration curve. In addition, the SHapley Additive exPlanations (SHAP) approach and partial dependence plot were introduced to understand the relative importance and the influence of a single feature.ResultsA total of 312 patients were included in this study. Of the four ML models that include baseline characteristics, the “Early” XGBoost had a better performance {AUC, 0.790 [95% confidence intervals (CI), 0.677–0.903]; Brier, 0.191}. Subsequent inclusion of peri-interventional characteristics into the “Early” XGBoost showed that the “Late” XGBoost performed better [AUC, 0.910 (95% CI, 0.837–0.984); Brier, 0.123]. NIHSS after 24 h, age, groin to recanalization, and the number of passages were the critical prognostic factors associated with futile recanalization, and the SHAP approach shows that NIHSS after 24 h ranks first in relative importance.ConclusionsThe “Early” XGBoost and the “Late” XGBoost allowed us to predict futile recanalization before and after EVT accurately. Our study suggests that including peri-interventional characteristics may lead to superior predictive performance compared to a model based on baseline characteristics only. In addition, NIHSS after 24 h was the most important prognostic factor for futile recanalization.

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Multiple-Factor Analyses of Futile Recanalization in Acute Ischemic Stroke Patients Treated With Mechanical Thrombectomy

Cited by 34 publications

References 39 publications

Predictors of futile recanalization in patients with acute ischemic stroke undergoing mechanical thrombectomy in late time windows

Predictors of futile recanalization in patients with acute ischemic stroke undergoing mechanical thrombectomy in late time windows

Multivariable Prediction Model for Futile Recanalization Therapies in Patients With Acute Ischemic Stroke

Machine learning to predict futile recanalization of large vessel occlusion before and after endovascular thrombectomy

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