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

Utilization, Workflow, and Outcomes of Endovascular Thrombectomy in Patients With vs Without Premorbid Disability in a National Registry

Background and Objectives

Given the paucity of high-quality safety/efficacy data on acute stroke therapies in patients with premorbid disability, they risk being routinely excluded from such therapies. We examined utilization of endovascular thrombectomy (EVT), associated workflow, and poststroke outcomes among patients with vs without premorbid disability.

Methods

We used national registry data on thrombolysis/EVT for the Czech Republic from 1 January 2016 to 31 December 2020. Premorbid disability was defined as prestroke modified Rankin Scale score (mRS) ≥3. We compared proportions of patients with vs without premorbid disability who received EVT and examined workflow times. We compared ΔmRS-change in mRS from prestroke to 3 months-in patients with vs without premorbid disability, in addition to intracerebral hemorrhage (ICH), mortality, and discharge NIHSS (National Institutes of Health Stroke Scale score), adjusting for age, sex, baseline NIHSS, and comorbidities, and verified using propensity score weighting (PSW) and matching for differences in treatment assignment. We stratified by age group (<65, 65-74, 75-84, ≥85 years) to explore outcome heterogeneity with vs without premorbid disability.

Results

Among 22,405 patients with ischemic stroke who received thrombolysis/EVT/both, 1,712 (7.6%) had prestroke mRS ≥ 3. Patients with prestroke disability were less likely to receive EVT vs those without (10.1% vs 20.7%, aOR: 0.30, 95% CI 0.24-0.36). When treated, they had longer door-to-arterial puncture times (median: 75 minutes, IQR: 58-100 vs 54, IQR: 27-77, adjusted difference: 12.5, 95% CI 2.68-22.3). Patients with prestroke disability receiving thrombolysis/EVT/both had worse ΔmRS (adjusted rate ratio, aIRR on PSW: 1.57, 95% CI 1.43-1.72), rates of 3-month mRS 5-6, discharge NIHSS, and mortality (aOR-PSW [mortality]: 2.54, 95% CI 1.92-3.34), while ICH did not significantly differ. 32.1% of patients with prestroke disability receiving thrombolysis/EVT/both successfully returned to prestroke state, but this proportion ranged from 19.6% for those older than 85 years to 66.0% for those younger than 65 years. Regardless of premorbid disability, EVT was associated with better outcomes including lower ΔmRS (aIRR-PSW: 0.87, 95% CI 0.83-0.91) and mortality, with no interaction of treatment effect by premorbid disability status (e.g., mortality pinteraction = 0.73). EVT recipients with premorbid disability did not differ significantly for several outcomes including ΔmRS (aIRR: 0.99, 95% CI 0.84-1.17) but were more likely to have 3-month mRS 5-6 (70.1% vs 39.5% without premorbid disability, aOR: 1.85, 95% CI 1.12-3.04).

Discussion

Patients with premorbid disability were less likely to receive EVT, had slower treatment times, and had worse outcomes compared with patients without premorbid disability. However, regardless of premorbid disability, patients fared better with EVT vs medical management and one-third with prestroke disability returned to their prestroke status.

Machine learning is an effective method to predict the 3-month prognosis of patients with acute ischemic stroke

Background and objectives

Upwards of 50% of acute ischemic stroke (AIS) survivors endure varying degrees of disability, with a recurrence rate of 17.7%. Thus, the prediction of outcomes in AIS may be useful for treatment decisions. This study aimed to determine the applicability of a machine learning approach for forecasting early outcomes in AIS patients.

Methods

A total of 659 patients with new-onset AIS admitted to the Department of Neurology of both the First and Second Affiliated Hospitals of Bengbu Medical University from January 2020 to October 2022 included in the study. The patient' demographic information, medical history, Trial of Org 10,172 in Acute Stroke Treatment (TOAST), National Institute of Health Stroke Scale (NIHSS) and laboratory indicators at 24 h of admission data were collected. The Modified Rankine Scale (mRS) was used to assess the 3-mouth outcome of participants' prognosis. We constructed nine machine learning models based on 18 parameters and compared their accuracies for outcome variables.

Results

Feature selection through the Least Absolute Shrinkage and Selection Operator cross-validation (Lasso CV) method identified the most critical predictors for early prognosis in AIS patients as white blood cell (WBC), homocysteine (HCY), D-Dimer, baseline NIHSS, fibrinogen degradation product (FDP), and glucose (GLU). Among the nine machine learning models evaluated, the Random Forest model exhibited superior performance in the test set, achieving an Area Under the Curve (AUC) of 0.852, an accuracy rate of 0.818, a sensitivity of 0.654, a specificity of 0.945, and a recall rate of 0.900.

Conclusion

These findings indicate that RF models utilizing general clinical and laboratory data from the initial 24 h of admission can effectively predict the early prognosis of AIS patients.

A Multimodal Ensemble Deep Learning Model for Functional Outcome Prognosis of Stroke Patients

BACKGROUND AND PURPOSE

The accurate prediction of functional outcomes in patients with acute ischemic stroke (AIS) is crucial for informed clinical decision-making and optimal resource utilization. As such, this study aimed to construct an ensemble deep learning model that integrates multimodal imaging and clinical data to predict the 90-day functional outcomes after AIS.

METHODS

We used data from the Korean Stroke Neuroimaging Initiative database, a prospective multicenter stroke registry to construct an ensemble model integrated individual 3D convolutional neural networks for diffusion-weighted imaging and fluid-attenuated inversion recovery (FLAIR), along with a deep neural network for clinical data, to predict 90-day functional independence after AIS using a modified Rankin Scale (mRS) of 3-6. To evaluate the performance of the ensemble model, we compared the area under the curve (AUC) of the proposed method with that of individual models trained on each modality to identify patients with AIS with an mRS score of 3-6.

RESULTS

Of the 2,606 patients with AIS, 993 (38.1%) achieved an mRS score of 3-6 at 90 days post-stroke. Our model achieved AUC values of 0.830 (standard cross-validation [CV]) and 0.779 (time-based CV), which significantly outperformed the other models relying on single modalities: b-value of 1,000 s/mm2 (P<0.001), apparent diffusion coefficient map (P<0.001), FLAIR (P<0.001), and clinical data (P=0.004).

CONCLUSION

The integration of multimodal imaging and clinical data resulted in superior prediction of the 90-day functional outcomes in AIS patients compared to the use of a single data modality.

Uncertainty-aware deep learning for trustworthy prediction of long-term outcome after endovascular thrombectomy

Acute ischemic stroke (AIS) is a leading global cause of mortality and morbidity. Improving long-term outcome predictions after thrombectomy can enhance treatment quality by supporting clinical decision-making. With the advent of interpretable deep learning methods in recent years, it is now possible to develop trustworthy, high-performing prediction models. This study introduces an uncertainty-aware, graph deep learning model that predicts endovascular thrombectomy outcomes using clinical features and imaging biomarkers. The model targets long-term functional outcomes, defined by the three-month modified Rankin Score (mRS), and mortality rates. A sample of 220 AIS patients in the anterior circulation who underwent endovascular thrombectomy (EVT) was included, with 81 (37%) demonstrating good outcomes (mRS ≤ 2). The performance of the different algorithms evaluated was comparable, with the maximum validation under the curve (AUC) reaching 0.87 using graph convolutional networks (GCN) for mRS prediction and 0.86 using fully connected networks (FCN) for mortality prediction. Moderate performance was obtained at admission (AUC of 0.76 using GCN), which improved to 0.84 post-thrombectomy and to 0.89 a day after stroke. Reliable uncertainty prediction of the model could be demonstrated.

Development and Internal Validation of Machine Learning Models to Predict Mortality and Disability After Mechanical Thrombectomy for Acute Anterior Circulation Large Vessel Occlusion

OBJECTIVE

Mechanical thrombectomy (MT) improves outcomes in patients with LVO but many still experience mortality or severe disability. We sought to develop machine learning (ML) models that predict 90-day outcomes after MT for LVO.

METHODS

Consecutive patients who underwent MT for LVO between 2015-2021 at a Comprehensive Stroke Center were reviewed. Outcomes included 90-day favorable functional status (mRS 0-2), severe disability (mRS 4-6), and mortality. ML models were trained for each outcome using prethrombectomy data (pre) and with thrombectomy data (post).

RESULTS

Three hundred and fifty seven patients met the inclusion criteria. After model screening and hyperparameter tuning the top performing ML model for each outcome and timepoint was random forest (RF). Using only prethrombectomy features, the AUCs for the RFpre models were 0.73 (95% CI 0.62-0.85) for favorable functional status, 0.77 (95% CI 0.65-0.86) for severe disability, and 0.78 (95% CI 0.64-0.88) for mortality. All of these were better than a standard statistical model except for favorable functional status. Each RF model outperformed Pre, SPAN-100, THRIVE, and HIAT scores (P < 0.0001 for all). The most predictive features were premorbid mRS, age, and NIHSS. Incorporating MT data, the AUCs for the RFpost models were 0.80 (95% CI 0.67-0.90) for favorable functional status, 0.82 (95% CI 0.69-0.91) for severe disability, and 0.71 (95% CI 0.55-0.84) for mortality.

CONCLUSIONS

RF models accurately predicted 90-day outcomes after MT and performed better than standard statistical and clinical prediction models.

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.

Systematic Review of Machine Learning Applied to the Secondary Prevention of Ischemic Stroke

Ischemic stroke is a serious disease posing significant threats to human health and life, with the highest absolute and relative risks of a poor prognosis following the first occurrence, and more than 90% of strokes are attributable to modifiable risk factors. Currently, machine learning (ML) is widely used for the prediction of ischemic stroke outcomes. By identifying risk factors, predicting the risk of poor prognosis and thus developing personalized treatment plans, it effectively reduces the probability of poor prognosis, leading to more effective secondary prevention. This review includes 41 studies since 2018 that used ML algorithms to build prognostic prediction models for ischemic stroke, transient ischemic attack (TIA), and acute ischemic stroke (AIS). We analyzed in detail the risk factors used in these studies, the sources and processing methods of the required data, the model building and validation, and their application in different prediction time windows. The results indicate that among the included studies, the top five risk factors in terms of frequency were cardiovascular diseases, age, sex, national institutes of health stroke scale (NIHSS) score, and diabetes. Furthermore, 64% of the studies used single-center data, 65% of studies using imbalanced data did not perform data balancing, 88% of the studies did not utilize external validation datasets for model validation, and 72% of the studies did not provide explanations for their models. Addressing these issues is crucial for enhancing the credibility and effectiveness of the research, consequently improving the development and implementation of secondary prevention measures.

Preprocedural determination of an occlusion pathomechanism in endovascular treatment of acute stroke: a machine learning-based decision

OBJECTIVE

To evaluate whether an occlusion pathomechanism can be accurately determined by common preprocedural findings through a machine learning-based prediction model (ML-PM).

METHODS

A total of 476 patients with acute stroke who underwent endovascular treatment were retrospectively included to derive an ML-PM. For external validation, 152 patients from another tertiary stroke center were additionally included. An ML algorithm was trained to classify an occlusion pathomechanism into embolic or intracranial atherosclerosis. Various common preprocedural findings were entered into the model. Model performance was evaluated based on accuracy and area under the receiver operating characteristic curve (AUC). For practical utility, a decision flowchart was devised from an ML-PM with a few key preprocedural findings. Accuracy of the decision flowchart was validated internally and externally.

RESULTS

An ML-PM could determine an occlusion pathomechanism with an accuracy of 96.9% (AUC=0.95). In the model, CT angiography-determined occlusion type, atrial fibrillation, hyperdense artery sign, and occlusion location were top-ranked contributors. With these four findings only, an ML-PM had an accuracy of 93.8% (AUC=0.92). With a decision flowchart, an occlusion pathomechanism could be determined with an accuracy of 91.2% for the study cohort and 94.7% for the external validation cohort. The decision flowchart was more accurate than single preprocedural findings for determining an occlusion pathomechanism.

CONCLUSIONS

An ML-PM could accurately determine an occlusion pathomechanism with common preprocedural findings. A decision flowchart consisting of the four most influential findings was clinically applicable and superior to single common preprocedural findings for determining an occlusion pathomechanism.

Machine learning prediction of motor function in chronic stroke patients: a systematic review and meta-analysis

Background

Recent studies have reported that machine learning (ML), with a relatively strong capacity for processing non-linear data and adaptive ability, could improve the accuracy and efficiency of prediction. The article summarizes the published studies on ML models that predict motor function 3-6 months post-stroke.

Methods

A systematic literature search was conducted in PubMed, Embase, Cochorane and Web of Science as of April 3, 2023 for studies on ML prediction of motor function in stroke patients. The quality of the literature was assessed using the Prediction model Risk Of Bias Assessment Tool (PROBAST). A random-effects model was preferred for meta-analysis using R4.2.0 because of the different variables and parameters.

Results

A total of 44 studies were included in this meta-analysis, involving 72,368 patients and 136 models. Models were categorized into subgroups according to the predicted outcome Modified Rankin Scale cut-off value and whether they were constructed based on radiomics. C-statistics, sensitivity, and specificity were calculated. The random-effects model showed that the C-statistics of all models were 0.81 (95% CI: 0.79; 0.83) in the training set and 0.82 (95% CI: 0.80; 0.85) in the validation set. According to different Modified Rankin Scale cut-off values, C-statistics of ML models predicting Modified Rankin Scale>2(used most widely) in stroke patients were 0.81 (95% CI: 0.78; 0.84) in the training set, and 0.84 (95% CI: 0.81; 0.87) in the validation set. C-statistics of radiomics-based ML models in the training set and validation set were 0.81 (95% CI: 0.78; 0.84) and 0.87 (95% CI: 0.83; 0.90), respectively.

Conclusion

ML can be used as an assessment tool for predicting the motor function in patients with 3-6 months of post-stroke. Additionally, the study found that ML models with radiomics as a predictive variable were also demonstrated to have good predictive capabilities. This systematic review provides valuable guidance for the future optimization of ML prediction systems that predict poor motor outcomes in stroke patients.

Systematic review registration

https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022335260, identifier: CRD42022335260.

Artificial Intelligence for Clinical Decision Support in Acute Ischemic Stroke: A Systematic Review

BACKGROUND

Established randomized trial-based parameters for acute ischemic stroke group patients into generic treatment groups, leading to attempts using various artificial intelligence (AI) methods to directly correlate patient characteristics to outcomes and thereby provide decision support to stroke clinicians. We review AI-based clinical decision support systems in the development stage, specifically regarding methodological robustness and constraints for clinical implementation.

METHODS

Our systematic review included full-text English language publications proposing a clinical decision support system using AI techniques for direct decision support in acute ischemic stroke cases in adult patients. We (1) describe data and outcomes used in those systems, (2) estimate the systems' benefits compared with traditional stroke diagnosis and treatment, and (3) reported concordance with reporting standards for AI in healthcare.

RESULTS

One hundred twenty-one studies met our inclusion criteria. Sixty-five were included for full extraction. In our sample, utilized data sources, methods, and reporting practices were highly heterogeneous.

CONCLUSIONS

Our results suggest significant validity threats, dissonance in reporting practices, and challenges to clinical translation. We outline practical recommendations for the successful implementation of AI research in acute ischemic stroke treatment and diagnosis.

Outcome prediction in large vessel occlusion ischemic stroke with or without endovascular stroke treatment: THRIVE-EVT

INTRODUCTION

The THRIVE score and the THRIVE-c calculation are validated ischemic stroke outcome prediction tools based on patient variables that are readily available at initial presentation. Randomized controlled trials (RCTs) have demonstrated the benefit of endovascular treatment (EVT) for many patients with large vessel occlusion (LVO), and pooled data from these trials allow for adaptation of the THRIVE-c calculation for use in shared clinical decision making regarding EVT.

METHODS

To extend THRIVE-c for use in the context of EVT, we extracted data from the Virtual International Stroke Trials Archive (VISTA) from 7 RCTs of EVT. Models were built in a randomly selected development cohort using logistic regression that included the predictors from THRIVE-c: age, NIH Stroke Scale (NIHSS) score, presence of hypertension, diabetes mellitus, and/or atrial fibrillation, as well as randomization to EVT and, where available, the Alberta Stroke Program Early CT Score (ASPECTS).

RESULTS

Good outcome was achieved in 366/787 (46.5%) of subjects randomized to EVT and in 236/795 (29.7%) of subjects randomized to control (P < 0.001), and the improvement in outcome with EVT was seen across age, NIHSS, and THRIVE-c good outcome prediction. Models to predict outcome using THRIVE elements (age, NIHSS, and comorbidities) together with EVT, with or without ASPECTS, had similar performance by ROC analysis in the development and validation cohorts (THRIVE-EVT ROC area under the curve (AUC) = 0.716 in development, 0.727 in validation, P = 0.30; THRIVE-EVT + ASPECTS ROC AUC = 0.718 in development, 0.735 in validation, P = 0.12).

CONCLUSION

THRIVE-EVT may be used alongside the original THRIVE-c calculation to improve outcome probability estimation for patients with acute ischemic stroke, including patients with or without LVO, and to model the potential improvement in outcomes with EVT for an individual patient based on variables that are available at initial presentation. Online calculators for THRIVE-c estimation are available at www.thrivescore.org and www.mdcalc.com/thrive-score-for-stroke-outcome.

Predicting Functional Outcome Using 24-Hour Post-Treatment Characteristics: Application of Machine Learning Algorithms in the STRATIS Registry

FOR SOCIAL MEDIA: @AliciaCastongu2, @FazalZaidi9, @oozaidat, @Mouhammad_Jumaa OBJECTIVE: Machine learning (ML) algorithms have emerged as powerful predictive tools in the field stroke. Here, we examine the predictive accuracy of ML models for predicting functional outcomes using 24-hour post-treatment characteristics in the Systematic Evaluation of Patients Treated With Neurothrombectomy Devices for Acute Ischemic Stroke (STRATIS) Registry.

METHODS

ML models, adaptive boost, random forest (RF), classification and regression trees (CART), C5.0 decision tree (C5.0), support vector machine (SVM), least absolute shrinkage and selection operator (LASSO), and logistic regression (LR), and traditional LR models were used to predict 90-day functional outcome (modified Rankin Scale score 0-2). Twenty-four-hour National Institutes of Health Stroke Scale (NIHSS) was examined as a continuous or dichotomous variable in all models. Model accuracy was assessed using the area under characteristic curve (AUC).

RESULTS

The 24-hour NIHSS score was a top-predictor of functional outcome in all models. ML models using the continuous 24-hour NIHSS scored showed moderate-to-good predictive performance (range mean AUC: 0.76-0.92); however, RF (AUC: 0.92 ± 0.028) outperformed all ML models, except LASSO (AUC: 0.89 ± 0.023, p = 0.0958). Importantly, RF demonstrated a significantly higher predictive value than LR (AUC: 0.87 ± 0.031, p = 0.048) and traditional LR (AUC: 85 ± 0.06, p = 0.035) when using the 24-hour continuous NIHSS score. Predictive accuracy was similar between the 24-hour NIHSS score dichotomous and continuous ML models.

INTERPRETATION

In this substudy, we found similar predictive accuracy for functional outcome when using the 24-hour NIHSS score as a continuous or dichotomous variable in ML models. ML models had moderate-to-good predictive accuracy, with RF outperforming LR models. External validation of these ML models is warranted. ANN NEUROL 2023;93:40-49.

Machine learning based outcome prediction of large vessel occlusion of the anterior circulation prior to thrombectomy in patients with wake-up stroke

PURPOSE

Outcome prediction of large vessel occlusion of the anterior circulation in patients with wake-up stroke is important to identify patients that will benefit from thrombectomy. Currently, mismatch concepts that require MRI or CT-Perfusion (CTP) are recommended to identify these patients. We evaluated machine learning algorithms in their ability to discriminate between patients with favorable (defined as a modified Rankin Scale (mRS) score of 0-2) and unfavorable (mRS 3-6) outcome and between patients with poor (mRS5-6) and non-poor (mRS 0-4) outcome.

METHODS

Data of 8395 patients that were treated between 2018 and 2020 from the nationwide registry of the German Society for Neuroradiology was retrospectively analyzed. Five models were trained with clinical variables and Alberta Stroke Program Early CT Score (ASPECTS). The model with the highest accuracy was validated with a test dataset with known stroke onset and with a test dataset that consisted only of wake-up strokes.

RESULTS

2419 patients showed poor and 3310 patients showed favorable outcome. The best performing Random Forest model achieved a sensitivity of 0.65, a specificity of 0.81 and an AUC of 0.79 on the test dataset of patients with wake-up stroke in the classification analysis between favorable and unfavorable outcome and a sensitivity of 0.42, a specificity of 0.83 and an AUC of 0.72 in the classification analysis between poor and non-poor outcome.

CONCLUSION

Machine learning algorithms have the potential to aid in the decision making for thrombectomy in patients with wake-up stroke especially in hospitals, where emergency CTP or MRI imaging is not available.

Pre-thrombectomy prognostic prediction of large-vessel ischemic stroke using machine learning: A systematic review and meta-analysis

Introduction

Machine learning (ML) methods are being increasingly applied to prognostic prediction for stroke patients with large vessel occlusion (LVO) treated with endovascular thrombectomy. This systematic review aims to summarize ML-based pre-thrombectomy prognostic models for LVO stroke and identify key research gaps.

Methods

Literature searches were performed in Embase, PubMed, Web of Science, and Scopus. Meta-analyses of the area under the receiver operating characteristic curves (AUCs) of ML models were conducted to synthesize model performance.

Results

Sixteen studies describing 19 models were eligible. The predicted outcomes include functional outcome at 90 days, successful reperfusion, and hemorrhagic transformation. Functional outcome was analyzed by 10 conventional ML models (pooled AUC=0.81, 95% confidence interval [CI]: 0.77–0.85, AUC range: 0.68–0.93) and four deep learning (DL) models (pooled AUC=0.75, 95% CI: 0.70–0.81, AUC range: 0.71–0.81). Successful reperfusion was analyzed by three conventional ML models (pooled AUC=0.72, 95% CI: 0.56–0.88, AUC range: 0.55–0.88) and one DL model (AUC=0.65, 95% CI: 0.62–0.68).

Conclusions

Conventional ML and DL models have shown variable performance in predicting post-treatment outcomes of LVO without generally demonstrating superiority compared to existing prognostic scores. Most models were developed using small datasets, lacked solid external validation, and at high risk of potential bias. There is considerable scope to improve study design and model performance. The application of ML and DL methods to improve the prediction of prognosis in LVO stroke, while promising, remains nascent.

Systematic review registration

https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021266524, identifier CRD42021266524

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

BACKGROUND AND OBJECTIVES

Very poor outcome despite IV thrombolysis (IVT) and mechanical thrombectomy (MT) occurs in approximately 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 (FRTs) in patients undergoing those therapies.

METHODS

Patients from a prospectively collected observational registry of a single academic stroke center treated with MT and/or IVT were included. The data set was split into a training (N = 1,808, 80%) and internal validation (N = 453, 20%) cohort. We used gradient boosted decision tree machine learning models after k-nearest neighbor imputation of 32 variables available at admission to predict FRT defined as modified Rankin scale 5-6 at 3 months. We report feature importance, ability for discrimination, calibration, and decision curve analysis.

RESULTS

A total of 2,261 patients with a median (interquartile range) age of 75 years (64-83 years), 46% female, median NIH Stroke Scale 9 (4-17), 34% IVT alone, 41% MT alone, and 25% bridging were included. Overall, 539 (24%) had FRT, more often in MT alone (34%) as compared with IVT alone (11%). Feature importance identified clinical variables (stroke severity, age, active cancer, prestroke disability), laboratory values (glucose, C-reactive protein, 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 (area under the curve 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. Although it might be particularly useful in low resource healthcare settings, incorporation of further multifaceted variables is necessary to further increase the predictive performance.

CNN-LSTM Based Multimodal MRI and Clinical Data Fusion for Predicting Functional Outcome in Stroke Patients

Clinical outcome prediction plays an important role in stroke patient management. From a machine learning point-of-view, one of the main challenges is dealing with heterogeneous data at patient admission, i.e. the image data which are multidimensional and the clinical data which are scalars. In this paper, a multimodal convolutional neural network - long short-term memory (CNN-LSTM) based ensemble model is proposed. For each MR image module, a dedicated network provides preliminary prediction of the clinical outcome using the modified Rankin scale (mRS). The final mRS score is obtained by merging the preliminary probabilities of each module dedicated to a specific type of MR image weighted by the clinical metadata, here age or the National Institutes of Health Stroke Scale (NIHSS). The experimental results demonstrate that the proposed model surpasses the baselines and offers an original way to automatically encode the spatio-temporal context of MR images in a deep learning architecture. The highest AUC (0.77) was achieved for the proposed model with NIHSS. Clinical Relevance- - We present the first deep learning approach predicting the clinical outcome of stroke patients treated by mechanical thrombectomy which integrates imaging data at the voxel level with key clinical metadata. Combining clinical and imaging data to evaluate the potential benefit from therapy closely mirrors the clinical decision process. Our promising results suggest our predictive model could assist in acute stroke management.

Use of Machine Learning Algorithms to Predict the Outcomes of Mechanical Thrombectomy in Acute Ischemic Stroke Patients With an Extended Therapeutic Time Window

OBJECTIVE

The aim of this study was to evaluate the performance of machine learning (ML) algorithms in predicting the functional outcome of mechanical thrombectomy (MT) outside the 6-hour therapeutic time window in patients with acute ischemic stroke (AIS).

METHODS

One hundred seventy-seven consecutive AIS patients with large-vessel occlusion in the anterior circulation who underwent MT in the extended time window were enrolled. Clinical, neuroimaging, and treatment variables that could be obtained quickly in the real-world emergency settings were collected. Four machine learning algorithms (random forests, regularized logistic regression, support vector machine, and naive Bayes) were used to predict good outcomes (modified Rankin Scale scores of 0-2) at 90 days by using (1) only variables at admission and (2) both baseline and treatment variables. The performance of each model was evaluated using receiver operating characteristic (ROC) curve analysis. Feature importance was ranked using random forest algorithms.

RESULTS

Eighty patients (45.2%) had a favorable 90-day outcome. Machine learning models including baseline clinical and neuroimaging characteristics predicted 90-day modified Rankin Scale with an area under the ROC curve of 0.80-0.81, sensitivity of 0.60-0.71 and specificity of 0.71-0.76. Further inclusion the treatment variables significantly improved the predictive performance (mean area under the ROC curve, 0.89-0.90; sensitivity, 0.77-0.85; specificity, 0.75-0.87). The most important characteristics for predicting 90-day outcomes were age, hypoperfusion intensity ratio at admission, and National Institutes of Health Stroke Scale score at 24 hours after MT.

CONCLUSIONS

Machine learning algorithms may facilitate prediction of 90-day functional outcomes in AIS patients with an extended therapeutic time window.

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.

Optimizing acute stroke outcome prediction models: Comparison of generalized regression neural networks and logistic regressions

Background

Generalized regression neural network (GRNN) and logistic regression (LR) are extensively used in the medical field; however, the better model for predicting stroke outcome has not been established. The primary goal of this study was to compare the accuracies of GRNN and LR models to identify the most optimal model for the prediction of acute stroke outcome, as well as explore useful biomarkers for predicting the prognosis of acute stroke patients.

Method

In a single-center study, 216 (80% for the training set and 20% for the test set) acute stroke patients admitted to the Shenzhen Second People’s Hospital between December 2019 to June 2021 were retrospectively recruited. The functional outcomes of the patients were measured using Barthel Index (BI) on discharge. A training set was used to optimize the GRNN and LR models. The test set was utilized to validate and compare the performances of GRNN and LR in predicting acute stroke outcome based on the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, and the Kappa value.

Result

The LR analysis showed that age, the National Institute Health Stroke Scale score, BI index, hemoglobin, and albumin were independently associated with stroke outcome. After validating in test set using these variables, we found that the GRNN model showed a better performance based on AUROC (0.931 vs 0.702), sensitivity (0.933 vs 0.700), specificity (0.889 vs 0.722), accuracy (0.896 vs 0.729), and the Kappa value (0.775 vs 0.416) than the LR model.

Conclusion

Overall, the GRNN model demonstrated superior performance to the LR model in predicting the prognosis of acute stroke patients. In addition to its advantage in not affected by implicit interactions and complex relationship in the data. Thus, we suggested that GRNN could be served as the optimal statistical model for acute stroke outcome prediction. Simultaneously, prospective validation based on more variables of the GRNN model for the prediction is required in future studies.

Association of Ischemic Core Imaging Biomarkers With Post-Thrombectomy Clinical Outcomes in the MR CLEAN Registry

Background: A considerable proportion of acute ischemic stroke patients treated with endovascular thrombectomy (EVT) are dead or severely disabled at 3 months despite successful reperfusion. Ischemic core imaging biomarkers may help to identify patients who are more likely to have a poor outcome after endovascular thrombectomy (EVT) despite successful reperfusion. We studied the association of CT perfusion-(CTP), CT angiography-(CTA), and non-contrast CT-(NCCT) based imaging markers with poor outcome in patients who underwent EVT in daily clinical practice.

Methods: We included EVT-treated patients (July 2016–November 2017) with an anterior circulation occlusion from the Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands (MR CLEAN) Registry with available baseline CTP, CTA, and NCCT. We used multivariable binary and ordinal logistic regression to analyze the association of CTP ischemic core volume, CTA-Collateral Score (CTA-CS), and Alberta Stroke Program Early CT Score (ASPECTS) with poor outcome (modified Rankin Scale score (mRS) 5-6) and likelihood of having a lower score on the mRS at 90 days.

Results: In 201 patients, median core volume was 13 (IQR 5-41) mL. Median ASPECTS was 9 (IQR 8-10). Most patients had grade 2 (83/201; 42%) or grade 3 (28/201; 14%) collaterals. CTP ischemic core volume was associated with poor outcome [aOR per 10 mL 1.02 (95%CI 1.01–1.04)] and lower likelihood of having a lower score on the mRS at 90 days [aOR per 10 mL 0.85 (95% CI 0.78–0.93)]. In multivariable analysis, neither CTA-CS nor ASPECTS were significantly associated with poor outcome or the likelihood of having a lower mRS.

Conclusion: In our population of patients treated with EVT in daily clinical practice, CTP ischemic core volume is associated with poor outcome and lower likelihood of shift toward better outcome in contrast to either CTA-CS or ASPECTS.

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.