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BACKGROUND: Stroke is a leading cause of death and disability worldwide. Rapid and accurate diagnosis is crucial for minimizing brain damage and optimizing treatment plans. OBJECTIVE: This review aims to summarize the methods of artificial intelligence (AI)-assisted stroke diagnosis over the past 25 years, providing an overview of performance metrics and algorithm development trends. It also delves into existing issues and future prospects, intending to offer a comprehensive reference for clinical practice. METHODS: A total of 50 representative articles published between 1999 and 2024 on using AI technology for stroke prevention and diagnosis were systematically selected and analyzed in detail. RESULTS: AI-assisted stroke diagnosis has made significant advances in stroke lesion segmentation and classification, stroke risk prediction, and stroke prognosis. Before 2012, research mainly focused on segmentation using traditional thresholding and heuristic techniques. From 2012 to 2016, the focus shifted to machine learning (ML)-based approaches. After 2016, the emphasis moved to deep learning (DL), which brought significant improvements in accuracy. In stroke lesion segmentation and classification as well as stroke risk prediction, DL has shown superiority over ML. In stroke prognosis, both DL and ML have shown good performance. CONCLUSIONS: Over the past 25 years, AI technology has shown promising performance in stroke diagnosis.

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Stroke, as a critical global health concern and the second leading cause of death, occurs when blood flow to the brain is interrupted. Although machine learning has advanced in medical safety, there is limited research on stroke prediction using information fusion systems. This study presents a fusion framework that combines multiple base classifiers and a Meta classifier to improve stroke prediction performance. The research utilizes Grid Search optimized models, such as Random Forest, Support Vector Machine, K Nearest Neighbors, AdaBoost, Gradient Boosting, Light Gradient Boosting, Categorical Boosting, and eXtreme Gradient Boosting for in-depth stroke analysis. Since stroke events are rare and unpredictable, classification outcomes can be deceptive due to imbalanced data. This article examines stroke probability by comparing three data balancing methods: over-sampling, under-sampling, and tomek-link synthetic minority over-sampling (SMOTE-TL) to enhance prediction accuracy. The findings reveal that AdaBoost as a meta-classifier attains the highest performance in the fusion framework, with a peak of 88.09% Recall and 83.66% F1 score. This innovative approach provides crucial insights into stroke prediction and can be a valuable resource for strengthening intervention efforts in advanced healthcare systems.

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The main cause of stroke is the unexpected blockage of blood flow to the brain. The brain cells die if blood is not supplied to them, resulting in body disability. The timely identification of medical conditions ensures patients receive the necessary treatments and assistance. This early diagnosis plays a crucial role in managing symptoms effectively and enhancing the overall quality of life for individuals affected by the stroke. The research proposed an ensemble machine learning (ML) model that predicts brain stroke while reducing parameters and computational complexity. The dataset was obtained from an open-source website Kaggle and the total number of participants is 3,254. However, this dataset needs a significant class imbalance problem. To address this issue, we utilized Synthetic Minority Over-sampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADAYSN), a technique for oversampling issues. The primary focus of this study centers around developing a stacking and voting approach that exhibits exceptional performance. We propose a stacking ensemble classifier that is more accurate and effective in predicting stroke disease in order to improve the classifier's performance and minimize overfitting problems. To create a final stronger classifier, the study used three tree-based ML classifiers. Hyperparameters are used to train and fine-tune the random forest (RF), decision tree (DT), and extra tree classifier (ETC), after which they were combined using a stacking classifier and a k-fold cross-validation technique. The effectiveness of this method is verified through the utilization of metrics such as accuracy, precision, recall, and F1-score. In addition, we utilized nine ML classifiers with Hyper-parameter tuning to predict the stroke and compare the effectiveness of Proposed approach with these classifiers. The experimental outcomes demonstrated the superior performance of the stacking classification method compared to other approaches. The stacking method achieved a remarkable accuracy of 100% as well as exceptional F1-score, precision, and recall score. The proposed approach demonstrates a higher rate of accurate predictions compared to previous techniques.

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Atrial fibrillation (AF) is the most common arrhythmia in adults worldwide and represents an important burden for patients, physicians, and healthcare systems. AF is associated with substantial mortality and morbidity, due to the disease itself and its specific complications, such as the increased risk of stroke and thromboembolic events associated with AF. The temporal relation between AF episodes and stroke is nonetheless incompletely understood. The factors associated with an increased thromboembolic risk remain unclear, as well as the stroke risk stratification. Therefore, in this review, we intend to expose the mechanisms and physiopathology leading to intracardiac thrombus formation and stroke in AF patients, together with the evidence supporting the causal hypothesis. We also expose the risk factors associated with increased risk of stroke, the current different risk stratification tools as well as future prospects for improving this risk stratification.

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Cricket has a massive global following and is ranked as the second most popular sport globally, with an estimated 2.5 billion fans. Batting requires quick decisions based on ball speed, trajectory, fielder positions, etc. Recently, computer vision and machine learning techniques have gained attention as potential tools to predict cricket strokes played by batters. This study presents a cutting-edge approach to predicting batsman strokes using computer vision and machine learning. The study analyzes eight strokes: pull, cut, cover drive, straight drive, backfoot punch, on drive, flick, and sweep. The study uses the MediaPipe library to extract features from videos and several machine learning and deep learning algorithms, including random forest (RF), support vector machine, k-nearest neighbors, decision tree, linear regression, and long short-term memory to predict the strokes. The study achieves an outstanding accuracy of 99.77% using the RF algorithm, outperforming the other algorithms used in the study. The k-fold validation of the RF model is 95.0% with a standard deviation of 0.07, highlighting the potential of computer vision and machine learning techniques for predicting batsman strokes in cricket. The study's results could help improve coaching techniques and enhance batsmen's performance in cricket, ultimately improving the game's overall quality.

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Purpose: The interpretation of image data plays a critical role during acute brain stroke diagnosis, and promptly defining the requirement of a surgical intervention will drastically impact the patient's outcome. However, determining stroke lesions purely from images can be a daunting task. Many studies proposed automatic segmentation methods for brain stroke lesions from medical images in different modalities, though heretofore results do not satisfy the requirements to be clinically reliable. We investigate the segmentation of brain stroke lesions using a geometric deep learning model that takes advantage of the intrinsic interconnected diffusion features in a set of multi-modal inputs consisting of computer tomography (CT) perfusion parameters. Approach: We propose a geometric deep learning model for the segmentation of ischemic stroke brain lesions that employs spline convolutions and unpooling/pooling operators on graphs to excerpt graph-structured features in a fully convolutional network architecture. In addition, we seek to understand the underlying principles governing the different components of our model. Accordingly, we structure the experiments in two parts: an evaluation of different architecture hyperparameters and a comparison with state-of-the-art methods. Results: The ablation study shows that deeper layers obtain a higher Dice coefficient score (DCS) of up to 0.3654. Comparing different pooling and unpooling methods shows that the best performing unpooling method is the proportional approach, yet it often smooths the segmentation border. Unpooling achieves segmentation results more adapted to the lesion boundary corroborated with systematic lower values of Hausdorff distance. The model performs at the level of state-of-the-art models without optimized training methods, such as augmentation or patches, with a DCS of 0.4553±0.0031. Conclusions: We proposed and evaluated an end-to-end trainable fully convolutional graph network architecture using spline convolutional layers for the ischemic stroke lesion prediction. We propose a model that employs graph-based operations to predict acute stroke brain lesions from CT perfusion parameters. Our results prove the feasibility of using geometric deep learning to solve segmentation problems, and our model shows a better performance than other models evaluated. The proposed model achieves improved metric values for the DCS metric, ranging from 8.61% to 69.05%, compared with other models trained under the same conditions. Next, we compare different pooling and unpooling operations in relation to their segmentation results, and we show that the model can produce segmentation outputs that adapt to irregular segmentation boundaries when using simple heuristic unpooling operations.

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Introduction: Pediatric stroke is among the top 10 causes of death in pediatrics. Rapid recognition and treatment can improve outcomes in select patients, as evidenced by recent retrospective studies in pediatric thrombectomy. We established a collaborative protocol involving the vascular neurology and pediatric neurology division in our institution to rapidly diagnose and treat pediatric suspected stroke. We also prospectively collected data to attempt to identify predictors of acute stroke in pediatric patients. Methods: IRB approval was obtained to prospectively collect clinical data on pediatric code stroke activations based on timing metrics in resident-physician note templates. The protocol emphasized magnetic resonance imaging over computed tomography imaging when possible. We analyzed performance of the system with descriptive statistics. We then performed a Bayesian statistical analysis to search for predictors of pediatric stroke. Results: There were 40 pediatric code strokes over the 2.5-year study period with a median age of 10.8 years old. 12 (30%) of patients had stroke, and 28 (70%) of code stroke patients were diagnosed with a stroke mimic. Median time from code stroke activation to completion of imaging confirming or ruling out stroke was 1 h. In the Bayesian analysis, altered mental status, hemiparesis, and vasculopathy history were associated with increased odds of stroke, though credible intervals were wide due to the small sample size. Conclusion: A trainee developed and initiated pediatric acute stroke protocol quickly implemented a hospital wide change in management that led to rapid diagnosis and triage of pediatric stroke and suspected stroke. No additional personnel or resources were needed for this change, and we encourage other hospitals and emergency departments to implement similar systems. Additionally, hemiparesis and altered mental status were predictors of stroke for pediatric acute stroke activation in our Bayesian statistical analysis. However credible intervals were wide due to the small sample size. Further multicenter data collection could more definitively analyze predictors of stroke, as well as the help in the creation of diagnostic tools for clinicians in the emergency setting.

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The CHADS2 and CHA2DS2-VASc scores are widely used to assess ischemic risk in the patients with atrial fibrillation (AF). However, the discrimination performance of these scores is limited. Using the data from a community-based prospective cohort study, we sought to construct a machine learning-based prediction model for cerebral infarction in patients with AF, and to compare its performance with the existing scores. All consecutive patients with AF treated at 81 study institutions from March 2011 to May 2017 were enrolled (n = 4396). The whole dataset was divided into a derivation cohort (n = 1005) and validation cohort (n = 752) after excluding the patients with valvular AF and anticoagulation therapy. Using the derivation cohort dataset, a machine learning model based on gradient boosting tree algorithm (ML) was built to predict cerebral infarction. In the validation cohort, the receiver operating characteristic area under the curve of the ML model was higher than those of the existing models according to the Hanley and McNeil method: ML, 0.72 (95%CI, 0.66-0.79); CHADS2, 0.61 (95%CI, 0.53-0.69); CHA2DS2-VASc, 0.62 (95%CI, 0.54-0.70). As a conclusion, machine learning algorithm have the potential to perform better than the CHADS2 and CHA2DS2-VASc scores for predicting cerebral infarction in patients with non-valvular AF.

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Purpose: Accurate prediction of the progression to severe stroke in initially diagnosed nonsevere patients with acute-subacute anterior circulation nonlacuna ischemic infarction (ASACNLII) is important in making clinical decision. This study aimed to apply a machine learning method to predict if the initially diagnosed nonsevere patients with ASACNLII would progress to severe stroke by using diffusion-weighted images and clinical information on admission. Methods: This retrospective study enrolled 344 patients with ASACNLII from June 2017 to August 2020 on admission, and 108 cases progressed to severe stroke during hospitalization within 3-21 days. The entire data were randomized into a training set (n = 271) and an independent test set (n = 73). A U-Net neural network was employed for automatic segmentation and volume measurement of the ischemic lesions. Predictive models were developed and used for evaluating the progression to severe stroke using different feature sets (the volume data, the clinical data, and the combination) and machine learning methods (random forest, support vector machine, and logistic regression). Results: The U-Net showed high correlation with manual segmentation in terms of Dice coefficient of 0.806 and R 2 value of the volume measurements of 0.960 in the test set. The random forest classifier of the volume + clinical combination achieved the best area under the receiver operating characteristic curve of 0.8358 (95% CI 0.7321-0.9269), and the accuracy, sensitivity, and specificity were 0.7780 (0.7397-0.7945), 0.7695 (0.6102-0.9074), and 0.8686 (0.6923-1.0), respectively. The Shapley additive explanation diagram showed the volume variable as the most important predictor. Conclusion: The U-Net was fully automatic and showed a high correlation with manual segmentation. An integrated approach combining clinical variables and stroke lesion volumes that were derived from the advanced machine learning algorithms had high accuracy in predicting the progression to severe stroke in ASACNLII patients.

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The emergence of an aging society is inevitable due to the continued increases in life expectancy and decreases in birth rate. These social changes require new smart healthcare services for use in daily life, and COVID-19 has also led to a contactless trend necessitating more non-face-to-face health services. Due to the improvements that have been achieved in healthcare technologies, an increasing number of studies have attempted to predict and analyze certain diseases in advance. Research on stroke diseases is actively underway, particularly with the aging population. Stroke, which is fatal to the elderly, is a disease that requires continuous medical observation and monitoring, as its recurrence rate and mortality rate are very high. Most studies examining stroke disease to date have used MRI or CT images for simple classification. This clinical approach (imaging) is expensive and time-consuming while requiring bulky equipment. Recently, there has been increasing interest in using non-invasive measurable EEGs to compensate for these shortcomings. However, the prediction algorithms and processing procedures are both time-consuming because the raw data needs to be separated before the specific attributes can be obtained. Therefore, in this paper, we propose a new methodology that allows for the immediate application of deep learning models on raw EEG data without using the frequency properties of EEG. This proposed deep learning-based stroke disease prediction model was developed and trained with data collected from real-time EEG sensors. We implemented and compared different deep-learning models (LSTM, Bidirectional LSTM, CNN-LSTM, and CNN-Bidirectional LSTM) that are specialized in time series data classification and prediction. The experimental results confirmed that the raw EEG data, when wielded by the CNN-bidirectional LSTM model, can predict stroke with 94.0% accuracy with low FPR (6.0%) and FNR (5.7%), thus showing high confidence in our system. These experimental results demonstrate the feasibility of non-invasive methods that can easily measure brain waves alone to predict and monitor stroke diseases in real time during daily life. These findings are expected to lead to significant improvements for early stroke detection with reduced cost and discomfort compared to other measuring techniques.

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BACKGROUND: Strokes represent a leading cause of mortality globally. The evolution of developing new therapies is subject to safety and efficacy testing in clinical trials, which operate in a limited timeframe. To maximize the impact of these trials, patient cohorts for whom ischemic stroke is likely during that designated timeframe should be identified. Machine learning may improve upon existing candidate identification methods in order to maximize the impact of clinical trials for stroke prevention and treatment and improve patient safety. METHODS: A retrospective study was performed using 41,970 qualifying patient encounters with ischemic stroke from inpatient visits recorded from over 700 inpatient and ambulatory care sites. Patient data were extracted from electronic health records and used to train and test a gradient boosted machine learning algorithm (MLA) to predict the patients' risk of experiencing ischemic stroke from the period of 1 day up to 1 year following the patient encounter. The primary outcome of interest was the occurrence of ischemic stroke. RESULTS: After training for optimization, XGBoost obtained a specificity of 0.793, a positive predictive value (PPV) of 0.194, and a negative predictive value (NPV) of 0.985. The MLA further obtained an area under the receiver operating characteristic (AUROC) of 0.88. The Logistic Regression and multilayer perceptron models both achieved AUROCs of 0.862. Among features that significantly impacted the prediction of ischemic stroke were previous stroke history, age, and mean systolic blood pressure. CONCLUSION: MLAs have the potential to more accurately predict the near risk of ischemic stroke within a 1-year prediction window for individuals who have been hospitalized. This risk stratification tool can be used to design clinical trials to test stroke prevention treatments in high-risk populations by identifying subjects who would be more likely to benefit from treatment.

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BACKGROUND AND PURPOSE: Age and stroke severity as 2 independent predictors have been included in many well-known prediction models. However, whether the model consisting of the 2 variables derived from early arrival group could bring equal clinical benefit for those patients presented late was unclear. This study aimed to investigate the performance of this transformation. METHODS: We enrolled ischemic stroke patients admitted to our stroke center within 3 days after symptom onset from January 1, 2018 to March 31, 2019.These patients were divided into 2 groups, early arrival group within 6 hours after onset and late arrival group between 6 hours and 3 days. Two multivariate logistic regression models were developed consisting of the variable age and stroke severity. The primary outcome was the unfavorable outcome which defined as modified Rankin Scale score of 3-6. The differences of the performance of the models were compared through 3 aspects (discrimination, calibration, and clinical utility). RESULTS: Five-hundred seventeen ischemic stroke patients were included in our study. There were 258 patients reached in our stroke center within 6 hours while 259 patients were not. The area under the curve were .78 (95% confidence interval .70-.87) for the model developed in the early arrival group and .82 (95% confidence interval .73-.90) for the model developed in the late arrival group respectively. The models calibrated well in the late arrival group. As for clinical utility, the net benefit of the model developed in the early group was only slightly lower than the model developed in the late arrival group. CONCLUSIONS: The prediction model consisting of the variable age and stroke severity derived from the early arrival group patients had the potential to be applied directly in the patients presented late.

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BACKGROUND AND OBJECTIVE: Cerebral stroke has become a significant global public health issue in recent years. The ideal solution to this concern is to prevent in advance by controlling related metabolic factors. However, it is difficult for medical staff to decide whether special precautions are needed for a potential patient only based on the monitoring of physiological indicators unless they are obviously abnormal. This paper will develop a hybrid machine learning approach to predict cerebral stroke for clinical diagnosis based on the physiological data with incompleteness and class imbalance. METHODS: Two steps are involved in the whole process. Firstly, random forest regression is adopted to impute missing values before classification. Secondly, an automated hyperparameter optimization(AutoHPO) based on deep neural network(DNN) is applied to stroke prediction on an imbalanced dataset. RESULTS: The medical dataset contains 43,400 records of potential patients which includes 783 occurrences of stroke. The false negative rate from our prediction approach is only 19.1%, which has reduced by an average of 51.5% in comparison to other traditional approaches. The false positive rate, accuracy and sensitivity predicted by the proposed approach are respectively 33.1, 71.6, and 67.4%. CONCLUSION: The approach proposed in this paper has effectively reduced the false negative rate with a relatively high overall accuracy, which means a successful decrease in the misdiagnosis rate for stroke prediction. The results are more reliable and valid as the reference in stroke prognosis, and also can be acquired conveniently at a low cost.

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BACKGROUND: Atrial fibrillation (AF) is associated with an increased risk of cardioembolic stroke. The risk of cardioembolism is not adequately reduced with the administration of oral anticoagulants, since a number of patients continue to experience thromboembolic events despite receiving treatment. Therefore, identification of a circulating biomarker to identify these high-risk patients would be clinically beneficial. OBJECTIVE: In the present article, we aim to review the available data regarding use of biomarkers to predict cardioembolic stroke in patients with AF. METHODS: We performed a thorough search of the literature in order to analyze the biomarkers identified thus far and critically evaluate their clinical significance. RESULTS: A number of biomarkers have been proposed to predict cardioembolic stroke in patients with AF. Some of them are already used in the clinical practice, such as d-dimers, troponins and brain natriuretic peptide. Novel biomarkers, such as the inflammatory growth differentiation factor-15, appear to be promising, while the role of micro-RNAs and genetics appear to be useful as well. Even though these biomarkers are associated with an increased risk for thromboembolism, they cannot accurately predict future events. In light of this, the use of a scoring system, that would incorporate both circulating biomarkers and clinical factors, might be more useful. CONCLUSIONS: Recent research has disclosed several biomarkers as potential predictors of cardioembolic stroke in patients with AF. However, further research is required to establish a multifactorial scoring system that will identify patients at high-risk of thromboembolism, who would benefit from more intensive treatment and monitoring.

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BACKGROUND: As of 2014, stroke is the fourth leading cause of death in Japan. Predicting a future diagnosis of stroke would better enable proactive forms of healthcare measures to be taken. We aim to predict a diagnosis of stroke within one year of the patient's last set of exam results or medical diagnoses. METHODS: Around 8000 electronic health records were provided by Tsuyama Jifukai Tsuyama Chuo Hospital in Japan. These records contained non-homogeneous temporal data which were first transformed into a form usable by an algorithm. The transformed data were used as input into several neural network architectures designed to evaluate efficacy of the supplied data and also the networks' capability at exploiting relationships that could underlie the data. The prevalence of stroke cases resulted in imbalanced class outputs which resulted in trained neural network models being biased towards negative predictions. To address this issue, we designed and incorporated regularization terms into the standard cross-entropy loss function. These terms penalized false positive and false negative predictions. We evaluated the performance of our trained models using Receiver Operating Characteristic. RESULTS: The best neural network incorporated and combined the different sources of temporal data through a dual-input topology. This network attained area under the Receiver Operating Characteristic curve of 0.669. The custom regularization terms had a positive effect on the training process when compared against the standard cross-entropy loss function. CONCLUSIONS: The techniques we describe in this paper are viable and the developed models form part of the foundation of a national clinical decision support system.

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AIMS: The aorta is a major source of cerebral thromboembolism, but its role in stroke pathogenesis is not well understood due to its poor accessibility for non-invasive imaging. We examined whether thoracic aortic calcification (TAC), a marker of aortic plaque load, is associated with stroke in addition to established risk factors. METHODS AND RESULTS: A total of 3930 subjects from the population-based Heinz Nixdorf Recall study (45-75 years; 47.1% men) without previous stroke, coronary heart disease, or myocardial infarction were evaluated for incident stroke events over 109.0 ± 23.3 months. Cox proportional hazards regressions were used to examine associations with stroke of TAC in addition to established risk factors (age, sex, systolic blood pressure, LDL, HDL, diabetes, and smoking) and coronary artery calcification (CAC). 101 incident strokes occurred during the follow-up period. Subjects suffering a stroke had significantly higher TAC values at baseline than the remaining subjects (median = 83.1 [Q1;Q3 = 4.7;472.9] vs. 15.7 [0.0;117.1]; P < 0.001). In a multivariable Cox proportional hazards regression, log(TAC + 1) (hazards ratio [HR] = 1.09 [95% confidence interval = 1.00-1.19]; P = 0.044) was associated with stroke in addition to established risk factors. Further analyses revealed that log(DTAC + 1), i.e. calcification of the descending aorta (1.11 [1.02-1.20]; P = 0.016), but not log(ATAC + 1), i.e. calcification of the ascending aorta (1.02 [0.93-1.11]; P = 0.713), was associated with stroke. The HR for log(TAC + 1) decreased to 1.06 (0.97-1.16; P = 0.202), when log(CAC + 1) was also inserted into multivariable analyses. CONCLUSION: Calcification of the thoracic aorta, more specifically its descending segment, is associated with incident stroke in addition to established risk factors. CAC outperforms aortic calcification as a stroke predictor.

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BACKGROUND: The greatest potential to reduce the burden of stroke is by primary prevention of first-ever stroke, which constitutes three quarters of all stroke. In addition to population-wide prevention strategies (the 'mass' approach), the 'high risk' approach aims to identify individuals at risk of stroke and to modify their risk factors, and risk, accordingly. Current methods of assessing and modifying stroke risk are difficult to access and implement by the general population, amongst whom most future strokes will arise. To help reduce the burden of stroke on individuals and the population a new app, the Stroke Riskometer(TM) , has been developed. We aim to explore the validity of the app for predicting the risk of stroke compared with current best methods. METHODS: 752 stroke outcomes from a sample of 9501 individuals across three countries (New Zealand, Russia and the Netherlands) were utilized to investigate the performance of a novel stroke risk prediction tool algorithm (Stroke Riskometer(TM) ) compared with two established stroke risk score prediction algorithms (Framingham Stroke Risk Score [FSRS] and QStroke). We calculated the receiver operating characteristics (ROC) curves and area under the ROC curve (AUROC) with 95% confidence intervals, Harrels C-statistic and D-statistics for measure of discrimination, R(2) statistics to indicate level of variability accounted for by each prediction algorithm, the Hosmer-Lemeshow statistic for calibration, and the sensitivity and specificity of each algorithm. RESULTS: The Stroke Riskometer(TM) performed well against the FSRS five-year AUROC for both males (FSRS = 75.0% (95% CI 72.3%-77.6%), Stroke Riskometer(TM) = 74.0(95% CI 71.3%-76.7%) and females [FSRS = 70.3% (95% CI 67.9%-72.8%, Stroke Riskometer(TM) = 71.5% (95% CI 69.0%-73.9%)], and better than QStroke [males - 59.7% (95% CI 57.3%-62.0%) and comparable to females = 71.1% (95% CI 69.0%-73.1%)]. Discriminative ability of all algorithms was low (C-statistic ranging from 0.51-0.56, D-statistic ranging from 0.01-0.12). Hosmer-Lemeshow illustrated that all of the predicted risk scores were not well calibrated with the observed event data (P < 0.006). CONCLUSIONS: The Stroke Riskometer(TM) is comparable in performance for stroke prediction with FSRS and QStroke. All three algorithms performed equally poorly in predicting stroke events. The Stroke Riskometer(TM) will be continually developed and validated to address the need to improve the current stroke risk scoring systems to more accurately predict stroke, particularly by identifying robust ethnic/race ethnicity group and country specific risk factors.

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BACKGROUND: Predictors of future stroke events gain importance in vascular medicine. Herein, we investigated the value of the ankle-brachial index (ABI), a simple non-invasive marker of atherosclerosis, as stroke predictor in addition to established risk factors that are part of the Framingham risk score (FRS). METHODS: 4299 subjects from the population-based Heinz Nixdorf Recall study (45-75 years; 47.3% men) without previous stroke, coronary heart disease or myocardial infarcts were followed up for ischemic and hemorrhagic stroke events over 109.0±23.3 months. Cox proportional hazard regressions were used to evaluate ABI as stroke predictor in addition to established vascular risk factors (age, sex, systolic blood pressure, LDL, HDL, diabetes, smoking). RESULTS: 104 incident strokes (93 ischemic) occurred (incidence rate: 2.69/1000 person-years). Subjects suffering stroke had significantly lower ABI values at baseline than the remaining subjects (1.03±0.22 vs. 1.13±0.14, p<0.001). In a multivariable Cox regression, ABI predicted stroke in addition to classical risk factors (hazard ratio=0.77 per 0.1, 95% confidence interval=0.69-0.86). ABI predicted stroke events in subjects above and below 65 years, both in men and women. ABI specifically influenced stroke risk in subjects belonging to the highest (>13%) and intermediate (8-13%) FRS tercile. In these subjects, stroke incidence was 28.13 and 8.13/1000 person-years, respectively, for ABI<0.9, compared with 3.97 and 2.07/1000 person-years for 0.9≤ABI≤1.3. CONCLUSIONS: ABI predicts stroke in the general population, specifically in subjects with classical risk factors, where ABI identifies subjects at particularly high stroke risk.

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BACKGROUND: The risk of future stroke after transient ischemic attack (TIA) has been widely studied, but most findings were obtained for classically defined TIA (time-defined TIA). A new definition of TIA, that is, tissue-defined TIA, which requires the absence of fresh brain infarction on magnetic resonance imaging, could change stroke risk assessments. We, therefore, aimed to evaluate the risk of future stroke in patients with tissue-defined TIA. METHODS: We retrospectively reviewed 74 patients with tissue-defined TIA, who could be followed for 2 years. Clinical, laboratory, and radiological data were collected and compared between groups that did and did not develop ischemic stroke within the 2-year period. RESULTS: Ischemic stroke occurred in 11 patients (14.9%). Increased age, hemiparesis, and/or dysarthria during the TIA, old cerebral infarction revealed by magnetic resonance imaging, and large-artery stenosis detected by magnetic resonance angiography and/or ultrasonography tended to increase the risk of future stroke, but no individual factor showed statistically significant effect. TIA etiology did not significantly affect the risk. ABCD2 score, an established score for predicting stroke after time-defined TIA, showed only a weak association with future stroke. In contrast, new scores that we created reliably predicted future stroke; these included the APO (age, paresis, and old cerebral infarction) and APOL (age, paresis, old cerebral infarction, and large-artery stenosis) scores. The areas under the receiver operating characteristic curves were .662, .737, and .807 for ABCD2, APO, and APOL, respectively. CONCLUSIONS: Compared with the established measures, our newly created scores could predict future stroke for tissue-defined TIA more reliably.

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