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Background: Stroke misdiagnosis, associated with poor outcomes, is estimated to occur in 9% of all stroke patients. Objectives: We hypothesized that machine learning (ML) could assist in the diagnosis of ischemic stroke in emergency departments (EDs). Design: The study was conducted and reported according to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis guidelines. We performed model development and prospective temporal validation, using data from pre- and post-COVID periods; we also performed a case study on a small cohort of previously misdiagnosed stroke patients. Methods: We used structured and unstructured electronic health records (EHRs) of 56,452 patient encounters from 13 hospitals in Pennsylvania, from September 2003 to January 2021. ML pipelines, including natural language processing, were created using pre-event clinical data and provider notes in the EDs. Results: Using pre-event information, our model’s area under the receiver operating characteristics curve (AUROC) ranged from 0.88 to 0.92 with a similar range accuracy (0.87–0.90). Using provider notes, we identified five models that reached a balanced performance in terms of AUROC, sensitivity, and specificity. Model AUROC ranged from 0.93 to 0.99. Model sensitivity and specificity reached 0.90 and 0.99, respectively. Four of the top five performing models were based on the post-COVID provider notes; however, no performance difference between models tested on pre- and post-COVID was observed. Conclusion: This study leveraged pre-event and at-encounter level EHR for stroke prediction. The results indicate that available clinical information can be used for building EHR-based stroke prediction models and ED stroke alert systems.

Abstract With the expansion of electronic health records(EHR)-linked genomic data comes the development of machine learning-enable models. There is a pressing need to develop robust pipelines to evaluate the performance of integrated models and minimize systemic bias. We developed a prediction model of symptomatic Clostridioides difficile infection(CDI) by integrating common EHR-based and genetic risk factors(rs2227306/IL8). Our pipeline includes (1) leveraging phenotyping algorithm to minimize temporal bias, (2) performing simulation studies to determine the predictive power in samples without genetic information, (3) propensity score matching to control for the confoundings, (4) selecting machine learning algorithms to capture complex feature interactions, (5) performing oversampling to address data imbalance, and (6) optimizing models and ensuring proper bias-variance trade-off. We evaluate the performance of prediction models of CDI when including common clinical risk factors and the benefit of incorporating genetic feature(s) into the models. We emphasize the importance of building a robust integrated pipeline to avoid systemic bias and thoroughly evaluating genetic features when integrated into the prediction models in the general population and subgroups.

Abstract We aim to determine whether ischemic stroke(IS)-related PRSs are also associated with and further predict 3-year all-cause mortality. 1756 IS patients with European ancestry were randomly split into training (n = 1226) and testing (n = 530) groups with 3-year post-event observations. Univariate Cox proportional hazards regression model (CoxPH) was used for primary screening of individual prognostic PRSs. Only the significantly associated PRSs and clinical risk factors with the same direction for a causal relationship with IS were used to construct a multivariate CoxPH. Feature selection was conducted by the LASSO method. After feature selection, a prediction model with 11 disease-associated pathway-specific PRSs outperformed the base model, as demonstrated by a higher concordance index (0.751, 95%CI [0.693–0.809] versus 0.729, 95%CI [0.676–0.782]) in the testing sample. A PRS derived from endothelial cell apoptosis showed independent predictability in the multivariate CoxPH (Hazard Ratio = 1.193 [1.027–1.385], p = 0.021). These PRSs fine-tuned the model by better stratifying high, intermediate, and low-risk groups. Several pathway-specific PRSs were associated with clinical risk factors in an age-dependent manner and further confirmed some known etiologies of IS and all-cause mortality. In conclusion, Pathway-specific PRSs for IS are associated with all-cause mortality, and the integrated multivariate risk model provides prognostic value in this context.

Introduction SARS‐CoV‐2 infection may be associated with uncommon complications such as intracerebral hemorrhage (ICH), with a high mortality rate. We compared a series of hospitalized ICH cases infected with SARS‐CoV‐2 with a non‐SARS‐CoV‐2 infected control group and evaluated if the SARS‐CoV‐2 infection is a predictor of mortality in ICH patients. Methods In a multinational retrospective study, 63 cases of ICH in SARS‐CoV‐2 infected patients admitted to 13 tertiary centers from the beginning of the pandemic were collected. We compared the clinical and radiological characteristics and in‐hospital mortality of these patients with a control group of non‐SARS‐CoV‐2 infected ICH patients of a previous cohort from the country where the majority of cases were recruited. Results Among 63 ICH patients with SARS‐CoV‐2 infection, 23 (36.5%) were women. Compared to the non‐SARS‐CoV‐2 infected control group, in SARS‐CoV‐2 infected patients, ICH occurred at a younger age (61.4± 18.1 years versus 66.8± 16.2 years, P = 0.044). These patients had higher median ICH scores ([3 (IQR 2–4)] versus [2 (IQR 1–3)], P = 0.025), a more frequent history of diabetes (34% versus 16%, P = 0.007), and lower platelet counts (177.8± 77.8 × 109/L versus 240.5± 79.3 × 109/L, P< 0.001). The in‐hospital mortality was not significantly different between cases and controls (65% versus 62%, P = 0.658) in univariate analysis; however, SARS‐CoV‐2 infection was significantly associated with in‐hospital mortality (aOR = 4.3, 95% CI: 1.28‐14.52) in multivariable analysis adjusting for potential confounders. Conclusions Infection with SARS‐CoV‐2 may be associated with increased odds of in‐hospital mortality in ICH patients.

Abstract Optic Neuritis is rare in Lyme borreliosis. The current knowledge of optic nerve involvement in Lyme borreliosis relies solely on case reports. The aim of this systematic review was to characterize and investigate the associated factors of optic neuritis in Lyme borreliosis. We further presented a very rare case of isolated bilateral optic neuritis in a Lyme seropositive patient.

Abstract Laboratory data from Electronic Health Records (EHR) are often used in prediction models where estimation bias and model performance from missingness can be mitigated using imputation methods. We demonstrate the utility of imputation in two real-world EHR-derived cohorts of ischemic stroke from Geisinger and of heart failure from Sutter Health to: (1) characterize the patterns of missingness in laboratory variables; (2) simulate two missing mechanisms, arbitrary and monotone; (3) compare cross-sectional and multi-level multivariate missing imputation algorithms applied to laboratory data; (4) assess whether incorporation of latent information, derived from comorbidity data, can improve the performance of the algorithms. The latter was based on a case study of hemoglobin A1c under a univariate missing imputation framework. Overall, the pattern of missingness in EHR laboratory variables was not at random and was highly associated with patients’ comorbidity data; and the multi-level imputation algorithm showed smaller imputation error than the cross-sectional method.

With advances in high-throughput image processing technologies and increasing availability of medical mega-data, the growing field of radiomics opened the door for quantitative analysis of medical images for prediction of clinically relevant information. One clinical area in which radiomics have proven useful is stroke neuroimaging, where rapid treatment triage is vital for patient outcomes and automated decision assistance tools have potential for significant clinical impact. Recent research, for example, has applied radiomics features extracted from CT angiography (CTA) images and a machine learning framework to facilitate risk-stratification in acute stroke. We here provide methodological guidelines and radiomics data supporting the referenced article “CT angiographic radiomics signature for risk-stratification in anterior large vessel occlusion stroke.” The data were extracted from the stroke center registry at Yale New Haven Hospital between 1/1/2014 and 10/31/2020; and Geisinger Medical Center between 1/1/2016 and 12/31/2019. It includes detailed radiomics features of the anterior circulation territories on admission CTA scans in stroke patients with large vessel occlusion stroke who underwent thrombectomy. We also provide the methodological details of the analysis framework utilized for training, optimization, validation and external testing of the machine learning and feature selection algorithms. With the goal of advancing the feasibility and quality of radiomics-based analyses to improve patient care within and beyond the field of stroke, the provided data and methodological support can serve as a baseline for future studies applying radiomics algorithms to machine-learning frameworks, and allow for analysis and utilization of radiomics features extracted in this study.

Contrast-induced encephalopathy (CIE) is a rare complication that arises from exposure to iodinated contrast medium and can result in a range of symptoms, including cortical blindness, aphasia, focal neurological deficits, and altered mental status. We present 4 individual cases of CIE who presented with stroke-mimic symptoms following surgery with localized iodixanol or ioversol injection. We outline a clinical timeline of all patients, showing that CIE follows a general pattern of delayed onset, worsening symptomology, and ultimately full recovery. All patients received IV hydration, corticosteroids, or both as part of their treatment protocol.

Acute ischemic stroke impacts men and women differently. Here, the authors show how different lesion patterns in men and women are linked to the extent of stroke severity.

Background/Purpose: To prevent falling, a common incident with debilitating health consequences among stroke survivors, it is important to identify significant fall risk factors (FRFs) towards developing and implementing predictive and preventive strategies and guidelines. This review provides a systematic approach for identifying the relevant FRFs and shedding light on future directions of research.Methods: A systematic search was conducted in 5 popular research databases. Studies investigating the FRFs in the stroke community were evaluated to identify the commonality and trend of FRFs in the relevant literature.Results: twenty-seven relevant articles were reviewed and analyzed spanning the years 1995–2020. The results confirmed that the most common FRFs were age (21/27, i.e., considered in 21 out of 27 studies), gender (21/27), motion-related measures (19/27), motor function/impairment (17/27), balance-related measures (16/27), and cognitive impairment (11/27). Among these factors, motion-related measures had the highest rate of significance (i.e., 84% or 16/19). Due to the high commonality of balance/motion-related measures, we further analyzed these factors. We identified a trend reflecting that subjective tools are increasingly being replaced by simple objective measures (e.g., 10-m walk), and most recently by quantitative measures based on detailed motion analysis.Conclusion: There remains a gap for a standardized systematic approach for selecting relevant FRFs in stroke fall risk literature. This study provides an evidence-based methodology to identify the relevant risk factors, as well as their commonalities and trends. Three significant areas for future research on post stroke fall risk assessment have been identified: 1) further exploration the efficacy of quantitative detailed motion analysis; 2) implementation of inertial measurement units as a cost-effective and accessible tool in clinics and beyond; and 3) investigation of the capability of cognitive-motor dual-task paradigms and their association with FRFs.

Background purposeA substantial number of patients with acute ischemic stroke (AIS) experience multiple acute lesions (MAL). We here aimed to scrutinize MAL in a large radiologically deep-phenotyped cohort.Materials and methodsAnalyses relied upon imaging and clinical data from the international MRI-GENIE study. Imaging data comprised both Fluid-attenuated inversion recovery (FLAIR) for white matter hyperintensity (WMH) burden estimation and diffusion-weighted imaging (DWI) sequences for the assessment of acute stroke lesions. The initial step featured the systematic evaluation of occurrences of MAL within one and several vascular supply territories. Associations between MAL and important imaging and clinical characteristics were subsequently determined. The interaction effect between single and multiple lesion status and lesion volume was estimated by means of Bayesian hierarchical regression modeling for both stroke severity and functional outcome.ResultsWe analyzed 2,466 patients (age = 63.4 ± 14.8, 39% women), 49.7% of which presented with a single lesion. Another 37.4% experienced MAL in a single vascular territory, while 12.9% featured lesions in multiple vascular territories. Within most territories, MAL occurred as frequently as single lesions (ratio ∼1:1). Only the brainstem region comprised fewer patients with MAL (ratio 1:4). Patients with MAL presented with a significantly higher lesion volume and acute NIHSS (7.7 vs. 1.7 ml and 4 vs. 3, pFDR < 0.001). In contrast, patients with a single lesion were characterized by a significantly higher WMH burden (6.1 vs. 5.3 ml, pFDR = 0.048). Functional outcome did not differ significantly between patients with single versus multiple lesions. Bayesian analyses suggested that the association between lesion volume and stroke severity between single and multiple lesions was the same in case of anterior circulation stroke. In case of posterior circulation stroke, lesion volume was linked to a higher NIHSS only among those with MAL.ConclusionMultiple lesions, especially those within one vascular territory, occurred more frequently than previously reported. Overall, multiple lesions were distinctly linked to a higher acute stroke severity, a higher total DWI lesion volume and a lower WMH lesion volume. In posterior circulation stroke, lesion volume was linked to a higher stroke severity in multiple lesions only.

Abstract Background Hemiplegic migraines represent a heterogeneous disorder with various presentations. Hemiplegic migraines are classified as sporadic or familial based on the presence of family history, but both subtypes have an underlying genetic etiology. Mutations in the ATP1A2 gene are responsible for Familial Hemiplegic type 2 (FHM2) or the sporadic hemiplegic migraine (SHM) counterpart if there is no family history of the disorder. Manifestations include migraine with aura and hemiparesis along with a variety of other symptoms likely dependent upon the specific mutation(s) present. Case presentation We report the case of an adult man who presented with headache, aphasia, and right-sided weakness. Workup for stroke and various infectious agents was unremarkable during the patient’s extended hospital stay. We emphasize the changes in the Magnetic Resonance Imaging (MRI) over time and the delay from onset of symptoms to MRI changes in Isotropic Diffusion Map (commonly referred to as Diffusion Weighted Imaging (DWI)) as well as Apparent Diffusion Coefficient (ADC). Conclusions We provide a brief review of imaging findings correlated with signs/symptoms and specific mutations in the ATP1A2 gene reported in the literature. Description of the various mutations and consequential presentations may assist neurologists in identifying cases of Hemiplegic Migraine, which may include transient changes in ADC and DWI imaging throughout the course of an attack.

Abstract Background Transient ischemic attack (TIA) is a brief episode of neurological dysfunction resulting from cerebral ischemia not associated with permanent cerebral infarction. TIA is associated with high diagnostic errors because of the subjective nature of findings and the lack of clinical and imaging biomarkers. The goal of this study was to design and evaluate a novel multinomial classification model, based on a combination of feature selection mechanisms coupled with logistic regression, to predict the likelihood of TIA, TIA mimics, and minor stroke. Methods We conducted our modeling on consecutive patients who were evaluated in our health system with an initial diagnosis of TIA in a 9-month period. We established the final diagnoses after the clinical evaluation by independent verification from two stroke neurologists. We used Recursive Feature Elimination (RFE) and Least Absolute Shrinkage and Selection Operator (LASSO) for prediction modeling. Results The RFE-based classifier correctly predicts 78% of the overall observations. In particular, the classifier correctly identifies 68% of the cases labeled as “TIA mimic” and 83% of the “TIA” discharge diagnosis. The LASSO classifier had an overall accuracy of 74%. Both the RFE and LASSO-based classifiers tied or outperformed the ABCD2 score and the Diagnosis of TIA (DOT) score. With respect to predicting TIA, the RFE-based classifier has 61.1% accuracy, the LASSO-based classifier has 79.5% accuracy, whereas the DOT score applied to the dataset yields an accuracy of 63.1%. Conclusion The results of this pilot study indicate that a multinomial classification model, based on a combination of feature selection mechanisms coupled with logistic regression, can be used to effectively differentiate between TIA, TIA mimics, and minor stroke.

Abstract Background Intrathecal injection is a rare complication of spinal anesthesia and an underreported complication of epidural blood patches. Although there are other reported cases of intrathecal blood injection, these cases lack confirmatory imaging and others report injection of mixed blood with other agents. Case presentation We present a case report of post-laminectomy cerebrospinal fluid leak who underwent epidural blood patch placement. CT and MRI brain imaging was obtained, depicting intrathecal blood products. The patient had subsequent seizures and respiratory distress, received supportive care, and returned to baseline after several days. Conclusion The patient’s clinical course illustrates the potential complications of blood products within CSF, including seizures and respiratory distress, which improved with supportive care in this case. Importantly, to our knowledge, this is the only report that clearly depicts injection of purely blood products, without other confounding agents (such as gadolinium), into intrathecal space and with diffuse spread through the CSF as visualized on CT and MRI imaging.

Posterior reversible encephalopathy syndrome (PRES) is a neurological syndrome characterized by an altered level of consciousness, headaches, seizure, and visual changes. PRES has several different etiologies, including malignant hypertension, eclampsia, and certain medications. Here, we describe a 41-year-old woman who presented with altered mental status. She had a preliminary diagnosis of serotonin syndrome as she was on many different serotonin-sparing agents, but her imaging findings were consistent with PRES. After her medications were reviewed and the causative agent was removed, the patient’s neurological exam and imaging findings improved, and she returned to her baseline. To our knowledge, this is a unique case of PRES caused by serotonin syndrome secondary to venlafaxine usage.

Background: The history of intracranial hemorrhage (ICrH) is considered a contraindication for intravenous thrombolysis (IVT) among patients with acute ischemic stroke (AIS). Objective: This study aimed at comparing the safety of IVT among patients with and without a history of ICrH. Methods: We performed a systematic review of the literature. Data regarding all AIS patients with prior ICrH who received IVT were retrieved. Meta-analysis was performed to compare the rate of symptomatic hemorrhagic transformation (sHT), death within 90 days, and favorable and unfavorable 90-day functional outcomes based on modified Rankin Scale (mRS) among stroke patients with and without prior ICrH. Results: Out of 13,032 reviewed records, 7 studies were included in the systematic review and meta-analysis. Quantitative synthesis of data regarding the rate of sHT (5068 patients) revealed no significant difference between the two groups [odds ratio, OR: 1.55 (0.77, 3.12); p = 0.22]. However, a significantly higher risk of death within 90 days [OR: 3.91 (2.16, 7.08); p < 0.00001] and a significantly higher 90-day poor functional outcomes (mRS, 4–6) [OR: 1.57 (1.07, 2.30); p = 0.02] were observed among patients with prior ICrH. Likewise, the percentage of 90-day good functional outcomes (mRS, 0–1) was lower in the prior ICrH group [OR: 0.54 (0.35, 0.84); p = 0.06]. Subgroup analyses in patients with a history of ICrH (based on both patients’ medical history and imaging confirmation) revealed no significant between-group differences in rates of sHT. Also, sensitivity analysis consisting of only studies using standard-dose IVT showed no difference in sHT rates and 90-day outcomes between the two groups. There was no evidence of heterogeneity ( I 2 >50%) among included studies. Conclusion: The results of this study indicated that prior history of ICrH does not increase the risk of sHT post-IVT, but it is associated with a higher risk of death and poor functional outcomes in 90 days.

Background and Purpose: As “time is brain” in acute stroke triage, the need for automated prognostication tools continues to increase, particularly in rapidly expanding tele-stroke settings. We aimed to create an automated prognostication tool for anterior circulation large vessel occlusion (LVO) stroke based on admission CTA radiomics. Methods: We automatically extracted 1116 radiomics features from the anterior circulation territory on admission CTAs of 829 acute LVO stroke patients who underwent mechanical thrombectomy in two academic centers. We trained, optimized, validated, and compared different machine-learning models to predict favorable outcome (modified Rankin Scale ≤ 2) at discharge and 3-month follow-up using four different input sets: “Radiomics”, “Radiomics + Treatment” (radiomics, post-thrombectomy reperfusion grade, and intravenous thrombolysis), “Clinical + Treatment” (baseline clinical variables and treatment), and “Combined” (radiomics, treatment, and baseline clinical variables). Results: For discharge outcome prediction, models were optimized/trained on n = 494 and tested on an independent cohort of n = 100 patients from Yale. Receiver operating characteristic analysis of the independent cohort showed no significant difference between best-performing Combined input models (area under the curve, AUC = 0.77) versus Radiomics + Treatment (AUC = 0.78, p = 0.78), Radiomics (AUC = 0.78, p = 0.55), or Clinical + Treatment (AUC = 0.77, p = 0.87) models. For 3-month outcome prediction, models were optimized/trained on n = 373 and tested on an independent cohort from Yale (n = 72), and an external cohort from Geisinger Medical Center (n = 232). In the independent cohort, there was no significant difference between Combined input models (AUC = 0.76) versus Radiomics + Treatment (AUC = 0.72, p = 0.39), Radiomics (AUC = 0.72, p = 0.39), or Clinical + Treatment (AUC = 76, p = 0.90) models; however, in the external cohort, the Combined model (AUC = 0.74) outperformed Radiomics + Treatment (AUC = 0.66, p

Objective: To personalize the prognostication of post-stroke outcome using MRI-detected cerebrovascular pathology, we sought to investigate the association between the excessive white matter hyperintensity (WMH) burden unaccounted for by the traditional stroke risk profile of individual patients and their long-term functional outcomes after a stroke.Methods: We included 890 patients who survived after an acute ischemic stroke from the MRI-Genetics Interface Exploration (MRI-GENIE) study, for whom data on vascular risk factors (VRFs), including age, sex, atrial fibrillation, diabetes mellitus, hypertension, coronary artery disease, smoking, prior stroke history, as well as acute stroke severity, 3- to−6-month modified Rankin Scale score (mRS), WMH, and brain volumes, were available. We defined the unaccounted WMH (uWMH) burden via modeling of expected WMH burden based on the VRF profile of each individual patient. The association of uWMH and mRS score was analyzed by linear regression analysis. The odds ratios of patients who achieved full functional independence (mRS < 2) in between trichotomized uWMH burden groups were calculated by pair-wise comparisons.Results: The expected WMH volume was estimated with respect to known VRFs. The uWMH burden was associated with a long-term functional outcome (β = 0.104, p < 0.01). Excessive uWMH burden significantly reduced the odds of achieving full functional independence after a stroke compared to the low and average uWMH burden [OR = 0.4, 95% CI: (0.25, 0.63), p < 0.01 and OR = 0.61, 95% CI: (0.42, 0.87), p < 0.01, respectively].Conclusion: The excessive amount of uWMH burden unaccounted for by the traditional VRF profile was associated with worse post-stroke functional outcomes. Further studies are needed to evaluate a lifetime brain injury reflected in WMH unrelated to the VRF profile of a patient as an important factor for stroke recovery and a plausible indicator of brain health.

Background: Ischemic and hemorrhagic stroke are associated with a high rate of long-term disability and death. Recent investigations focus efforts to better understand how alterations in gut microbiota composition influence clinical outcomes. A key metabolite, trimethylamine N-oxide (TMAO), is linked to multiple inflammatory, vascular, and oxidative pathways. The current biochemical underpinnings of microbial effects on stroke remain largely understudied. The goal of our study is to explore the current literature to explain the interactions between the human gut microbiome and stroke progression, recovery, and outcome. We also provide a descriptive review of TMAO.Methods: A systematic literature search of published articles between January 1, 1990, and March 22, 2020, was performed on the PubMed database to identify studies addressing the role of the microbiome and TMAO in the pathogenesis and recovery of acute stroke. Our initial investigation focused on human subject studies and was further expanded to include animal studies. Relevant articles were included, regardless of study design. The analysis included reviewers classifying and presenting selected articles by study design and sample size in a chart format.Results: A total of 222 titles and abstracts were screened. A review of the 68 original human subject articles resulted in the inclusion of 24 studies in this review. To provide further insight into TMAO as a key player, an additional 40 articles were also reviewed and included. Our findings highlighted that alterations in richness and abundance of gut microbes and increased plasma TMAO play an important role in vascular events and outcomes. Our analysis revealed that restoration of a healthy gut, through targeted TMAO-reducing therapies, could provide alternative secondary prevention for at-risk patients.Discussion: Biochemical interactions between the gut microbiome and inflammation, resulting in metabolic derangements, can affect stroke progression and outcomes. Clinical evidence supports the importance of TMAO in modulating underlying stroke risk factors. Lack of standardization and distinct differences in sample sizes among studies are major limitations.

ObjectiveNeuroimaging measurements of brain structural integrity are thought to be surrogates for brain health, but precise assessments require dedicated advanced image acquisitions. By means of quantitatively describing conventional images, radiomic analyses hold potential for evaluating brain health. We sought to: (1) evaluate radiomics to assess brain structural integrity by predicting white matter hyperintensities burdens (WMH) and (2) uncover associations between predictive radiomic features and clinical phenotypes.MethodsWe analyzed a multi-site cohort of 4,163 acute ischemic strokes (AIS) patients with T2-FLAIR MR images with total brain and WMH segmentations. Radiomic features were extracted from normal-appearing brain tissue (brain mask–WMH mask). Radiomics-based prediction of personalized WMH burden was done using ElasticNet linear regression. We built a radiomic signature of WMH with stable selected features predictive of WMH burden and then related this signature to clinical variables using canonical correlation analysis (CCA).ResultsRadiomic features were predictive of WMH burden (R2 = 0.855 ± 0.011). Seven pairs of canonical variates (CV) significantly correlated the radiomics signature of WMH and clinical traits with respective canonical correlations of 0.81, 0.65, 0.42, 0.24, 0.20, 0.15, and 0.15 (FDR-corrected p-valuesCV1–6 < 0.001, p-valueCV7 = 0.012). The clinical CV1 was mainly influenced by age, CV2 by sex, CV3 by history of smoking and diabetes, CV4 by hypertension, CV5 by atrial fibrillation (AF) and diabetes, CV6 by coronary artery disease (CAD), and CV7 by CAD and diabetes.ConclusionRadiomics extracted from T2-FLAIR images of AIS patients capture microstructural damage of the cerebral parenchyma and correlate with clinical phenotypes, suggesting different radiographical textural abnormalities per cardiovascular risk profile. Further research could evaluate radiomics to predict the progression of WMH and for the follow-up of stroke patients’ brain health.

Abstract Prior studies suggest an association between Vitamin K antagonists (VKA) and cerebral microbleeds (CMBs); less is known about nonvitamin K oral anticoagulants (NOACs). In this observational study we describe CMB profiles in a multicenter cohort of 89 anticoagulation‐related intracerebral hemorrhage (ICH) patients. CMB prevalence was 51% (52% in VKA‐ICH, 48% in NOAC‐ICH). NOAC‐ICH patients had lower median CMB count [2(IQR:1–3) vs. 7(4–11); P

Despite reports of a high incidence and various predictors of post-stroke depression (PSD), the underdiagnosis and undertreatment rates of PSD are still high. This study aimed to examine the incidence of depression in stroke patients and identify factors associated with PSD. This was a retrospective cohort study on ischemic stroke patients from the Geisinger Neuroscience Ischemic Stroke (GNSIS) registry. The following statistical analyses were performed to predict PSD in the studied population: a Kaplan–Meier estimator and a Cox proportional hazards model. A total of 5882 patients were included in the study. The median age at the time of an ischemic stroke was 72 years and 56% were males. A total of 294 patients were diagnosed with PSD within one year of a stroke. The cumulative incidence of depression was found to be 6.4% (95% CI 5.7–7.1%) at one year for the entire cohort. Women were found to have a higher risk of PSD than men (HR for women = 1.47, 95% CI 1.18–1.85, p = 0.001). A history of prior stroke (HR = 1.58, 95% CI 1.18–2.11, p = 0.002) and myocardial infarction (HR = 1.47, 95% CI 1.05–2.06, p = 0.025) were associated with PSD. Medicaid patients had a higher risk for PSD (HR = 2.16, 95% CI 1.5–3.12, p < 0.001) than those with commercial insurance or health maintenance organization plans. Our findings showed that women, patients with a history of prior stroke or myocardial infarction, and with Medicaid insurance were more likely to develop PSD. Through an observational study on the EHR data, we confirmed that chronic stress, including financial and health-related stress, irrespective of age, significantly increased the risk for PSD.

Since the early days of the pandemic, there have been several reports of cerebrovascular complications during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Numerous studies proposed a role for SARS-CoV-2 in igniting stroke. In this review, we focused on the pathoetiology of stroke among the infected patients. We pictured the results of the SARS-CoV-2 invasion to the central nervous system (CNS) via neuronal and hematogenous routes, in addition to viral infection in peripheral tissues with extensive crosstalk with the CNS. SARS-CoV-2 infection results in pro-inflammatory cytokine and chemokine release and activation of the immune system, COVID-19-associated coagulopathy, endotheliitis and vasculitis, hypoxia, imbalance in the renin-angiotensin system, and cardiovascular complications that all may lead to the incidence of stroke. Critically ill patients, those with pre-existing comorbidities and patients taking certain medications, such as drugs with elevated risk for arrhythmia or thrombophilia, are more susceptible to a stroke after SARS-CoV-2 infection. By providing a pictorial narrative review, we illustrated these associations in detail to broaden the scope of our understanding of stroke in SARS-CoV-2-infected patients. We also discussed the role of antiplatelets and anticoagulants for stroke prevention and the need for a personalized approach among patients with SARS-CoV-2 infection.

Background and Purpose: Hospital readmissions impose a substantial burden on the healthcare system. Reducing readmissions after stroke could lead to improved quality of care especially since stroke is associated with a high rate of readmission. The goal of this study is to enhance our understanding of the predictors of 30-day readmission after ischemic stroke and develop models to identify high-risk individuals for targeted interventions.Methods: We used patient-level data from electronic health records (EHR), five machine learning algorithms (random forest, gradient boosting machine, extreme gradient boosting–XGBoost, support vector machine, and logistic regression-LR), data-driven feature selection strategy, and adaptive sampling to develop 15 models of 30-day readmission after ischemic stroke. We further identified important clinical variables.Results: We included 3,184 patients with ischemic stroke (mean age: 71 ± 13.90 years, men: 51.06%). Among the 61 clinical variables included in the model, the National Institutes of Health Stroke Scale score above 24, insert indwelling urinary catheter, hypercoagulable state, and percutaneous gastrostomy had the highest importance score. The Model's AUC (area under the curve) for predicting 30-day readmission was 0.74 (95%CI: 0.64–0.78) with PPV of 0.43 when the XGBoost algorithm was used with ROSE-sampling. The balance between specificity and sensitivity improved through the sampling strategy. The best sensitivity was achieved with LR when optimized with feature selection and ROSE-sampling (AUC: 0.64, sensitivity: 0.53, specificity: 0.69).Conclusions: Machine learning-based models can be designed to predict 30-day readmission after stroke using structured data from EHR. Among the algorithms analyzed, XGBoost with ROSE-sampling had the best performance in terms of AUC while LR with ROSE-sampling and feature selection had the best sensitivity. Clinical variables highly associated with 30-day readmission could be targeted for personalized interventions. Depending on healthcare systems' resources and criteria, models with optimized performance metrics can be implemented to improve outcomes.

Background and purposeObesity is an established risk factor for ischemic stroke but the association of increased body mass index (BMI) with survival after ischemic stroke remains controversial. Many studies have shown that increased BMI has a "protective" effect on survival after stroke while other studies have debunked the "obesity paradox". This study aimed at examining the relationship between BMI and all-cause mortality at one year in first-time ischemic stroke patients using a large dataset extracted from different resources including electronic health records.MethodsThis was a retrospective cohort study of consecutive ischemic stroke patients captured in our Geisinger NeuroScience Ischemic Stroke (GNSIS) database. Survival in first-time ischemic stroke patients in different BMI categories was analyzed using Kaplan Meier survival curves. The predictors of mortality at one-year were assessed using a stratified Cox proportional hazards model.ResultsAmong 6,703 first-time ischemic stroke patients, overweight and obese patients were found to have statistically decreased hazard ratio (HR) compared to the non-overweight patients (overweight patients- HR = 0.61 [95% CI, 0.52-0.72]; obese patients- HR = 0.56 [95% CI, 0.48-0.67]). Predictors with a significant increase in the hazard ratio for one-year mortality were age at the ischemic stroke event, history of neoplasm, atrial fibrillation/flutter, diabetes, myocardial infarction and heart failure.ConclusionOur study results support the obesity paradox in ischemic stroke patients as shown by a significantly decreased hazard ratio for one-year mortality among overweight and obese patients in comparison to non-overweight patients.

Neurological manifestations are not uncommon during infection with the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). A clear association has been reported between cerebrovascular disease and coronavirus disease 2019 (COVID-19). However, whether this association is causal or incidental is still unknown. In this narrative review, we sought to present the possible pathophysiological mechanisms linking COVID-19 and cerebrovascular disease, describe the stroke syndromes and their prognosis and discuss several clinical, radiological, and laboratory characteristics that may aid in the prompt recognition of cerebrovascular disease during COVID-19. A systematic literature search was conducted, and relevant information was abstracted. Angiotensin-converting enzyme-2 receptor dysregulation, uncontrollable immune reaction and inflammation, coagulopathy, COVID-19-associated cardiac injury with subsequent cardio-embolism, complications due to critical illness and prolonged hospitalization can all contribute as potential etiopathogenic mechanisms leading to diverse cerebrovascular clinical manifestations. Acute ischemic stroke, intracerebral hemorrhage, and cerebral venous sinus thrombosis have been described in case reports and cohorts of COVID-19 patients with a prevalence ranging between 0.5% and 5%. SARS-CoV-2-positive stroke patients have higher mortality rates, worse functional outcomes at discharge and longer duration of hospitalization as compared with SARS-CoV-2-negative stroke patients in different cohort studies. Specific demographic, clinical, laboratory and radiological characteristics may be used as ‘red flags’ to alarm clinicians in recognizing COVID-19-related stroke.

Background: Several studies suggest women may be disproportionately affected by poorer stroke outcomes than men. This study aims to investigate whether women have a higher risk of all-cause mortality and recurrence after an ischemic stroke than men in a rural population in central Pennsylvania, United States. Methods: We analyzed consecutive ischemic stroke patients captured in the Geisinger NeuroScience Ischemic Stroke research database from 2004 to 2019. Kaplan–Meier (KM) estimator curves stratified by gender and age were used to plot survival probabilities and Cox Proportional Hazards Ratios were used to analyze outcomes of all-cause mortality and the composite outcome of ischemic stroke recurrence or death. Fine–Gray Competing Risk models were used for the outcome of recurrent ischemic stroke, with death as the competing risk. Two models were generated; Model 1 was adjusted by data-driven associated health factors, and Model 2 was adjusted by traditional vascular risk factors. Results: Among 8900 adult ischemic stroke patients [median age of 71.6 (interquartile range: 61.1–81.2) years and 48% women], women had a higher crude all-cause mortality. The KM curves demonstrated a 63.3% survival in women compared with a 65.7% survival in men ( p = 0.003) at 5 years; however, the survival difference was not present after controlling for covariates, including age, atrial fibrillation or flutter, myocardial infarction, diabetes mellitus, dyslipidemia, heart failure, chronic lung diseases, rheumatic disease, chronic kidney disease, neoplasm, peripheral vascular disease, past ischemic stroke, past hemorrhagic stroke, and depression. There was no adjusted or unadjusted sex difference in terms of recurrent ischemic stroke or composite outcome. Conclusion: Sex was not an independent risk factor for all-cause mortality and ischemic stroke recurrence in the rural population in central Pennsylvania.

Background: There is an increased attention to stroke following SARS-CoV-2. The goal of this study was to better depict the short-term risk of stroke and its associated factors among SARS-CoV-2 hospitalized patients. Methods: This multicentre, multinational observational study includes hospitalized SARS-CoV-2 patients from North and South America (United States, Canada, and Brazil), Europe (Greece, Italy, Finland, and Turkey), Asia (Lebanon, Iran, and India), and Oceania (New Zealand). The outcome was the risk of subsequent stroke. Centres were included by non-probability sampling. The counts and clinical characteristics including laboratory findings and imaging of the patients with and without a subsequent stroke were recorded according to a predefined protocol. Quality, risk of bias, and heterogeneity assessments were conducted according to ROBINS-E and Cochrane Q-test. The risk of subsequent stroke was estimated through meta-analyses with random effect models. Bivariate logistic regression was used to determine the parameters with predictive outcome value. The study was reported according to the STROBE, MOOSE, and EQUATOR guidelines. Findings: We received data from 26,175 hospitalized SARS-CoV-2 patients from 99 tertiary centres in 65 regions of 11 countries until May 1st, 2020. A total of 17,799 patients were included in meta-analyses. Among them, 156(0.9%) patients had a stroke—123(79%) ischaemic stroke, 27(17%) intracerebral/subarachnoid hemorrhage, and 6(4%) cerebral sinus thrombosis. Subsequent stroke risks calculated with meta-analyses, under low to moderate heterogeneity, were 0.5% among all centres in all countries, and 0.7% among countries with higher health expenditures. The need for mechanical ventilation (OR: 1.9, 95% CI:1.1–3.5, p = 0.03) and the presence of ischaemic heart disease (OR: 2.5, 95% CI:1.4–4.7, p = 0.006) were predictive of stroke. Interpretation: The results of this multi-national study on hospitalized patients with SARS-CoV-2 infection indicated an overall stroke risk of 0.5%(pooled risk: 0.9%). The need for mechanical ventilation and the history of ischaemic heart disease are the independent predictors of stroke among SARS-CoV-2 patients. Funding: None.

Introduction: Cerebral autosomal dominant arteriopathy and subcortical infarct leukoencephalopathy (CADASIL) is the most common form of hereditary stroke caused by a mutation in the NOTCH3 gene located on the short arm of chromosome 19. A small number of published reports describe CADASIL patients who were initially diagnosed as multiple sclerosis. Although it was previously indicated that there was no association between NOTCH3 mutations and multiple sclerosis, the involvement of autoimmune mechanisms among patients with CADASIL has been hypothesized.Case Presentation: Case 1 is a middle-aged woman with initial diagnoses of multiple sclerosis (MS) and myelitis that continued to progress despite treatment with disease-modifying agents. She had occasional migraines, transient blurred vision, and multiple lacunar infarcts. She continued treatment for about 15 years with no significant alleviation and progressive changes on brain MRI; genetic testing was ordered which showed NOTCH3 mutation, and diagnosis was changed to CADASIL with subsequent revision of treatment course. However, the presence of myelitis in this patient is unusual and may raise the question of a concurrent autoimmune process. Case 2 is a woman presenting with vertigo and paresthesia and diagnosed with MS based on an initial brain MRI showing biventricular white matter hyperintensities; however, she was not started on any disease-modifying agents. Her symptoms were reevaluated by a neurologist, and genetic testing was performed for NOTCH 3. Case 3 is a young woman with a history of migraines who initially presented with numbness and gait ataxia which later progressed to speech difficulty and memory loss. A diagnosis of MS was established which was later changed to CADASIL.Conclusion: Since CADASIL is a rare disease, it is imperative to raise awareness of its unique clinical condition as well as variation in its clinical presentations. It is crucial that the overlapping symptoms between MS and CADASIL be thoroughly examined to avoid misdiagnosis and treatment complications. The involvement of autoimmune mechanisms in CADASIL and the role of NOTCH3 gene mutations in provoking an autoimmune process should be further investigated.

Stroke is the fifth leading cause of death in the United States and a major cause of severe disability worldwide. Yet, recognizing the signs of stroke in an acute setting is still challenging and leads to loss of opportunity to intervene, given the narrow therapeutic window. A decision support system using artificial intelligence (AI) and clinical data from electronic health records combined with patients’ presenting symptoms can be designed to support emergency department providers in stroke diagnosis and subsequently reduce the treatment delay. In this article, we present a practical framework to develop a decision support system using AI by reflecting on the various stages, which could eventually improve patient care and outcome. We also discuss the technical, operational, and ethical challenges of the process.

The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in Wuhan, China and rapidly spread worldwide, with a vast majority of confirmed cases presenting with respiratory symptoms. Potential neurological manifestations and their pathophysiological mechanisms have not been thoroughly established. In this narrative review, we sought to present the neurological manifestations associated with coronavirus disease 2019 (COVID-19). Case reports, case series, editorials, reviews, case-control and cohort studies were evaluated, and relevant information was abstracted. Various reports of neurological manifestations of previous coronavirus epidemics provide a roadmap regarding potential neurological complications of COVID-19, due to many shared characteristics between these viruses and SARS-CoV-2. Studies from the current pandemic are accumulating and report COVID-19 patients presenting with dizziness, headache, myalgias, hypogeusia and hyposmia, but also with more serious manifestations including polyneuropathy, myositis, cerebrovascular diseases, encephalitis and encephalopathy. However, discrimination between causal relationship and incidental comorbidity is often difficult. Severe COVID-19 shares common risk factors with cerebrovascular diseases, and it is currently unclear whether the infection per se represents an independent stroke risk factor. Regardless of any direct or indirect neurological manifestations, the COVID-19 pandemic has a huge impact on the management of neurological patients, whether infected or not. In particular, the majority of stroke services worldwide have been negatively influenced in terms of care delivery and fear to access healthcare services. The effect on healthcare quality in the field of other neurological diseases is additionally evaluated.

Nutrition plays a vital role in health and the recovery process. Deficiencies in macronutrients and micronutrients can impact the development and progression of various disorders. However, malnutrition screening tools and their utility in the clinical setting remain largely understudied. In this study, we summarize the importance of nutritional adequacy and its association with neurological, cardiovascular, and immune-related disorders. We also examine general and specific malnutrition assessment tools utilized in healthcare settings. Since the implementation of the screening process in 2016, malnutrition data from hospitalized patients in the Geisinger Health System is presented and discussed as a case study. Clinical data from five Geisinger hospitals shows that ~10% of all admitted patients are acknowledged for having some form of nutritional deficiency, from which about 60–80% of the patients are targeted for a more comprehensive assessment. Finally, we conclude that with a reflection on how technological advances, specifically machine learning-based algorithms, can be integrated into electronic health records to provide decision support system to care providers in the identification and management of patients at higher risk of malnutrition.

Introduction: The risk and benefit of restarting oral anticoagulation (OAC) therapy among patients with atrial fibrillation or flutter (AF) and an episode of anticoagulation-associated intracerebral hemorrhage (ICH) remain unclear. Whether or not to resume OAC after an OAC-associated ICH will remain an unanswered clinical question until we have sufficient data through randomized clinical trials. Here, we analyzed the long-term outcome of patients with AF who did or did not resume OAC after an OAC-associated ICH. Patients and methods: We studied consecutive patients with AF who were discharged from our institution after an OAC-associated ICH event between 2010 and 2017. Baseline characteristics of patients, past medical history, and history or OAC use were recorded. Outcome measures in our study included recurrent ICH, ischemic stroke or systemic emboli, and death. Results: Out of 115 patients with AF and OAC-associated ICH, 93 patients (mean age 76.2 ± 10.3 years [44–91 years old], 54.3% men) were included in this study. Thirty-eight (40.9%) patients resumed OAC after the episode of OAC-associated ICH. More than 70% of patients had resumed OAC within two months of ICH (mean delay 56.0 ± 52.5 days). There was no significant difference between the group who resumed OAC and the group who did not in terms of mean follow-up duration (1.9 vs. 2.4 years), the type of initial ICH, as well as history of hypertension, diabetes, previous ischemic stroke, congestive heart failure, coronary artery disease, and tobacco use. There was no significant difference between the two groups considering the incidence rate of recurrent ICH (relative risk 2.9; 95% CI, 0.3–30.8). There was also no significant difference between the two groups regarding the incidence rate of ischemic stroke or systemic emboli (relative risk 0.9; 95% CI, 0.3–2.7). There was no significate difference between patients who did and did not resume OAC was 96 and 121 per 1000 patient-years, respectively (relative risk 0.8; 95% CI, 0.3–1.9). Conclusions: We did not observe any significant difference between the group of patients who resumed OAC and the patients who did not in terms of recurrent ICH, ischemic stroke or systemic emboli, and death. However, there was a tendency toward a higher long-term risk of recurrent ICH among patients who resumed OAC. Keywords: Anticoagulation, Intracerebral hemorrhage, Atrial fibrillation

The MGP single nucleotide polymorphism (SNP) rs1800801 has previously been associated with recurrent ischemic stroke in a Spanish cohort. Here, we tested for association of this SNP with ischemic stroke recurrence in a North American Caucasian cohort. Acute ischemic stroke patients admitted between 10/2009 and 12/2016 at three hospitals within a large healthcare system in the northeastern United States that were enrolled in a healthcare system-wide exome sequencing program were retrospectively reviewed. Patients with recurrent stroke within 1 year after index event were compared to those without recurrence. Of 9,348 suspected acute ischemic strokes admitted between 10/2009 and 12/2016, 1,727 (18.5%) enrolled in the exome-sequencing program. Among those, 1,068 patients had exome sequencing completed and were eligible for inclusion. Recurrent stroke within the first year of stroke was observed in 79 patients (7.4%). In multivariable analysis, stroke prior to the index stroke (OR 9.694, 95% CI 5.793-16.224, p ≤ 0.001), pro-coagulant status (OR = 3.563, 95% CI 1.504-8.443, p = 0.004) and the AA genotype of SNP rs1800801 (OR = 2.408, 95% CI 1.079-4.389, p = 0.004) were independently associated with recurrent stroke within the first year. The AA genotype of the MGP SNP rs1800801 is associated with recurrence within the first year after ischemic stroke in North American Caucasians. Study of stroke subtypes and additional populations will be required to determine if incorporation of allelic status at this SNP into current risk scores improves prediction of recurrent ischemic stroke.

Background: Paroxysmal atrial fibrillation (PAF) or flutter is prevalent among patients with cryptogenic stroke. The goal of this study was to investigate the feasibility of incorporating a fast-track, long term continuous heart monitoring (LTCM) program within a stroke clinic.Method: We designed and implemented a fast-track LTCM program in our stroke clinics. The instrument that we used for the study was the ZioXT® device from IRhythm™ Technologies. To implement the program, all clinic support staff received training on the skin preparation and proper placement of the device. We prospectively followed every patient who had a request from one of our inpatient or outpatient stroke or neurology providers to receive LTCM. We recorded patients' demographics, the LTCM indication, as well as related quality measures including same-visit placement, wearing time, analyzable time, LTCM application to the preliminary finding time, as well as patients' out of pocket cost.Results: Out of 501 patients included in the study, 467 (93.2%) patients (mean age 65.9 ± 13; men: 48%) received LTCM; and 92.5% of the patients had the diagnosis of stroke or TIA. 93.7% of patients received their LTCM during the same outpatient visit in the stroke clinic. The mean wearing time for LTCM was 12.1 days (out of 14 days). The average analyzable time among our patients was 95.0%. Eighteen (3.9%, 95%CI: 2.4–6.0) patients had at least one episode of PAF that was sustained for more than 30 s. The rate of PAF was 5.9% (95% CI: 3.5–9.2) among patients with the diagnosis of stroke. Out of 467 patients, 392 (84%) had an out-of-pocket cost of < $100.Conclusion: It is feasible to implement a fast-track cardiac monitoring as part of a stroke clinic with proper training of stroke providers, clinic staff, and support from a cardiology team.

Posterior inferior cerebellar artery (PICA) aneurysms are rare. The most common complication of intracranial aneurysms is rupture causing subarachnoid hemorrhage. Ischemic infarct, although more common in giant thrombosed aneurysms, is a very rare manifestation of small intracranial aneurysms. Here we describe a patient who presented with lateral medullary acute infarction associated with an ipsilateral, small (4 × 3.5 mm), unruptured and non-thrombosed PICA aneurysm.

Introduction: Recurrent stroke has a higher rate of death and disability. A number of risk scores have been developed to predict short-term and long-term risk of stroke following an initial episode of stroke or transient ischemic attack (TIA) with limited clinical utilities. In this paper, we review different risk score models and discuss their validity and clinical utilities.Methods: The PubMed bibliographic database was searched for original research articles on the various risk scores for risk of stroke following an initial episode of stroke or TIA. The validation of the models was evaluated by examining the internal and external validation process as well as statistical methodology, the study power, as well as the accuracy and metrics such as sensitivity and specificity.Results: Different risk score models have been derived from different study populations. Validation studies for these risk scores have produced conflicting results. Currently, ABCD2 score with diffusion weighted imaging (DWI) and Recurrence Risk Estimator at 90 days (RRE-90) are the two acceptable models for short-term risk prediction whereas Essen Stroke Risk Score (ESRS) and Stroke Prognosis Instrument-II (SPI-II) can be useful for prediction of long-term risk.Conclusion: The clinical risk scores that currently exist for predicting short-term and long-term risk of recurrent cerebral ischemia are limited in their performance and clinical utilities. There is a need for a better predictive tool which can overcome the limitations of current predictive models. Application of machine learning methods in combination with electronic health records may provide platform for development of new-generation predictive tools.

Background and purpose: The goal of this study was to investigate the rate and associated factors of Transient Ischemic Attack (TIA) misdiagnosis. Methods: We retrospectively analyzed consecutive patients with an initial diagnosis of TIA in the emergency department (ED) in a 9-month period. All hospitalized TIA patients were evaluated by a neurologist within 24 h and had at least one hospital discharge follow-up visit within three months. Patients' clinical data and neuroimaging were reviewed. The final diagnosis was independently verified by two stroke neurologists. Results: Out of 276 patients with the initial diagnosis of TIA, 254 patients (mean age 68.7 ± 15.4 years, 40.9% male, 25.2% final diagnosis of TIA) were included in the analysis. Twenty-four patients (9.4%) were referred to our rapid-access TIA clinic. The rate of TIA misdiagnosis among TIA clinic referred patients was 45.8%. Among the 230 patients in inpatient setting, the rate of TIA misdiagnosis was 60.0%. A hospital discharge diagnosis of TIA was observed in 54.3% of hospitalized patients; however, only 24.8% had the final diagnosis of TIA. Among hospitalized patients, the univariate analysis suggests a significant difference (P

Background and Objective: Although the risk of recurrent cerebral ischemia is higher after a transient ischemic attack (TIA), there is limited data on the outcome of TIA mimics. The goal of this study is to compare the 6-month outcome of patients with negative and positive diffusion-weighted imaging (DWI) TIAs (DWI-neg TIA vs. DWI-pos TIA) and also TIA mimics.Methods: We prospectively studied consecutive patients with an initial diagnosis of TIA in our tertiary stroke centers in a 2-year period. Every included patient had an initial magnetic resonance (MR) with DWI and one-, three-, and six-month follow-up visits. The primary outcome was defined as the composition of intracerebral hemorrhage, ischemic stroke, TIA, coronary artery disease, and death.Results: Out of 269 patients with the initial diagnosis of TIA, 259 patients (mean age 70.5 ± 15.0 [30–100] years old, 56.8% men) were included in the final analysis. Twenty-one (8.1%, 95% confidence interval [CI] 5.1-12.1%) patients had a composite outcome event within the six-month follow-up. Five (23.8%) and 13 (61.9%) composite outcome events occurred in the first 30 and 90 days, respectively. Among patients with DWI-neg TIA, the one- and six-month ischemic stroke rate was 1.5 and 4.6%, respectively. The incidence proportion of composite outcome event was significantly higher among patients who had the diagnosis of DWI-neg TIA compared with those who had the diagnosis of TIA mimics (12.2 vs. 2.1%—relative risk 5.9; 95% CI, 1.4–25.2). In our univariable analysis among patients with DWI-neg TIA and DWI-pos TIA, age (P = 0.017) was the only factor that was significantly associated with the occurrence of the composite outcome.Conclusion: Our study indicated that the overall six-month rate of the composite outcome among patients DWI-neg TIA, DWI-pos TIA, and TIA mimics were 12.2, 9.7, and 2.1%, respectively. Age was the only factor that was significantly associated with the occurrence of the composite outcome.