Genetically proxied HTRA1 protease activity and circulating levels independently predict risk of ischemic stroke and coronary artery disease.

Genetic variants in HTRA1 are associated with stroke risk. However, the mechanisms mediating this remain largely unknown, as does the full spectrum of phenotypes associated with genetic variation in HTRA1. Here we show that rare HTRA1 variants are linked to ischemic stroke in the UK Biobank and BioBank Japan.

Self-Management among Stroke Survivors in the United States, 2016 to 2021.

: Self-management among stroke survivors is effective in mitigating the risk of a recurrent stroke. This study aims to determine the prevalence of self-management and its associated factors among stroke survivors in the United States. : We analyzed the Behavioral Risk Factor Surveillance System (BRFSS) data from 2016 to 2021, a nationally representative health survey.

Care settings of transient ischemic attack in the United States: A cohort study from the TriNetX health research network.

Background: Evaluation and hospitalization rates after a transient ischemic attack (TIA)-like presentation vary widely in clinical practice. This study aimed to examine variations in care settings at initial TIA diagnosis in the United States.

Methods: We retrospectively analyzed an adult cohort with a first TIA principal diagnosis between January 1, 2015, and December 31, 2019, from TriNetX Diamond Network.

Machine learning-based Cerebral Venous Thrombosis diagnosis with clinical data.

Objectives: Cerebral Venous Thrombosis (CVT) poses diagnostic challenges due to the variability in disease course and symptoms. The prognosis of CVT relies on early diagnosis. Our study focuses on developing a machine learning-based screening algorithm using clinical data from a large neurology referral center in southern Iran.

Automated detection of early signs of irreversible ischemic change on CTA source images in patients with large vessel occlusion.

Purpose: To create and validate an automated pipeline for detection of early signs of irreversible ischemic change from admission CTA in patients with large vessel occlusion (LVO) stroke.

Methods: We retrospectively included 368 patients for training and 143 for external validation. All patients had anterior circulation LVO stroke, endovascular therapy with successful reperfusion, and follow-up diffusion-weighted imaging (DWI).

The Impact of Conventional Stroke Risk Factors on Early and Late Onset Ischemic Stroke: a Mendelian Randomization Study.

Objective: Although stroke incidence is decreasing in older ages, it is increasing in young adults. While these divergent trends in stroke incidence are at least partially attributable to diverging prevalence trends in stoke risk factors, age-dependent differences in the impact of stroke risk factors on stroke may also contribute. To address this issue, we utilized Mendelian Randomization (MR) to assess differences in the association of stroke risk factors between early onset ischemic stroke (EOS) and late onset ischemic stroke (LOS).

Many Models, Little Adoption-What Accounts for Low Uptake of Machine Learning Models for Atrial Fibrillation Prediction and Detection?

(1) Atrial fibrillation (AF) is a major risk factor for stroke and is often underdiagnosed, despite being present in 13-26% of ischemic stroke patients. Recently, a significant number of machine learning (ML)-based models have been proposed for AF prediction and detection for primary and secondary stroke prevention. However, clinical translation of these technological innovations to close the AF care gap has been scant.

Machine Learning-Based Prediction of Stroke in Emergency Departments.

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.

Development and internal validation of machine learning-based models and external validation of existing risk scores for outcome prediction in patients with ischaemic stroke.

Aims: We developed new machine learning (ML) models and externally validated existing statistical models [ischaemic stroke predictive risk score (iScore) and totalled health risks in vascular events (THRIVE) scores] for predicting the composite of recurrent stroke or all-cause mortality at 90 days and at 3 years after hospitalization for first acute ischaemic stroke (AIS).

Methods And Results: In adults hospitalized with AIS from January 2005 to November 2016, with follow-up until November 2019, we developed three ML models [random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBOOST)] and externally validated the iScore and THRIVE scores for predicting the composite outcomes after AIS hospitalization, using data from 721 patients and 90 potential predictor variables. At 90 days and 3 years, 11 and 34% of patients, respectively, reached the composite outcome.

Burden of Ischemic and Hemorrhagic Stroke Across the US From 1990 to 2019.

Importance: Stroke is a leading cause of death and disability in the US. Accurate and updated measures of stroke burden are needed to guide public health policies.

Objective: To present burden estimates of ischemic and hemorrhagic stroke in the US in 2019 and describe trends from 1990 to 2019 by age, sex, and geographic location.

Prevalence and associated factors of cerebral microbleeds in a rural population of the United States.

Objective: Cerebral microbleeds (CMBs) can carry an advanced risk for the development and burden of cerebrovascular and cognitive disorders. Large-scale population-based studies are required to identify the at-risk population.

Method: Ten percent (N = 3,056) of the Geisinger DiscovEHR Initiative Cohort participants who had brain magnetic resonance imaging (MRI) for any indication were randomly selected.

Cysteine-Altering NOTCH3 Variants Are Associated with an Increased Risk of Autoimmune Diseases.

Autoimmune conditions have been reported among patients with cysteine-altering variants and CADASIL. This study aimed to investigate the occurrence of autoimmune illnesses and markers of inflammation in such populations. Cases were identified who had a cysteine-altering variant from the Geisinger MyCode Community Health Initiative (MyCode).

An integrated pipeline for prediction of Clostridioides difficile infection.

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).

Transient Ischemic Attack Outpatient Clinic: Past Journey and Future Adventure.

A transient ischemic attack (TIA), a constellation of temporary neurological symptoms, precedes stroke in one-fifth of patients. Thus far, many clinical models have been introduced to optimize the quality, time to treatment, and cost of acute TIA care, either in an inpatient or outpatient setting. In this article, we aim to review the characteristics and outcomes of outpatient TIA clinics across the globe.

Defining the Age of Young Ischemic Stroke Using Data-Driven Approaches.

: The cut-point for defining the age of young ischemic stroke (IS) is clinically and epidemiologically important, yet it is arbitrary and differs across studies. In this study, we leveraged electronic health records (EHRs) and data science techniques to estimate an optimal cut-point for defining the age of young IS. Patient-level EHRs were extracted from 13 hospitals in Pennsylvania, and used in two parallel approaches.

SARS-CoV-2 infection might be a predictor of mortality in intracerebral hemorrhage.

Background: 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.

The relevance of rich club regions for functional outcome post-stroke is enhanced in women.

This study aimed to investigate the influence of stroke lesions in predefined highly interconnected (rich-club) brain regions on functional outcome post-stroke, determine their spatial specificity and explore the effects of biological sex on their relevance. We analyzed MRI data recorded at index stroke and ~3-months modified Rankin Scale (mRS) data from patients with acute ischemic stroke enrolled in the multisite MRI-GENIE study. Spatially normalized structural stroke lesions were parcellated into 108 atlas-defined bilateral (sub)cortical brain regions.

Dissecting Polygenic Etiology of Ischemic Stroke in the Era of Precision Medicine.

Ischemic stroke (IS), the leading cause of death and disability worldwide, is caused by many modifiable and non-modifiable risk factors. This complex disease is also known for its multiple etiologies with moderate heritability. Polygenic risk scores (PRSs), which have been used to establish a common genetic basis for IS, may contribute to IS risk stratification for disease/outcome prediction and personalized management.