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. 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.

Global, regional, and national burden of disorders affecting the nervous system, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND

Disorders affecting the nervous system are diverse and include neurodevelopmental disorders, late-life neurodegeneration, and newly emergent conditions, such as cognitive impairment following COVID-19. Previous publications from the Global Burden of Disease, Injuries, and Risk Factor Study estimated the burden of 15 neurological conditions in 2015 and 2016, but these analyses did not include neurodevelopmental disorders, as defined by the International Classification of Diseases (ICD)-11, or a subset of cases of congenital, neonatal, and infectious conditions that cause neurological damage. Here, we estimate nervous system health loss caused by 37 unique conditions and their associated risk factors globally, regionally, and nationally from 1990 to 2021.

METHODS

We estimated mortality, prevalence, years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs), with corresponding 95% uncertainty intervals (UIs), by age and sex in 204 countries and territories, from 1990 to 2021. We included morbidity and deaths due to neurological conditions, for which health loss is directly due to damage to the CNS or peripheral nervous system. We also isolated neurological health loss from conditions for which nervous system morbidity is a consequence, but not the primary feature, including a subset of congenital conditions (ie, chromosomal anomalies and congenital birth defects), neonatal conditions (ie, jaundice, preterm birth, and sepsis), infectious diseases (ie, COVID-19, cystic echinococcosis, malaria, syphilis, and Zika virus disease), and diabetic neuropathy. By conducting a sequela-level analysis of the health outcomes for these conditions, only cases where nervous system damage occurred were included, and YLDs were recalculated to isolate the non-fatal burden directly attributable to nervous system health loss. A comorbidity correction was used to calculate total prevalence of all conditions that affect the nervous system combined.

FINDINGS

Globally, the 37 conditions affecting the nervous system were collectively ranked as the leading group cause of DALYs in 2021 (443 million, 95% UI 378-521), affecting 3·40 billion (3·20-3·62) individuals (43·1%, 40·5-45·9 of the global population); global DALY counts attributed to these conditions increased by 18·2% (8·7-26·7) between 1990 and 2021. Age-standardised rates of deaths per 100 000 people attributed to these conditions decreased from 1990 to 2021 by 33·6% (27·6-38·8), and age-standardised rates of DALYs attributed to these conditions decreased by 27·0% (21·5-32·4). Age-standardised prevalence was almost stable, with a change of 1·5% (0·7-2·4). The ten conditions with the highest age-standardised DALYs in 2021 were stroke, neonatal encephalopathy, migraine, Alzheimer's disease and other dementias, diabetic neuropathy, meningitis, epilepsy, neurological complications due to preterm birth, autism spectrum disorder, and nervous system cancer.

INTERPRETATION

As the leading cause of overall disease burden in the world, with increasing global DALY counts, effective prevention, treatment, and rehabilitation strategies for disorders affecting the nervous system are needed.

FUNDING

Bill & Melinda Gates Foundation.

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. For the 90-day prediction, the area under the receiver operating characteristic curve (AUC) was 0.779 for RF, 0.771 for SVM, 0.772 for XGBOOST, 0.720 for iScore, and 0.664 for THRIVE. For 3-year prediction, the AUC was 0.743 for RF, 0.777 for SVM, 0.773 for XGBOOST, 0.710 for iScore, and 0.675 for THRIVE.

Conclusion

The study provided three ML-based predictive models that achieved good discrimination and clinical usefulness in outcome prediction after AIS and broadened the application of the iScore and THRIVE scoring system for long-term outcome prediction. Our findings warrant comparative analyses of ML and existing statistical method-based risk prediction tools for outcome prediction after AIS in new data sets.

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. Patients with CMBs were compared to an age-, gender-, body mass index-, and hypertension-matched cohort of patients without CMB. The prevalence of comorbidities and use of anticoagulation therapy was investigated in association with CMB presence (binary logistic regression), quantity (ordinal regression), and topography (multinomial regression).

RESULTS

Among 3,056 selected participants, 477 (15.6 %) had CMBs in their MRI. Patients with CMBs were older and were more prevalently hypertensive, with ischemic stroke, arrhythmia, dyslipidemia, coronary artery disease, and the use of warfarin. After propensity-score matching, 477 patients with CMBs and 974 without were included for further analyses. Predictors of ≥5 CMBs were ischemic stroke (OR, 1.6; 95 % CI, 1.2 -2.0), peripheral vascular disease (OR, 1.6; 95 % CI, 1.1-2.3), and thrombocytopenia (OR, 1.9; 95 % CI, 1.2-2.9). Ischemic stroke was associated with strictly lobar CMBs more strongly than deep/infra-tentorial CMBs (OR, 2.1; 95 % CI, 1.5-3.1; vs. OR, 1.4; CI, 1.1-1.8).

CONCLUSIONS

CMBs were prevalent in our white population. Old age, hypertension, anticoagulant treatment, thrombocytopenia, and a history of vascular diseases including stroke, were associated with CMBs.

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

(1) Background: 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. Herein, we sought to systematically examine studies, employing ML models to predict incident AF in a population without prior AF or to detect paroxysmal AF in stroke cohorts to identify key reasons for the lack of translation into the clinical workflow. We conclude with a set of recommendations to improve the clinical translatability of ML-based models for AF. (2) Methods: MEDLINE, Embase, Web of Science, Clinicaltrials.gov, and ICTRP databases were searched for relevant articles from the inception of the databases up to September 2022 to identify peer-reviewed articles in English that used ML methods to predict incident AF or detect AF after stroke and reported adequate performance metrics. The search yielded 2815 articles, of which 16 studies using ML models to predict incident AF and three studies focusing on ML models to detect AF post-stroke were included. (3) Conclusions: This study highlights that (1) many models utilized only a limited subset of variables available from patients' health records; (2) only 37% of models were externally validated, and stratified analysis was often lacking; (3) 0% of models and 53% of datasets were explicitly made available, limiting reproducibility and transparency; and (4) data pre-processing did not include bias mitigation and sufficient details, leading to potential selection bias. Low generalizability, high false alarm rate, and lack of interpretability were identified as additional factors to be addressed before ML models can be widely deployed in the clinical care setting. Given these limitations, our recommendations to improve the uptake of ML models for better AF outcomes include improving generalizability, reducing potential systemic biases, and investing in external validation studies whilst developing a transparent modeling pipeline to ensure reproducibility.

Prevalence and Determinants of Long-term Post-COVID Conditions in the United States: 2022 Behavioral Risk Factor Surveillance System

BACKGROUND

A significant proportion of COVID survivors experience lingering and debilitating symptoms following acute COVID-19 infection. According to the national research plan on long COVID, it is a national priority to identify the prevalence of post-COVID conditions and their associated factors.

METHOD

We performed a cross-sectional analysis of the Prevention Behavioral Risk Factor Surveillance System (BRFSS) 2022, the largest continuously gathered health survey dataset worldwide by the Centers for Disease Control. After identifying individuals with a positive history of COVID-19, we grouped COVID-19 survivors based on whether they experienced long-term post-COVID conditions. Using survey-specific R packages, we compared the two groups' socio-demographics, comorbidities, and lifestyle-related factors. A logistic regression model was used to identify factors associated with post-COVID conditions.

RESULTS

The overall estimated prevalence of long-term post-COVID conditions among COVID survivors was 21.7%. Fatigue(5.7%), dyspnea(4.2%), and anosmia/ageusia(3.8%) were the most frequent symptoms. Based on multivariate logistic regression analysis, female sex, body mass index (BMI)≥25, lack of insurance, history of pulmonary disease, depression, and arthritis, being a former smoker, and sleep duration<7h/day were associated with higher odds of post-COVID conditions. On the other hand, age>64 y/o, Black race, and annual household income≥$10000 were associated with lower odds of post-COVID conditions.

CONCLUSION

Our findings indicate a notable prevalence of post-COVID conditions, particularly among middle-aged women and individuals with comorbidities or adverse lifestyles. This high-risk demographic may require long-term follow-up and support. Further investigations are essential to facilitate the development of specified healthcare and therapeutic strategies for those suffering from post-COVID conditions.

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). 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.

Global, regional, and national incidence of six major immune-mediated inflammatory diseases: findings from the global burden of disease study 2019

Background

The causes for immune-mediated inflammatory diseases (IMIDs) are diverse and the incidence trends of IMIDs from specific causes are rarely studied. The study aims to investigate the pattern and trend of IMIDs from 1990 to 2019.

Methods

We collected detailed information on six major causes of IMIDs, including asthma, inflammatory bowel disease, multiple sclerosis, rheumatoid arthritis, psoriasis, and atopic dermatitis, between 1990 and 2019, derived from the Global Burden of Disease study in 2019. The average annual percent change (AAPC) in number of incidents and age standardized incidence rate (ASR) on IMIDs, by sex, age, region, and causes, were calculated to quantify the temporal trends.

Findings

In 2019, rheumatoid arthritis, atopic dermatitis, asthma, multiple sclerosis, psoriasis, inflammatory bowel disease accounted 1.59%, 36.17%, 54.71%, 0.09%, 6.84%, 0.60% of overall new IMIDs cases, respectively. The ASR of IMIDs showed substantial regional and global variation with the highest in High SDI region, High-income North America, and United States of America. Throughout human lifespan, the age distribution of incident cases from six IMIDs was quite different. Globally, incident cases of IMIDs increased with an AAPC of 0.68 and the ASR decreased with an AAPC of -0.34 from 1990 to 2019. The incident cases increased across six IMIDs, the ASR of rheumatoid arthritis increased (0.21, 95% CI 0.18, 0.25), while the ASR of asthma (AAPC = -0.41), inflammatory bowel disease (AAPC = -0.72), multiple sclerosis (AAPC = -0.26), psoriasis (AAPC = -0.77), and atopic dermatitis (AAPC = -0.15) decreased. The ASR of overall and six individual IMID increased with SDI at regional and global level. Countries with higher ASR in 1990 experienced a more rapid decrease in ASR.

Interpretation

The incidence patterns of IMIDs varied considerably across the world. Innovative prevention and integrative management strategy are urgently needed to mitigate the increasing ASR of rheumatoid arthritis and upsurging new cases of other five IMIDs, respectively.

Funding

The Global Burden of Disease Study is funded by the Bill and Melinda Gates Foundation. The project funded by Scientific Research Fund of Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital (2022QN38).

The Phenotypic Spectrum of COL4A3 Heterozygotes

Introduction

The penetrance and phenotypic spectrum of autosomal dominant Alport Syndrome (ADAS), affecting 1 in 106, remains understudied.

Methods

Using data from 174,418 participants in the Geisinger MyCode/DiscovEHR study, an unselected health system-based cohort with whole exome sequencing, we identified 403 participants who were heterozygous for likely pathogenic COL4A3 variants. Phenotypic data was evaluated using International Classification of Diseases (ICD) codes, laboratory data, and chart review. To evaluate the phenotypic spectrum of genetically-determined ADAS, we matched COL4A3 heterozygotes 1:5 to nonheterozygotes using propensity scores by demographics, hypertension, diabetes, and nephrolithiasis.

Results

COL4A3 heterozygotes were at significantly increased risks of hematuria, decreased estimated glomerular filtration rate (eGFR), albuminuria, and kidney failure (P < 0.05 for all comparisons) but not bilateral sensorineural hearing loss (P = 0.9). Phenotypic severity was more severe for collagenous domain glycine missense variants than protein truncating variants (PTVs). For example, patients with Gly695Arg (n = 161) had markedly increased risk of dipstick hematuria (odds ratio [OR] 9.50; 95% confidence interval [CI]: 6.32, 14.28) and kidney failure (OR 7.02; 95% CI: 3.48, 14.16) whereas those with PTVs (n = 119) had moderately increased risks of dipstick hematuria (OR 1.64; 95% CI: 1.03, 2.59) and kidney failure (OR 3.44; 95% CI: 1.28, 9.22). Less than a third of patients had albuminuria screening completed, and fewer than 1 of 3 were taking inhibitors of the renin-angiotensin-aldosterone system.

Conclusion

This study demonstrates a wide spectrum of phenotypic severity in ADAS due to COL4A3 with phenotypic variability by genotype. Future studies are needed to evaluate the impact of earlier diagnosis, appropriate evaluation, and treatment of ADAS.

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

Autoimmune conditions have been reported among patients with cysteine-altering NOTCH3 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 NOTCH3 cysteine-altering variant from the Geisinger MyCode® Community Health Initiative (MyCode®). We further performed external validation using the UK Biobank cohort. A cohort of 121 individuals with a NOTCH3 cysteine-altering variant from MyCode® was compared to a control group with no non-synonymous variation in NOTCH3 (n = 184). Medical records were evaluated for inflammatory markers and autoimmune conditions, which were grouped by the organ systems involved. A similar analysis was conducted using data from the UK Biobank (n~450,000). An overall increase in inflammatory markers among participants with a NOTCH3 cysteine-altering variant was observed when compared to an age- and sex-matched MyCode® control group (out of participants with laboratory testing: 50.9% versus 26.7%; p = 0.0047; out of total participants: 23.1% versus 10.9%; p = 0.004). Analysis of UK Biobank data indicated any autoimmune diagnosis (1.63 [1.14, 2.09], p= 2.665 × 10-3) and multiple sclerosis (3.42 [1.67, 6.02], p = 9.681 × 10-4) are associated with a NOTCH3 cysteine-altering variant in any domain. Our findings suggest a possible association between NOTCH3 cysteine-altering variants and autoimmune conditions.

Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND

Diabetes is one of the leading causes of death and disability worldwide, and affects people regardless of country, age group, or sex. Using the most recent evidentiary and analytical framework from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD), we produced location-specific, age-specific, and sex-specific estimates of diabetes prevalence and burden from 1990 to 2021, the proportion of type 1 and type 2 diabetes in 2021, the proportion of the type 2 diabetes burden attributable to selected risk factors, and projections of diabetes prevalence through 2050.

METHODS

Estimates of diabetes prevalence and burden were computed in 204 countries and territories, across 25 age groups, for males and females separately and combined; these estimates comprised lost years of healthy life, measured in disability-adjusted life-years (DALYs; defined as the sum of years of life lost [YLLs] and years lived with disability [YLDs]). We used the Cause of Death Ensemble model (CODEm) approach to estimate deaths due to diabetes, incorporating 25 666 location-years of data from vital registration and verbal autopsy reports in separate total (including both type 1 and type 2 diabetes) and type-specific models. Other forms of diabetes, including gestational and monogenic diabetes, were not explicitly modelled. Total and type 1 diabetes prevalence was estimated by use of a Bayesian meta-regression modelling tool, DisMod-MR 2.1, to analyse 1527 location-years of data from the scientific literature, survey microdata, and insurance claims; type 2 diabetes estimates were computed by subtracting type 1 diabetes from total estimates. Mortality and prevalence estimates, along with standard life expectancy and disability weights, were used to calculate YLLs, YLDs, and DALYs. When appropriate, we extrapolated estimates to a hypothetical population with a standardised age structure to allow comparison in populations with different age structures. We used the comparative risk assessment framework to estimate the risk-attributable type 2 diabetes burden for 16 risk factors falling under risk categories including environmental and occupational factors, tobacco use, high alcohol use, high body-mass index (BMI), dietary factors, and low physical activity. Using a regression framework, we forecast type 1 and type 2 diabetes prevalence through 2050 with Socio-demographic Index (SDI) and high BMI as predictors, respectively.

FINDINGS

In 2021, there were 529 million (95% uncertainty interval [UI] 500-564) people living with diabetes worldwide, and the global age-standardised total diabetes prevalence was 6·1% (5·8-6·5). At the super-region level, the highest age-standardised rates were observed in north Africa and the Middle East (9·3% [8·7-9·9]) and, at the regional level, in Oceania (12·3% [11·5-13·0]). Nationally, Qatar had the world's highest age-specific prevalence of diabetes, at 76·1% (73·1-79·5) in individuals aged 75-79 years. Total diabetes prevalence-especially among older adults-primarily reflects type 2 diabetes, which in 2021 accounted for 96·0% (95·1-96·8) of diabetes cases and 95·4% (94·9-95·9) of diabetes DALYs worldwide. In 2021, 52·2% (25·5-71·8) of global type 2 diabetes DALYs were attributable to high BMI. The contribution of high BMI to type 2 diabetes DALYs rose by 24·3% (18·5-30·4) worldwide between 1990 and 2021. By 2050, more than 1·31 billion (1·22-1·39) people are projected to have diabetes, with expected age-standardised total diabetes prevalence rates greater than 10% in two super-regions: 16·8% (16·1-17·6) in north Africa and the Middle East and 11·3% (10·8-11·9) in Latin America and Caribbean. By 2050, 89 (43·6%) of 204 countries and territories will have an age-standardised rate greater than 10%.

INTERPRETATION

Diabetes remains a substantial public health issue. Type 2 diabetes, which makes up the bulk of diabetes cases, is largely preventable and, in some cases, potentially reversible if identified and managed early in the disease course. However, all evidence indicates that diabetes prevalence is increasing worldwide, primarily due to a rise in obesity caused by multiple factors. Preventing and controlling type 2 diabetes remains an ongoing challenge. It is essential to better understand disparities in risk factor profiles and diabetes burden across populations, to inform strategies to successfully control diabetes risk factors within the context of multiple and complex drivers.

FUNDING

Bill & Melinda Gates Foundation.

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. In addition, we discussed the main challenges for outpatient management of TIA, including triage and diagnosis, and the system dynamics of the clinics. We further reviewed the potential developments in TIA care, such as telemedicine, predictive analytics, personalized medicine, and advanced imaging.

The Phenotypic Spectrum of COL4A3 Heterozygotes

Most data on Alport Syndrome (AS) due to COL4A3 are limited to families with autosomal recessive AS or severe manifestations such as focal segmental glomerulosclerosis (FSGS). Using data from 174,418 participants in the Geisinger MyCode/DiscovEHR study, an unselected health system-based cohort with whole exome sequencing, we identified 403 participants (0.2%) who were heterozygous for likely pathogenic COL4A3 variants. Phenotypic data was evaluated using International Classification of Diseases (ICD) codes, laboratory data, and chart review. To evaluate the phenotypic spectrum of genetically-determined autosomal dominant AS, we matched COL4A3 heterozygotes 1:5 to non-heterozygotes using propensity scores by demographics, hypertension, diabetes, and nephrolithiasis. COL4A3 heterozygotes were at significantly increased risks of hematuria, decreased estimated glomerular filtration rate (eGFR), albuminuria, and end-stage kidney disease (ESKD) (p<0.05 for all comparisons) but not bilateral sensorineural hearing loss (p=0.9). Phenotypic severity tended to be more severe among patients with glycine missense variants located within the collagenous domain. For example, patients with Gly695Arg (n=161) had markedly increased risk of dipstick hematuria (OR 9.47, 95% CI: 6.30, 14.22) and ESKD diagnosis (OR 7.01, 95% CI: 3.48, 14.12) whereas those with PTVs (n=119) had moderately increased risks of dipstick hematuria (OR 1.63, 95% CI: 1.03, 2.58) and ESKD diagnosis (OR 3.43, 95% CI: 1.28, 9.19). Less than a third of patients had albuminuria screening completed, and fewer than 1/3 were taking inhibitors of the renin-angiotensin-aldosterone system (RAASi). Future studies are needed to evaluate the impact of earlier diagnosis, appropriate evaluation, and treatment of ADAS.

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

Introduction: 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. Methods: Patient-level EHRs were extracted from 13 hospitals in Pennsylvania, and used in two parallel approaches. The first approach included ICD9/10, from IS patients to group comorbidities, and computed similarity scores between every patient pair. We determined the optimal age of young IS by analyzing the trend of patient similarity with respect to their clinical profile for different ages of index IS. The second approach used the IS cohort and control (without IS), and built three sets of machine-learning models—generalized linear regression (GLM), random forest (RF), and XGBoost (XGB)—to classify patients for seventeen age groups. After extracting feature importance from the models, we determined the optimal age of young IS by analyzing the pattern of comorbidity with respect to the age of index IS. Both approaches were completed separately for male and female patients. Results: The stroke cohort contained 7555 ISs, and the control included 31,067 patients. In the first approach, the optimal age of young stroke was 53.7 and 51.0 years in female and male patients, respectively. In the second approach, we created 102 models, based on three algorithms, 17 age brackets, and two sexes. The optimal age was 53 (GLM), 52 (RF), and 54 (XGB) for female, and 52 (GLM and RF) and 53 (RF) for male patients. Different age and sex groups exhibited different comorbidity patterns. Discussion: Using a data-driven approach, we determined the age of young stroke to be 54 years for women and 52 years for men in our mainly rural population, in central Pennsylvania. Future validation studies should include more diverse populations.

Stroke genetics informs drug discovery and risk prediction across ancestries

Previous genome-wide association studies (GWASs) of stroke - the second leading cause of death worldwide - were conducted predominantly in populations of European ancestry1,2. Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated (P < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis3, and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN) and variants (such as at GRK5 and NOS3). Using a three-pronged approach4, we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry5. Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries.

Age-sex differences in the global burden of lower respiratory infections and risk factors, 1990-2019: results from the Global Burden of Disease Study 2019

BACKGROUND

The global burden of lower respiratory infections (LRIs) and corresponding risk factors in children older than 5 years and adults has not been studied as comprehensively as it has been in children younger than 5 years. We assessed the burden and trends of LRIs and risk factors across all age groups by sex, for 204 countries and territories.

METHODS

In this analysis of data for the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019, we used clinician-diagnosed pneumonia or bronchiolitis as our case definition for LRIs. We included International Classification of Diseases 9th edition codes 079.6, 466-469, 470.0, 480-482.8, 483.0-483.9, 484.1-484.2, 484.6-484.7, and 487-489 and International Classification of Diseases 10th edition codes A48.1, A70, B97.4-B97.6, J09-J15.8, J16-J16.9, J20-J21.9, J91.0, P23.0-P23.4, and U04-U04.9. We used the Cause of Death Ensemble modelling strategy to analyse 23 109 site-years of vital registration data, 825 site-years of sample vital registration data, 1766 site-years of verbal autopsy data, and 681 site-years of mortality surveillance data. We used DisMod-MR 2.1, a Bayesian meta-regression tool, to analyse age-sex-specific incidence and prevalence data identified via systematic reviews of the literature, population-based survey data, and claims and inpatient data. Additionally, we estimated age-sex-specific LRI mortality that is attributable to the independent effects of 14 risk factors.

FINDINGS

Globally, in 2019, we estimated that there were 257 million (95% uncertainty interval [UI] 240-275) LRI incident episodes in males and 232 million (217-248) in females. In the same year, LRIs accounted for 1·30 million (95% UI 1·18-1·42) male deaths and 1·20 million (1·07-1·33) female deaths. Age-standardised incidence and mortality rates were 1·17 times (95% UI 1·16-1·18) and 1·31 times (95% UI 1·23-1·41) greater in males than in females in 2019. Between 1990 and 2019, LRI incidence and mortality rates declined at different rates across age groups and an increase in LRI episodes and deaths was estimated among all adult age groups, with males aged 70 years and older having the highest increase in LRI episodes (126·0% [95% UI 121·4-131·1]) and deaths (100·0% [83·4-115·9]). During the same period, LRI episodes and deaths in children younger than 15 years were estimated to have decreased, and the greatest decline was observed for LRI deaths in males younger than 5 years (-70·7% [-77·2 to -61·8]). The leading risk factors for LRI mortality varied across age groups and sex. More than half of global LRI deaths in children younger than 5 years were attributable to child wasting (population attributable fraction [PAF] 53·0% [95% UI 37·7-61·8] in males and 56·4% [40·7-65·1] in females), and more than a quarter of LRI deaths among those aged 5-14 years were attributable to household air pollution (PAF 26·0% [95% UI 16·6-35·5] for males and PAF 25·8% [16·3-35·4] for females). PAFs of male LRI deaths attributed to smoking were 20·4% (95% UI 15·4-25·2) in those aged 15-49 years, 30·5% (24·1-36·9) in those aged 50-69 years, and 21·9% (16·8-27·3) in those aged 70 years and older. PAFs of female LRI deaths attributed to household air pollution were 21·1% (95% UI 14·5-27·9) in those aged 15-49 years and 18·2% (12·5-24·5) in those aged 50-69 years. For females aged 70 years and older, the leading risk factor, ambient particulate matter, was responsible for 11·7% (95% UI 8·2-15·8) of LRI deaths.

INTERPRETATION

The patterns and progress in reducing the burden of LRIs and key risk factors for mortality varied across age groups and sexes. The progress seen in children younger than 5 years was clearly a result of targeted interventions, such as vaccination and reduction of exposure to risk factors. Similar interventions for other age groups could contribute to the achievement of multiple Sustainable Development Goals targets, including promoting wellbeing at all ages and reducing health inequalities. Interventions, including addressing risk factors such as child wasting, smoking, ambient particulate matter pollution, and household air pollution, would prevent deaths and reduce health disparities.

FUNDING

Bill & Melinda Gates Foundation.

Contribution of Common Genetic Variants to Risk of Early-Onset Ischemic Stroke

BACKGROUND AND OBJECTIVES

Current genome-wide association studies of ischemic stroke have focused primarily on late-onset disease. As a complement to these studies, we sought to identify the contribution of common genetic variants to risk of early-onset ischemic stroke.

METHODS

We performed a meta-analysis of genome-wide association studies of early-onset stroke (EOS), ages 18-59 years, using individual-level data or summary statistics in 16,730 cases and 599,237 nonstroke controls obtained across 48 different studies. We further compared effect sizes at associated loci between EOS and late-onset stroke (LOS) and compared polygenic risk scores (PRS) for venous thromboembolism (VTE) between EOS and LOS.

RESULTS

We observed genome-wide significant associations of EOS with 2 variants in ABO, a known stroke locus. These variants tag blood subgroups O1 and A1, and the effect sizes of both variants were significantly larger in EOS compared with LOS. The odds ratio (OR) for rs529565, tagging O1, was 0.88 (95% confidence interval [CI]: 0.85-0.91) in EOS vs 0.96 (95% CI: 0.92-1.00) in LOS, and the OR for rs635634, tagging A1, was 1.16 (1.11-1.21) for EOS vs 1.05 (0.99-1.11) in LOS; p-values for interaction = 0.001 and 0.005, respectively. Using PRSs, we observed that greater genetic risk for VTE, another prothrombotic condition, was more strongly associated with EOS compared with LOS (p = 0.008).

DISCUSSION

The ABO locus, genetically predicted blood group A, and higher genetic propensity for venous thrombosis are more strongly associated with EOS than with LOS, supporting a stronger role of prothrombotic factors in EOS.

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. Statistical modeling and machine learning algorithms have contributed significantly to this field. For instance, multiple algorithms have been successfully applied to PRS construction and integration of genetic and non-genetic features for outcome prediction to aid in risk stratification for personalized management and prevention measures. PRS derived from variants with effect size estimated based on the summary statistics of a specific subtype shows a stronger association with the matched subtype. The disruption of the extracellular matrix and amyloidosis account for the pathogenesis of cerebral small vessel disease (CSVD). Pathway-specific PRS analyses confirm known and identify novel etiologies related to IS. Some of these specific PRSs (e.g., derived from endothelial cell apoptosis pathway) individually contribute to post-IS mortality and, together with clinical risk factors, better predict post-IS mortality. In this review, we summarize the genetic basis of IS, emphasizing the application of methodologies and algorithms used to construct PRSs and integrate genetics into risk models.

Dataset on acute stroke risk stratification from CT angiographic radiomics

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.

A Simple Review of Small Vessel Disease Manifestation in the Brain, Retina, and Kidneys

Small blood vessels express specific phenotypical and functional characteristics throughout the body. Alterations in the microcirculation contribute to many correlated physiological and pathological events in related organs. Factors such as comorbidities and genetics contribute to the complexity of this topic. Small vessel disease primarily affects end organs that receive significant cardiac output, such as the brain, kidney, and retina. Despite the differences in location, concurrent changes are seen in the micro-vasculature of the brain, retina, and kidneys under pathological conditions due to their common histological, functional, and embryological characteristics. While the cardiovascular basis of pathology in association with the brain, retina, or kidneys has been well documented, this is a simple review that uniquely considers the relationship between all three organs and highlights the prevalence of coexisting end organ injuries in an attempt to elucidate connections between the brain, retina, and kidneys, which has the potential to transform diagnostic and therapeutic approaches.

Long-term mortality in ischemic stroke patients with concomitant chronic obstructive pulmonary disease

BACKGROUND

Long-term mortality in ischemic stroke patients with concomitant COPD has been largely unexplored. This study aimed to compare long-term all-cause mortality in ischemic stroke patients with and without COPD.

METHODS

This was a retrospective cohort study of ischemic stroke patients with and without COPD in the Geisinger Neuroscience Ischemic Stroke database to examine all-cause mortality up to 3 years using Kaplan-Meier estimator and Cox proportional hazards model.

RESULTS

Of the 6,589 ischemic stroke patients included in this study, 5,525 (83.9%) did not have COPD (group A). Group B (n=1,006) consisted of patients with COPD diagnosis by ICD-9/10-CM codes. COPD patients in Group C (n=233) were diagnosed by spirometry, and in Group D (n=175) by both ICD-9/10-CM codes and spirometry confirmation. The survival probabilities at three years in Group B, C, and D were significantly lower than in Group A. Group B (HR=1.262, 95% CI 1.122-1.42, p<0.001) and group C (HR=1.251, 95% CI 1.01-1.55, p=0.041) had significantly lower hazard of mortality compared to group A. There was no significant difference in survival between COPD subtypes of chronic bronchitis and emphysema. Patients in Global Initiative for Chronic Obstructive Lung Disease (GOLD) 2 stage had an increased mortality hazard compared to the GOLD 1 stage.

CONCLUSIONS

While ischemic stroke patients with preexisting COPD have worse long-term survival than those without COPD, the results largely depended on the definition of COPD used. These results suggest that ischemic stroke patients with COPD need more personalized medical care to decrease long-term mortality.

Prediction of Influenza Complications: Development and Validation of a Machine Learning Prediction Model to Improve and Expand the Identification of Vaccine-Hesitant Patients at Risk of Severe Influenza Complications

Influenza vaccinations are recommended for high-risk individuals, but few population-based strategies exist to identify individual risks. Patient-level data from unvaccinated individuals, stratified into retrospective cases (n = 111,022) and controls (n = 2,207,714), informed a machine learning model designed to create an influenza risk score; the model was called the Geisinger Flu-Complications Flag (GFlu-CxFlag). The flag was created and validated on a cohort of 604,389 unique individuals. Risk scores were generated for influenza cases; the complication rate for individuals without influenza was estimated to adjust for unrelated complications. Shapley values were used to examine the model’s correctness and demonstrate its dependence on different features. Bias was assessed for race and sex. Inverse propensity weighting was used in the derivation stage to correct for biases. The GFlu-CxFlag model was compared to the pre-existing Medial EarlySign Flu Algomarker and existing risk guidelines that describe high-risk patients who would benefit from influenza vaccination. The GFlu-CxFlag outperformed other traditional risk-based models; the area under curve (AUC) was 0.786 [0.783−0.789], compared with 0.694 [0.690−0.698] (p-value < 0.00001). The presence of acute and chronic respiratory diseases, age, and previous emergency department visits contributed most to the GFlu-CxFlag model’s prediction. When higher numerical scores were assigned to more severe complications, the GFlu-CxFlag AUC increased to 0.828 [0.823−0.833], with excellent discrimination in the final model used to perform the risk stratification of the population. The GFlu-CxFlag can better identify high-risk individuals than existing models based on vaccination guidelines, thus creating a population-based risk stratification for individual risk assessment and deployment in vaccine hesitancy reduction programs in our health system.

Predicting mortality among ischemic stroke patients using pathways-derived polygenic risk scores

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.

Social Determinants of Stroke Hospitalization and Mortality in United States' Counties

(1) Background: Stroke incidence and outcomes are influenced by socioeconomic status. There is a paucity of reported population-level studies regarding these determinants. The goal of this ecological analysis was to determine the county-level associations of social determinants of stroke hospitalization and death rates in the United States. (2) Methods: Publicly available data as of 9 April 2021, for the socioeconomic factors and outcomes, was extracted from the Centers for Disease Control and Prevention. The outcomes of interest were “all stroke hospitalization rates per 1000 Medicare beneficiaries” (SHR) and “all stroke death rates per 100,000 population” (SDR). We used a multivariate binomial generalized linear mixed model after converting the outcomes to binary based on their median values. (3) Results: A total of 3226 counties/county-equivalents of the states and territories in the US were analyzed. Heart disease prevalence (odds ratio, OR = 2.03, p < 0.001), blood pressure medication nonadherence (OR = 2.02, p < 0.001), age-adjusted obesity (OR = 1.24, p = 0.006), presence of hospitals with neurological services (OR = 1.9, p < 0.001), and female head of household (OR = 1.32, p = 0.021) were associated with high SHR while cost of care per capita for Medicare patients with heart disease (OR = 0.5, p < 0.01) and presence of hospitals (OR = 0.69, p < 0.025) were associated with low SHR. Median household income (OR = 0.6, p < 0.001) and park access (OR = 0.84, p = 0.016) were associated with low SDR while no college degree (OR = 1.21, p = 0.049) was associated with high SDR. (4) Conclusions: Several socioeconomic factors (e.g., education, income, female head of household) were found to be associated with stroke outcomes. Additional research is needed to investigate intermediate and potentially modifiable factors that can serve as targeted interventions.

CT angiographic radiomics signature for risk stratification in anterior large vessel occlusion stroke

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 < 0.001) and Radiomics (AUC = 0.68, p = 0.005) models for 3-month prediction.

CONCLUSION

Machine-learning signatures of admission CTA radiomics can provide prognostic information in acute LVO stroke candidates for mechanical thrombectomy. Such objective and time-sensitive risk stratification can guide treatment decisions and facilitate tele-stroke assessment of patients. Particularly in the absence of reliable clinical information at the time of admission, models solely using radiomics features can provide a useful prognostication tool.

Comparison of Long-Term Outcomes and Associated Factors between Younger and Older Rural Ischemic Stroke Patients

Introduction: The rise of ischemic stroke among young adults has stressed the need to understand their risk profiles and outcomes better. This study aimed to examine the five-year ischemic stroke recurrence and survival probability among young patients in rural Pennsylvania. Methods: This retrospective cohort study included first-time ischemic stroke patients from the Geisinger Health System between September 2003 and May 2014. The outcomes included all-cause mortality and ischemic stroke recurrence at five years. Kaplan-Meier estimator, cumulative incidence function, Cox proportional hazards model, and Cause-specific hazard model were used to examine the association of independent variables with the outcomes. Results: A total of 4459 first-time ischemic stroke patients were included in the study, with 664 (14.9%) patients in the 18−55 age group and 3795 (85.1%) patients in the >55 age group. In the 18−55 age group, the five-year survival probability was 87.2%, and the cumulative incidence of recurrence was 8%. Patients in the 18−55 age group had significantly lower hazard for all-cause mortality (HR = 0.37, 95% CI 0.29−0.46, p < 0.001), and non-significant hazard for five-year recurrence (HR = 0.81, 95% CI 0.58−1.12, p = 0.193) compared to the >55 age group. Chronic kidney disease was found to be associated with increased mortality in the 18−55 age group. Conclusion: In our rural population, younger ischemic stroke patients were at the same risk of long-term ischemic stroke recurrence as the older ischemic stroke patients. Identifying the factors and optimizing adequate long-term secondary prevention may reduce the risk of poor outcomes among younger ischemic stroke patients.

Abstract 27: Predicting Ischemic Stroke In Emergency Departments: Development And Validation Of Machine Learning Models

Background: Stroke misdiagnosis is estimated to occur in 9% of all stroke patients and is associated with poor outcomes. We hypothesized that machine learning (ML) could be used to detect ischemic stroke at the point of care in emergency departments (ED).

Methods: Clinical data from 13 hospitals of a large health system in Pennsylvania, United States, from September 2003 to May 2019 were used for model development. Data from June 2019 to December 2020 were prospectively collected and divided into pre- and post-COVID cohorts for validation. We simulated three clinical settings by enrollment with different inclusion/exclusion criteria and created two ML pipelines: ML applied to pre-event clinical data, and natural language processing (NLP) with ML applied to clinical notes during triaging. Misdiagnosed stroke in ED was a case study to show the discriminative power.

Results: We included a total of 49,155 patient encounters (8,900 consecutive ischemic stroke patients and 40,255 controls). The best model, based on the patient’s pre-event information, was XGBoost (AUROC=0.91). Using NLP+ML pipeline, we collected 2,070 notes during the triage process and 9,607 notes (non-stroke) for modeling. For independent validation, we identified 2,989 notes for stroke and 4,303 notes for non-stroke. We identified nine models that reached a balanced performance in terms of AUROC, sensitivity, and specificity. Model performance, in terms of AUROC, ranged from 0.876 to 0.985. Model sensitivity and specificity reached 0.993 and 0.975, respectively. There was no performance difference between models tested on pre- and post-COVID. Three out of four models correctly identified a separate case series of five misdiagnosed strokes.

Conclusion: Available clinical information can be used to reduce stroke misdiagnosis in real-time. In addition, this study serves as a foundation for prospective trials to establish how pre-event clinical data and triage information can be used to improve care.

Intravenous thrombolysis in ischemic stroke patients with a prior intracranial hemorrhage: a meta-analysis

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 (I² >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.

Risk of Subsequent Stroke Among Patients Receiving Outpatient vs Inpatient Care for Transient Ischemic Attack: A Systematic Review and Meta-analysis

IMPORTANCE

Transient ischemic attack (TIA) often indicates a high risk of subsequent cerebral ischemic events. Timely preventive measures improve the outcome.

OBJECTIVE

To estimate and compare the risk of subsequent ischemic stroke among patients with TIA or minor ischemic stroke (mIS) by care setting.

DATA SOURCES

MEDLINE, Web of Science, Scopus, Embase, International Clinical Trials Registry Platform, ClinicalTrials.gov, Trip Medical Database, CINAHL, and all Evidence-Based Medicine review series were searched from the inception of each database until October 1, 2020.

STUDY SELECTION

Studies evaluating the occurrence of ischemic stroke after TIA or mIS were included. Cohorts without data on evaluation time for reporting subsequent stroke, with retrospective diagnosis of the index event after stroke occurrence, and with a report of outcomes that were not limited to patients with TIA or mIS were excluded. Two authors independently screened the titles and abstracts and provided the list of candidate studies for full-text review; discrepancies and disagreements in all steps of the review were addressed by input from a third reviewer.

DATA EXTRACTION AND SYNTHESIS

The study was prepared and reported following the Preferred Reporting Items for Systematic Reviews and Meta-analyses, Meta-analysis of Observational Studies in Epidemiology, Methodological Expectations of Cochrane Intervention Reviews, and Enhancing the Quality and Transparency of Health Research guidelines. The Risk of Bias in Nonrandomized Studies-of Exposures (ROBINS-E) tool was used for critical appraisal of cohorts, and funnel plots, Begg-Mazumdar rank correlation, Kendall τ2, and the Egger bias test were used for evaluating the publication bias. All meta-analyses were conducted under random-effects models.

MAIN OUTCOMES AND MEASURES

Risk of subsequent ischemic stroke among patients with TIA or mIS who received care at rapid-access TIA or neurology clinics, inpatient units, emergency departments (EDs), and unspecified or multiple settings within 4 evaluation intervals (ie, 2, 7, 30, and 90 days).

RESULTS

The analysis included 226 683 patients from 71 articles recruited between 1981 and 2018; 5636 patients received care at TIA clinics (mean [SD] age, 65.7 [3.9] years; 2291 of 4513 [50.8%] men), 130 139 as inpatients (mean [SD] age, 78.3 [4.0] years; 49 458 of 128 745 [38.4%] men), 3605 at EDs (mean [SD] age, 68.9 [3.9] years; 1596 of 3046 [52.4%] men), and 87 303 patients received care in an unspecified setting (mean [SD] age, 70.8 [3.8] years, 43 495 of 87 303 [49.8%] men). Among the patients who were treated at a TIA clinic, the risk of subsequent stroke following a TIA or mIS was 0.3% (95% CI, 0.0%-1.2%) within 2 days, 1.0% (95% CI, 0.3%-2.0%) within 7 days, 1.3% (95% CI, 0.4%-2.6%) within 30 days, and 2.1% (95% CI, 1.4%-2.8%) within 90 days. Among the patients who were treated as inpatients, the risk of subsequent stroke was to 0.5% (95% CI, 0.1%-1.1%) within 2 days, 1.2% (95% CI, 0.4%-2.2%) within 7 days, 1.6% (95% CI, 0.6%-3.1%) within 30 days, and 2.8% (95% CI, 2.1%-3.5%) within 90 days. The risk of stroke among patients treated at TIA clinics was not significantly different from those hospitalized. Compared with the inpatient cohort, TIA clinic patients were younger and had had lower ABCD2 (age, blood pressure, clinical features, duration of TIA, diabetes) scores (inpatients with ABCD2 score >3, 1101 of 1806 [61.0%]; TIA clinic patients with ABCD2 score >3, 1933 of 3703 [52.2%]).

CONCLUSIONS AND RELEVANCE

In this systematic review and meta-analysis, the risk of subsequent stroke among patients who were evaluated in a TIA clinic was not higher than those hospitalized. Patients who received treatment in EDs without further follow-up had a higher risk of subsequent stroke. These findings suggest that TIA clinics can be an effective component of the TIA care component pathway.

Sex-specific association of RAGE and HMGB1 genotype variations with susceptibility to ischemic stroke in Caucasians

BACKGROUND

The role of genotype variants of HMGB1 and RAGE in susceptibility to acute ischemic stroke remains inconclusive.

METHODS

Caucasian acute ischemic stroke patients admitted to three hospitals within a large healthcare system in the U.S. between 2009 and 2017 were reviewed. For each stroke case, three age and sex-matched non-stroke patients were identified as controls. Associations of phased-genotype data for RAGE (rs1035798, rs2070600, rs1800624, rs1800625) and HMGB1 (rs1360485, rs1045411, rs3742305, rs2249825, rs1412125) single-nucleotide-polymorphisms (SNPs) and haplotypes with stroke susceptibility were analyzed. The Benjamini-Hochberg procedure was performed.

RESULTS

Collectively, 4,264 patients, 1,066 acute ischemic stroke and 3,198 controls were identified. Genotype distributions were in Hardy-Weinberg equilibrium. None of the SNPs alternate allele frequencies differed from the NCBI SNP database. No differences were found in the genotype distributions when analyzing each SNP and the two most common haplotypes in a covariate adjusted model. In a sex-specific stratification, males harboring the RAGE SNP rs1800625 AG or GG genotype had an independently increased risk for ischemic strokes compared to controls (adjusted OR = 1.27,95%CI 1.03-1.57, p_a = 0.0276). After the Benjamini-Hochberg procedure, a trend towards this association remained (p_BH = 0.1104).

CONCLUSION

No association of RAGE and HMGB1 genotypes variations with risk for overall ischemic stroke or specific stroke subtypes could be observed. Congruent with the literature, a sex-specific role of RAGE SNPs might associate with stroke susceptibility. The functional role of the HMGB1-RAGE axis in this context warrants further exploration.

At the Intersection of Gut Microbiome and Stroke: A Systematic Review of the Literature

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.

Global, regional, and national burden of stroke and its risk factors, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019

BACKGROUND

Regularly updated data on stroke and its pathological types, including data on their incidence, prevalence, mortality, disability, risk factors, and epidemiological trends, are important for evidence-based stroke care planning and resource allocation. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) aims to provide a standardised and comprehensive measurement of these metrics at global, regional, and national levels.

METHODS

We applied GBD 2019 analytical tools to calculate stroke incidence, prevalence, mortality, disability-adjusted life-years (DALYs), and the population attributable fraction (PAF) of DALYs (with corresponding 95% uncertainty intervals [UIs]) associated with 19 risk factors, for 204 countries and territories from 1990 to 2019. These estimates were provided for ischaemic stroke, intracerebral haemorrhage, subarachnoid haemorrhage, and all strokes combined, and stratified by sex, age group, and World Bank country income level.

FINDINGS

In 2019, there were 12·2 million (95% UI 11·0-13·6) incident cases of stroke, 101 million (93·2-111) prevalent cases of stroke, 143 million (133-153) DALYs due to stroke, and 6·55 million (6·00-7·02) deaths from stroke. Globally, stroke remained the second-leading cause of death (11·6% [10·8-12·2] of total deaths) and the third-leading cause of death and disability combined (5·7% [5·1-6·2] of total DALYs) in 2019. From 1990 to 2019, the absolute number of incident strokes increased by 70·0% (67·0-73·0), prevalent strokes increased by 85·0% (83·0-88·0), deaths from stroke increased by 43·0% (31·0-55·0), and DALYs due to stroke increased by 32·0% (22·0-42·0). During the same period, age-standardised rates of stroke incidence decreased by 17·0% (15·0-18·0), mortality decreased by 36·0% (31·0-42·0), prevalence decreased by 6·0% (5·0-7·0), and DALYs decreased by 36·0% (31·0-42·0). However, among people younger than 70 years, prevalence rates increased by 22·0% (21·0-24·0) and incidence rates increased by 15·0% (12·0-18·0). In 2019, the age-standardised stroke-related mortality rate was 3·6 (3·5-3·8) times higher in the World Bank low-income group than in the World Bank high-income group, and the age-standardised stroke-related DALY rate was 3·7 (3·5-3·9) times higher in the low-income group than the high-income group. Ischaemic stroke constituted 62·4% of all incident strokes in 2019 (7·63 million [6·57-8·96]), while intracerebral haemorrhage constituted 27·9% (3·41 million [2·97-3·91]) and subarachnoid haemorrhage constituted 9·7% (1·18 million [1·01-1·39]). In 2019, the five leading risk factors for stroke were high systolic blood pressure (contributing to 79·6 million [67·7-90·8] DALYs or 55·5% [48·2-62·0] of total stroke DALYs), high body-mass index (34·9 million [22·3-48·6] DALYs or 24·3% [15·7-33·2]), high fasting plasma glucose (28·9 million [19·8-41·5] DALYs or 20·2% [13·8-29·1]), ambient particulate matter pollution (28·7 million [23·4-33·4] DALYs or 20·1% [16·6-23·0]), and smoking (25·3 million [22·6-28·2] DALYs or 17·6% [16·4-19·0]).

INTERPRETATION

The annual number of strokes and deaths due to stroke increased substantially from 1990 to 2019, despite substantial reductions in age-standardised rates, particularly among people older than 70 years. The highest age-standardised stroke-related mortality and DALY rates were in the World Bank low-income group. The fastest-growing risk factor for stroke between 1990 and 2019 was high body-mass index. Without urgent implementation of effective primary prevention strategies, the stroke burden will probably continue to grow across the world, particularly in low-income countries.

FUNDING

Bill & Melinda Gates Foundation.

Outcomes of Mechanical Thrombectomy in the Early (<6-hour) and Extended (≥6-hour) Time Window Based Solely on Noncontrast CT and CT Angiography: A Propensity Score-Matched Cohort Study

BACKGROUND AND PURPOSE

Current stroke care recommendations for patient selection for mechanical thrombectomy in the extended time window demand advanced imaging to determine the stroke core volume and hypoperfusion mismatch, which may not be available at every center. We aimed to determine outcomes in patients selected for mechanical thrombectomy solely on the basis of noncontrast CT and CTA in the early (<6-hour) and extended (≥6-hour) time windows.

MATERIALS AND METHODS

Consecutive mechanical thrombectomies performed for acute large-vessel occlusion ischemic (ICA, M1, M2) stroke between February 2016 and August 2020 were retrospectively reviewed. Eligibility was based solely on demographics and noncontrast CT (ASPECTS) and CTA, due to the limited availability of perfusion imaging during the study period. Propensity score matching was performed to compare outcomes between time windows.

RESULTS

Of 417 mechanical thrombectomies performed, 337 met the inclusion criteria, resulting in 205 (60.8%) and 132 (39.2%) patients in the 0- to 6- and 6- to 24-hour time windows, respectively. The ASPECTS was higher in the early time window (9; interquartile range = 8-10) than the extended time window (9; interquartile range = 7-10; P = .005). Propensity score matching yielded 112 well-matched pairs. Equal rates of TICI 2b/3 revascularization and symptomatic intracranial hemorrhage were observed. A favorable functional outcome (mRS 0-2) at 90 days was numerically more frequent in the early window (45.5% versus 33.9%, P = .091). Mortality was numerically more frequent in the early window (25.9% versus 17.0%, P = .096).

CONCLUSIONS

Patients selected for mechanical thrombectomy in the extended time window solely on the basis of noncontrast CT and CTA still achieved decent rates of favorable 90-day functional outcomes, not statistically different from patients in the early time window.

A 5-Decade Analysis of Incidence Trends of Ischemic Stroke After Transient Ischemic Attack: A Systematic Review and Meta-analysis

Importance

Management of transient ischemic attack (TIA) has gained significant attention during the past 25 years after several landmark studies indicated the high incidence of a subsequent stroke.

Objective

To calculate the pooled event rate of subsequent ischemic stroke within 2, 7, 30, and 90 days of a TIA and compare this incidence among the population with TIA recruited before 1999 (group A), from 1999 to 2007 (group B), and after 2007 (group C).

Data Sources

All published studies of TIA outcomes were obtained by searching PubMed from 1996, to the last update on January 31, 2020, irrespective of the study design, document type, or language.

Study Selection

Of 11 516 identified citations, 175 articles were relevant to this review. Both the classic time-based definition of TIA and the new tissue-based definition were accepted. Studies with a combined record of patients with TIA and ischemic stroke, without clinical evaluation for the index TIA, with diagnosis of index TIA event after ischemic stroke occurrence, with low suspicion for TIA, or duplicate reports of the same database were excluded.

Data Extraction and Synthesis

The study was conducted and reported according to the PRISMA, MOOSE, and EQUATOR guidelines. Critical appraisal and methodological quality assessment used the Quality in Prognosis Studies tool. Publication bias was visualized by funnel plots and measured by the Begg-Mazumdar rank correlation Kendall τ2 statistic and Egger bias test. Data were pooled using double arcsine transformations, DerSimonian-Laird estimator, and random-effects models.

Main Outcomes and Measures

The proportion of the early ischemic stroke after TIA within 4 evaluation intervals (2, 7, 30, and 90 days) was considered as effect size.

Results

Systematic review yielded 68 unique studies with 223 866 unique patients from 1971 to 2019. The meta-analysis included 206 455 patients (58% women) during a span of 4 decades. The overall subsequent ischemic stroke incidence rates were estimated as 2.4% (95% CI, 1.8%-3.2%) within 2 days, 3.8% (95% CI, 2.5%-5.4%) within 7 days, 4.1% (95% CI, 2.4%-6.3%) within 30 days, and 4.7% (95% CI, 3.3%-6.4%) within 90 days. There was a recurrence risk of 3.4% among group A in comparison with 2.1% in group B or 2.1% in group C within 2 days; 5.5% in group A vs 2.9% in group B or 3.2% in group C within 7 days; 6.3% in group A vs 2.9% in group B or 3.4% in group C within 30 days, and 7.4% in group A vs 3.9% in group B or 3.9% in group C within 90 days.

Conclusions and Relevance

These findings suggest that TIA continues to be associated with a high risk of early stroke; however, the rate of post-TIA stroke might have decreased slightly during the past 2 decades.

Genetic basis of lacunar stroke: a pooled analysis of individual patient data and genome-wide association studies

BACKGROUND

The genetic basis of lacunar stroke is poorly understood, with a single locus on 16q24 identified to date. We sought to identify novel associations and provide mechanistic insights into the disease.

METHODS

We did a pooled analysis of data from newly recruited patients with an MRI-confirmed diagnosis of lacunar stroke and existing genome-wide association studies (GWAS). Patients were recruited from hospitals in the UK as part of the UK DNA Lacunar Stroke studies 1 and 2 and from collaborators within the International Stroke Genetics Consortium. Cases and controls were stratified by ancestry and two meta-analyses were done: a European ancestry analysis, and a transethnic analysis that included all ancestry groups. We also did a multi-trait analysis of GWAS, in a joint analysis with a study of cerebral white matter hyperintensities (an aetiologically related radiological trait), to find additional genetic associations. We did a transcriptome-wide association study (TWAS) to detect genes for which expression is associated with lacunar stroke; identified significantly enriched pathways using multi-marker analysis of genomic annotation; and evaluated cardiovascular risk factors causally associated with the disease using mendelian randomisation.

FINDINGS

Our meta-analysis comprised studies from Europe, the USA, and Australia, including 7338 cases and 254 798 controls, of which 2987 cases (matched with 29 540 controls) were confirmed using MRI. Five loci (ICA1L-WDR12-CARF-NBEAL1, ULK4, SPI1-SLC39A13-PSMC3-RAPSN, ZCCHC14, ZBTB14-EPB41L3) were found to be associated with lacunar stroke in the European or transethnic meta-analyses. A further seven loci (SLC25A44-PMF1-BGLAP, LOX-ZNF474-LOC100505841, FOXF2-FOXQ1, VTA1-GPR126, SH3PXD2A, HTRA1-ARMS2, COL4A2) were found to be associated in the multi-trait analysis with cerebral white matter hyperintensities (n=42 310). Two of the identified loci contain genes (COL4A2 and HTRA1) that are involved in monogenic lacunar stroke. The TWAS identified associations between the expression of six genes (SCL25A44, ULK4, CARF, FAM117B, ICA1L, NBEAL1) and lacunar stroke. Pathway analyses implicated disruption of the extracellular matrix, phosphatidylinositol 5 phosphate binding, and roundabout binding (false discovery rate <0·05). Mendelian randomisation analyses identified positive associations of elevated blood pressure, history of smoking, and type 2 diabetes with lacunar stroke.

INTERPRETATION

Lacunar stroke has a substantial heritable component, with 12 loci now identified that could represent future treatment targets. These loci provide insights into lacunar stroke pathogenesis, highlighting disruption of the vascular extracellular matrix (COL4A2, LOX, SH3PXD2A, GPR126, HTRA1), pericyte differentiation (FOXF2, GPR126), TGF-β signalling (HTRA1), and myelination (ULK4, GPR126) in disease risk.

FUNDING

British Heart Foundation.

SARS-CoV-2 and Stroke Characteristics: A Report From the Multinational COVID-19 Stroke Study Group

Supplemental Digital Content is available in the text.

Background and Purpose:

Stroke is reported as a consequence of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection in several reports. However, data are sparse regarding the details of these patients in a multinational and large scale.

Methods:

We conducted a multinational observational study on features of consecutive acute ischemic stroke, intracranial hemorrhage, and cerebral venous or sinus thrombosis among SARS-CoV-2–infected patients. We further investigated the risk of large vessel occlusion, stroke severity as measured by the National Institutes of Health Stroke Scale, and stroke subtype as measured by the TOAST (Trial of ORG 10172 in Acute Stroke Treatment) criteria among patients with acute ischemic stroke. In addition, we explored the neuroimaging findings, features of patients who were asymptomatic for SARS-CoV-2 infection at stroke onset, and the impact of geographic regions and countries’ health expenditure on outcomes.

Results:

Among the 136 tertiary centers of 32 countries who participated in this study, 71 centers from 17 countries had at least 1 eligible stroke patient. Of 432 patients included, 323 (74.8%) had acute ischemic stroke, 91 (21.1%) intracranial hemorrhage, and 18 (4.2%) cerebral venous or sinus thrombosis. A total of 183 (42.4%) patients were women, 104 (24.1%) patients were <55 years of age, and 105 (24.4%) patients had no identifiable vascular risk factors. Among acute ischemic stroke patients, 44.5% (126 of 283 patients) had large vessel occlusion; 10% had small artery occlusion according to the TOAST criteria. We observed a lower median National Institutes of Health Stroke Scale (8 [3–17] versus 11 [5–17]; P=0.02) and higher rate of mechanical thrombectomy (12.4% versus 2%; P<0.001) in countries with middle-to-high health expenditure when compared with countries with lower health expenditure. Among 380 patients who had known interval onset of the SARS-CoV-2 and stroke, 144 (37.8%) were asymptomatic at the time of admission for SARS-CoV-2 infection.

Conclusions:

We observed a considerably higher rate of large vessel occlusions, a much lower rate of small vessel occlusion and lacunar infarction, and a considerable number of young stroke when compared with the population studies before the pandemic. The rate of mechanical thrombectomy was significantly lower in countries with lower health expenditures.

Machine Learning-Enabled 30-Day Readmission Model for Stroke Patients

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.

Stroke in SARS-CoV-2 Infection: A Pictorial Overview of the Pathoetiology

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.

Variants at the MHC Region Associate With Susceptibility to Clostridioides difficile Infection: A Genome-Wide Association Study Using Comprehensive Electronic Health Records

Background

Clostridioides difficile is a major cause of healthcare-associated and community-acquired diarrhea. Host genetic susceptibility to Clostridioides difficile infection has not been studied on a large-scale.

Methods

A total of 1,160 Clostridioides difficile infection cases and 15,304 controls were identified by applying the eMERGE Clostridioides difficile infection algorithm to electronic health record data. A genome-wide association study was performed using a linear mixed model, adjusted for significant covariates in the full dataset and the antibiotic subgroup. Colocalization and MetaXcan were performed to identify potential target genes in Clostridioides difficile infection - relevant tissue types.

Results

No significant genome-wide association was found in the meta-analyses of the full Clostridioides difficile infection dataset. One genome-wide significant variant, rs114751021, was identified (OR = 2.42; 95%CI = 1.84-3.11; p=4.50 x 10^-8) at the major histocompatibility complex region associated with Clostridioides difficile infection in the antibiotic group. Colocalization and MetaXcan identified MICA, C4A/C4B, and NOTCH4 as potential target genes. Down-regulation of MICA, upregulation of C4A and NOTCH4 was associated with a higher risk for Clostridioides difficile infection.

Conclusions

Leveraging the EHR and genetic data, genome-wide association, and fine-mapping techniques, this study identified variants and genes associated with Clostridioides difficile infection, provided insights into host immune mechanisms, and described the potential for novel treatment strategies for Clostridioides difficile infection. Future replication and functional validation are needed.

Adherence to anticoagulant guideline for atrial fibrillation: A large care gap among stroke patients in a rural population

OBJECTIVE

This study aimed to investigate the prevalence and factors associated with oral anticoagulant undertreatment of atrial fibrillation (AF) among a cohort of rural patients with stroke outcomes and examine how undertreatment may influence a patient's one-year survival after stroke.

METHODS

This retrospective cohort study examined ischemic stroke patients with pre-stroke AF diagnosis from September 2003 to May 2019 and divided them into proper treatment and undertreatment group. Analysis included chi-square test, variance analysis, Kruskal-Wallis test, logistic regression, Kaplan-Meier estimator, and Cox proportional-hazards model.

RESULTS

Out of 1062 ischemic stroke patients with a pre-stroke AF diagnosis, 1015 patients had a CHA₂DS₂-VASc score ≥2, and 532 (52.4%) of those were undertreated. Median time from AF diagnosis to index stroke was significantly lower among undertreated patients (1.9 years vs. 3.6 years, p < 0.001). Other thromboembolism, excluding stroke, TIA, and myocardial infarction (OR 0.41, p < 0.001), the number of encounters per year (OR 0.90, p < 0.001), and the median time between AF diagnosis and stroke event (OR 0.86, p < 0.001) were negatively associated with undertreatment. Kaplan-Meier estimator showed no statistical difference in the one-year survival probability between groups (log-rank test, p = 0.29), while the Cox-Hazard model showed that age (HR 1.05, p < 0.001) and history of congestive heart failure (HR 1.88, p < 0.001) increased the risk of mortality.

CONCLUSIONS

More than half of our rural stroke patients with a pre-index AF diagnosis were not on guideline-recommended treatment. The study highlights a large care gap and an opportunity to improve AF management.

Polygenic Risk Scores Augment Stroke Subtyping

Objective

To determine whether the polygenic risk score (PRS) derived from MEGASTROKE is associated with ischemic stroke (IS) and its subtypes in an independent tertiary health care system and to identify the PRS derived from gene sets of known biological pathways associated with IS.

Methods

Controls (n = 19,806/7,484, age ≥69/79 years) and cases (n = 1,184/951 for discovery/replication) of acute IS with European ancestry and clinical risk factors were identified by leveraging the Geisinger Electronic Health Record and chart review confirmation. All Geisinger MyCode patients with age ≥69/79 years and without any stroke-related diagnostic codes were included as low risk control. Genetic heritability and genetic correlation between Geisinger and MEGASTROKE (EUR) were calculated using the summary statistics of the genome-wide association study by linkage disequilibrium score regression. All PRS for any stroke (AS), any ischemic stroke (AIS), large artery stroke (LAS), cardioembolic stroke (CES), and small vessel stroke (SVS) were constructed by PRSice-2.

Results

A moderate heritability (10%–20%) for Geisinger sample as well as the genetic correlation between MEGASTROKE and the Geisinger cohort was identified. Variation of all 5 PRS significantly explained some of the phenotypic variations of Geisinger IS, and the R² increased by raising the cutoff for the age of controls. PRSLAS, PRSCES, and PRSSVS derived from low-frequency common variants provided the best fit for modeling (R² = 0.015 for PRSLAS). Gene sets analyses highlighted the association of PRS with Gene Ontology terms (vascular endothelial growth factor, amyloid precursor protein, and atherosclerosis). The PRSLAS, PRSCES, and PRSSVS explained the most variance of the corresponding subtypes of Geisinger IS suggesting shared etiologies and corroborated Geisinger TOAST subtyping.

Conclusions

We provide the first evidence that PRSs derived from MEGASTROKE have value in identifying shared etiologies and determining stroke subtypes.

Abstract P308: Machine Learning Based Models of the 30-Day Readmission for Stroke Patients Using Electronic Health Record Data

Background: At a personalized level, identification of patients at higher risk of 30-day readmission and in need of special clinical attention could lower their chances of readmission. While at a system’s level, reducing hospital readmission improves the overall quality of care delivery and reduces the associated cost burden.

Objective: To enhance understanding of the predictors of 30-day readmission after ischemic stroke and identify high-risk individuals. We aimed to compare the performance and the predictive power of machine learning-based methods and identify the best model.

Method: The electronic health records (EHR) of acute ischemic stroke patients were extracted from two tertiary centers within the Geisinger Health System between January 1, 2015, and October 7, 2018. A total of 61 variables, including clinical variables, demographical characteristics, discharge status, and type of health insurance were used in this study. Patients were randomly split for model development (80%) and testing (20%). Random forest, gradient boosting machine, extreme gradient boosting (XGBoost), support vector machine, and logistic regression, were developed to predict the 30-day readmission after stroke. The models were evaluated based on the area under the curve (AUC), sensitivity, specificity, and positive predictive value (PPV).

Results: A total of 3,184 patients with ischemic stroke (mean age: 71±13.90 years, men: 51.06%) were included in this study. From the 3,184, 301 (9.40%) were readmitted within 30-day. The best performance was obtained when XGBoost was used with ROSE-sampling. The AUC for the test set was 0.74 (95% CI: 0.64-0.78) with PPV of 0.43. The top four predictors of the 30-day readmission model were National Institutes of Health Stroke Scale score above 24, insert an indwelling urinary catheter, hypercoagulable state, and percutaneous gastrostomy.

Conclusions: Machine learning model can be designed to predict 30-day readmission after stroke using structured data from EHR. Among the five algorithms analyzed, XGBoost had the best performance.

Abstract P261: Machine Learning-Enabled Prediction of Long-Term Stroke Recurrence Using Data From Electronic Health Records

Objective: The long-term risk of recurrent ischemic stroke, estimated to be between 17%-30%, cannot be reliably assessed. Our goal was to study whether machine-learning can be trained to predict stroke recurrence and identify key clinical variables and assess whether performance metrics can be optimized.

Methods: We used patient-level data from electronic health records, 6 algorithms (Logistic Regression, Extreme Gradient Boosting, Gradient Boosting Machine, Random Forest, Support Vector Machine, Decision Tree), 4 feature selection strategies, 5 prediction windows, and 2 sampling strategies to develop 288 models for up to 5-year stroke recurrence prediction. We further identified important clinical features and different optimization strategies.

Results: We included 2,091 ischemic stroke patients for this study. Model AUROC was stable for prediction windows of 1,2,3,4 and 5 years, with the highest score for the 1-year (0.79) and the lowest score for the 5-year prediction window (0.69). A total of 21(7%) models reached an AUROC above 0.73 while 110(38%) models reached an AUROC greater than 0.7. Among the 53 features analyzed, age, body mass index, and laboratory-based features (such as high-density lipoprotein, hemoglobin A1C, and creatinine) had the highest overall importance scores. The balance between specificity and sensitivity improved through sampling strategies.

Conclusion: All the selected six modeling algorithms could be trained to predict the long-term stroke recurrence and laboratory-based variables were highly associated with stroke recurrence. The latter could be targeted for personalized interventions. Model performance metrics could be optimized, and models can be implemented in the same healthcare system as intelligent decision support to improve outcomes.

Abstract P636: Social Determinants of Stroke Hospitalization and Mortality in the United States

Introduction: Stroke hospitalization and mortality are influenced by various social determinants. This ecological study aimed to determine the associations between social determinants and stroke hospitalization and outcome at county-level in the United States.

Methods: County-level data were recorded from the Centers for Disease Control and Prevention as of January 7, 2020. We considered four outcomes: all-age (1) Ischemic and (2) Hemorrhagic stroke Death rates per 100,000 individuals (ID and HD respectively), and (3) Ischemic and (4) Hemorrhagic stroke Hospitalization rate per 1,000 Medicare beneficiaries (IH and HH respectively).

Results: Data of 3,225 counties showed IH (12.5 ± 3.4) and ID (22.2 ± 5.1) were more frequent than HH (2.0 ± 0.4) and HD (9.8 ± 2.1). Income inequality as expressed by Gini Index was found to be 44.6% ± 3.6% and unemployment rate was 4.3% ± 1.5%. Only 29.8% of the counties had at least one hospital with neurological services. The uninsured rate was 11.0% ± 4.7% and people living within half a mile of a park was only 18.7% ± 17.6%. Age-adjusted obesity rate was 32.0% ± 4.5%. In regression models, age-adjusted obesity (OR for IH: 1.11; HH: 1.04) and number of hospitals with neurological services (IH: 1.40; HH: 1.50) showed an association with IH and HH. Age-adjusted obesity (ID: 1.16; HD: 1.11), unemployment (ID: 1.21; HD: 1.18) and income inequality (ID: 1.09; HD: 1.11) showed an association with ID and HD. Park access showed inverse associations with all four outcomes. Additionally, population per primary-care physician was associated with HH while number of pharmacy and uninsured rate were associated with ID. All associations and OR had p ≤0.04.

Conclusion: Unemployment and income inequality are significantly associated with increased stroke mortality rates.

Abstract P670: Obesity and Mortality After the First Ischemic Stroke: Is Obesity Paradox Real?

Introduction: Obesity 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 data extracted from different resources including electronic health records.

Methods: We analyzed consecutive ischemic stroke patients captured in the Geisinger NeuroScience Ischemic Stroke (GNSIS) database. Survival in first-time ischemic stroke patients was analyzed using Kaplan-Meier estimator, stratified by different BMI categories. The predictors of mortality at one-year were assessed using a multivariate Cox proportional hazards model.

Results: Among 6,703 first-time adult ischemic stroke patients, mean age was 70.2 ±13.5 years and 52% were men. Of these patients, 24% patients were non-overweight (BMI < 25), 34% were overweight (BMI 25-29.9) and 41% were obese (BMI ≥ 30). One-year survival probability was significantly higher in overweight patients (87%, 95% CI: [85.6 - 88.4], p<0.001) and obese patients (89.5%, 95% CI: [88.4 - 90.7], p<0.001) compared to non-overweight patients (78.1%, 95% CI: [76.0 - 80.1]). In multivariate analysis, one-year mortality was significantly lower in overweight and obese patients (overweight patients- HR = 0.61 [95% CI, 0.52 - 0.72]; obese patients- HR = 0.56 [95% CI, 0.48 - 0.67]). Other significant predictors of one-year mortality were age at the ischemic stroke event (HR = 1.04 [95% CI, 1.03 - 1.04]), history of neoplasm (HR = 1.59 [95% CI, 1.38 - 1.85]), atrial fibrillation or flutter (HR = 1.26 [95% CI, 1.09 - 1.46]), heart failure (HR = 1.68 [95% CI, 1.42 - 1.98]), diabetes mellitus (HR = 1.27 [95% CI, 1.1 - 1.47]), rheumatic disease (HR = 1.37 [95% CI, 1.05 - 1.78]) and myocardial infarction ((HR = 1.23 [95% CI, 1.02 - 1.48]).

Conclusion: Our 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.

Abstract P693: Sex Disparity in Long-Term Stroke Recurrence and Mortality in a Rural Population in the United States

Introduction: Several studies suggest women may be disproportionately affected by poorer stroke outcomes than men. This study aimed to investigate if women had a higher risk of all-cause mortality and recurrence after an ischemic stroke in a rural population in central Pennsylvania, United States.

Methods: We analyzed consecutive ischemic stroke patients captured in our comprehensive 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 8,900 adult ischemic stroke patients (median age of 71.6 [IQR: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 to a 65.7% survival in men (p=0.003) at five years; however, the survival difference was not present after adjustment by the associated factors of 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.

Abstract P635: Polygenic Risk Scores Augment Stroke Subtyping and Outcome Evaluation

Objective: To determine whether Polygenic Risk Scores (PRS) derived from MEGASTROKE is associated with ischemic stroke (IS) and its subtypes in an independent tertiary healthcare system and to identify PRS derived from gene-sets of known biological pathways associated with IS and its outcomes (mortality and recurrent stroke).

Methods: Controls(n=19806/7484, age≥69/79) and cases(n=1184/951 for discovery/replication) of acute IS with European ancestry and clinical risk factors identified by leveraging the Geisinger Electronic Health Record (EHR) and chart review confirmation. All Geisinger MyCode patients with age ≥69/79, without any stroke-related diagnostic codes as low-risk control. Genetic heritability and genetic correlation between Geisinger and MEGASTROKE(EUR) using the summary statistics of GWAS by LDSC. All PRS for any stroke — AS, any ischemic stroke — AIS, large artery stroke — LAS, cardioembolic stroke — CES, and small vessel stroke — SVS constructed by PRSice-2. PRS (top or bottom quantiles) derived from 7350 gene-sets of GO Biological Process was used to stratify the IS.

Results: A moderate heritability (10-20%) for Geisinger sample as well as the genetic correlation between MEGASTROKE and the Geisinger cohort was identified. Gene sets analyses highlighted the association of PRS with Gene Ontology terms (vascular endothelial growth factor, amyloid precursor protein, and atherosclerosis). The PRS LAS , PRS CES , and PRS SVS explained the most variance of the corresponding subtypes of Geisinger IS suggesting shared etiologies and corroborated Geisinger TOAST subtyping. We further evaluate PRS derived from the gene-sets of which GO terms may best differentiate the survival in IS cases with the corresponding high or low genetic burden. The result from the Cox regression analysis showed simultaneously the effect of several risk factors including PRS on mortality and recurrent stroke probabilities.

Conclusions: We provide the first evidence that PRSs derived from MEGASTROKE have value in identifying shared etiologies and determining stroke subtypes.