Blood test predicts stroke 15 years before onset

 Will your doctor and hospital ensure further research that identifies the EXACT PREVENTION PROTOCOLS FOR THIS? 

Do you prefer your doctor, hospital and board of director's incompetence NOT KNOWING? OR NOT DOING? Your choice; let them be incompetent or demand action!

Blood test predicts stroke 15 years before onset

A blood test could predict stroke and other cardiovascular diseases up to 15 years before symptoms appear, researchers say.

The tool, called CardiOmicScore, uses a single blood sample to generate personalised risk scores for six major cardiovascular conditions: coronary artery disease, stroke, heart failure, atrial fibrillation, peripheral artery disease and venous thromboembolism.

The system was developed by a team at the University of Hong Kong’s LKS Faculty of Medicine using deep learning, a type of artificial intelligence that spots patterns in large amounts of data.

The study used large-scale population data from the UK Biobank, combining multiomics data, including genomics, metabolomics and proteomics, and analysing 2,920 circulating proteins and 168 metabolites from blood samples.

Unlike polygenic risk scores, which estimate inherited risk based on a person’s genes and are largely fixed at birth, the tool is designed to reflect a person’s current biological state and how it may be changing over time.

Professor Zhang Qingpeng, associate professor in the department of pharmacology and pharmacy at the university, said: “Genes determine where we start.

“They define our baseline health risk. However, proteins and metabolites reflect our current physical health.

“Our AI tool is designed to decode these complex molecular signals, enabling doctors and patients to identify risks much earlier, which can potentially change the trajectory of disease through timely lifestyle modifications and early prevention.”

The researchers said CardiOmicScore transformed complex multiomics measurements into personalised risk scores with substantially improved predictive performance compared with conventional polygenic risk scores.

When combined with clinical information such as age and gender, it significantly improved risk prediction accuracy and could flag elevated risk up to 15 years before symptoms appear.

Zhang added: “We aim to leverage technology to identify and prevent diseases before they develop.

“By shifting health management from reactive treatment to proactive prediction and intervention, we aim to create a lasting impact for both public health and individual patient care.”

Cardiovascular diseases remain the leading cause of death worldwide, accounting for about 19.8 million deaths in 2022.

Doctors usually assess risk using factors such as age, blood pressure and smoking, but those measures can miss subtle biological changes before disease becomes clinically apparent.

Cardiologists Still Have Little Idea Who Is Going to Have a Heart Attack

 Just like all the stroke predictions models that suggested I had a minimal risk from stroke.

Cardiologists Still Have Little Idea Who Is Going to Have a Heart Attack

ABC-AF Risk Scores Offer Superior Stroke Prediction in AF Patients: Study

 WHOM is going to do the followup research that can prevent this stroke? THAT'S THE REAL RESEARCH NEEDED! Will never occur! There is NO leadership in stroke!

ABC-AF Risk Scores Offer Superior Stroke Prediction in AF Patients: Study

Read at link.

This Simple Test Could Predict Heart Attack and Stroke Risk 30 Years in Advance, Says

I can't get at this since I won't disable my ad blocker.

  This Simple Test Could Predict Heart Attack and Stroke Risk 30 Years in Advance, Says

Measurement of left atrial mechanical function found to improve stroke prediction

 'Measurements' by themselves are useless without actions needed to prevent such strokes.

Measurement of left atrial mechanical function found to improve stroke prediction

A cohort study of more than 4,500 persons without a history of atrial fibrillation (AF) or stroke has found that measuring left atrial mechanical function can improve stroke prediction. The findings are published in Annals of Internal Medicine.

AF is a serious public health problem because of its increasing prevalence in the aging population and its association with risks of cardiac thromboembolism and stroke. An intrinsically pro-thrombotic atrial myopathy, characterised by changes in left atrial mechanical function and size, may precede and promote development of AF. Evaluating left atrial mechanics and size may have utility in enhancing prediction of cardiac embolism and stroke earlier in a patient’s disease course before development of AF.

Researchers from multiple institutions including the Pennsylvania State University, University of California Los Angeles, and the University of Minnesota evaluated data from 4,917 persons participating in the ARIC (Atherosclerosis Risk in Communities) study. The authors found that left atrial mechanical dysfunction, detected by analysis of left atrial strain, was associated with ischemic stroke independently of left atrial size and risk factors from the CHADS-VASc score. They also found that the addition of left atrial reservoir strain to the CHADS-VASc variables improved stroke prediction and yielded a greater predicted net benefit, as shown by decision curve analysis. According to the authors, the results of this study support the hypothesis that atrial myopathy, characterised by left atrial mechanical dysfunction, is intrinsically prothrombotic, resulting in higher risk for cardiac embolism and ischemic stroke.

An accompanying editorial from authors at the Boston University Chobanian and Avedisian School of Medicine supports the study authors for bringing attention to the possible role of atrial cardiopathy in mediating cardioembolic stroke in the absence of AF and explains the real-world importance of their findings.

Population-based study comparing predictors of ischemic stroke recurrence after index ischemic stroke in non-elderly adults with or without diabetes

 Oh well more prediction crapola.

Population-based study comparing predictors of ischemic stroke recurrence after index ischemic stroke in non-elderly adults with or without diabetes

Elhefnawy ME, Sheikh Ghadzi SM, Tangiisuran B, et al.

International Journal of General Medicine|April 8, 2021

NEXT

Journal Summary

A population-based study was conducted to distinguish recurrent ischemic stroke (IS) predictors and to ascertain the possible effect of secondary preventive medications on IS recurrence in non-elderly adults with or without diabetes. Researchers extracted data of 3,386 patients < 60 years old who had a history of index IS from the Malaysian National Neurology Registry from 2009 to 2016. Via SPSS version 22, multivariate logistic regression analysis was performed. In non-elderly adults after the index IS event, IHD was found as the main predictor of IS recurrence, regardless of diabetes status. The data revealed that after index IS, receiving antidiabetic and antiplatelet medications upon discharge were significant predictors of recurrent IS in non-elderly diabetic adults. A randomized clinical trial may be needed to ascertain the effect of secondary preventive medication on IS recurrence, especially in non-elderly adults.

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Journal Summary

Read the full article on International Journal of General Medicine.

 

ACTB Methylation in Blood as a Potential Marker for the Pre-clinical Detection of Stroke: A Prospective Nested Case-Control Study

 When this is fully proved out your doctors and hospital will need SPECIFIC STROKE PREVENTION PROTOCOLS that will change your trajectory of stroke.  That is if we have stroke leaders that can put two and two together and get those prevention protocols created. Otherwise what is the use of predicting stroke if you can do nothing about it?

ACTB Methylation in Blood as a Potential Marker for the Pre-clinical Detection of Stroke: A Prospective Nested Case-Control Study

Chunlan Liu¹, Qiming Yin¹, Mengxia Li¹, Yao Fan², Chong Shen^1* and Rongxi Yang^1*

• ¹Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing, China
• ²Division of Clinical Epidemiology, Affiliated Geriatric Hospital of Nanjing Medical University, Nanjing, China

Background: 

Stroke is the second leading cause of death worldwide. If risk of stroke could be evaluated early or even at a preclinical stage, the mortality rate could be reduced dramatically. However, the identified genetic factors only account for 5–10% of the risk of stroke. Studies on the risk factors of stroke are urgently needed. We investigated the correlation between blood-based β-actin (ACTB) methylation and the risk of stroke in a prospective nested case-control study.

Methods: 

The methylation level of ACTB was quantitatively determined by mass spectrometry in 139 stroke cases who developed stroke within 2 years after recruitment and 147 age- and sex-matched controls who remained stroke-free in a median follow-up of 2.71 years.

Results: 

We observed a highly significant correlation between hypomethylation of one CpG site of ACTB and increased risk of stroke in an onset-time-dependent manner (for onset time ≤ 1.5 years: odds ratio (OR) per + 10% methylation = 0.76, P = 0.001; for onset time ≤ 1.32 years: OR per + 10% methylation = 0.59, P = 7.82 × 10^–7; for onset time ≤ 1 year: OR per + 10% methylation = 0.43, P = 3.00 × 10^–6), and the increased cumulative incidence of stroke (log-rank P = 3.13 × 10^–7). Neighboring CpG sites showed an inverse correlation with age and drinking status in controls (P < 0.05) but not in stroke cases.

Conclusion: 

We firstly reported the blood-based ACTB methylation as a marker for the risk evaluation and preclinical detection of stroke, which can be further modified by age and drinking.

Introduction

Stroke represents the second leading cause of death worldwide (GBD 2016 Causes of Death Collaborators, 2017) and has contributed to almost 5% of all disability-adjusted life-years (Feigin et al., 2017). In 2016, the worldwide lifetime risk of stroke for people aged 25 years and above approaches 25%; in China, the risk is estimated to be as high as 39.3% (41.1% in men and 36.7% in women) (GBD 2016 Lifetime Risk of Stroke Collaborators et al., 2018).

Stroke is a multifactorial disease and is related to several genetic factors, and genetic-environmental interaction (Benjamin et al., 2017). β-actin (encoded by ACTB), a highly conserved cytoskeletal protein, is widely distributed in all eukaryotic cells (Rubenstein, 1990). β-actin is characterized by its ability to polymerize and participate in a variety of cell functions, such as maintenance of cell shape, cell migration, division, growth, and signal transduction (Herman, 1993; Chen et al., 2016). Moustafa-Bayoumi et al. (2007) suggested that elevated actin polymerization and stress fiber formation would generate mechanical force to trigger the hypertrophic signaling pathway, subsequently resulting in vascular remodeling and hypertension that can reduce blood flow in brain and alter the mechanics and function of cerebral blood vessels, and ultimately increase the risk of stroke (Legrand et al., 1993; Ibrahim et al., 2006). Our previous study showed that ACTB polymorphisms may contribute to the genetic susceptibility to stroke (Yang et al., 2020), although the mechanism of ACTB polymorphisms to stroke remains unclear. Taken together, all the identified stroke-related genetic factors account for only 5–10% of the risk of stroke (Bevan et al., 2012; Malik and Dichgans, 2018). Studies on stroke risk factors are still urgently needed.

Epigenetic factors may contribute new hints for the understanding and evaluation of the risk of stroke (Felling and Song, 2015). Epigenetics refers to DNA modifications affecting gene expression that are not based on mutation of the underlying DNA sequence (Nicoglou and Merlin, 2017). DNA methylation, a major type of epigenetic regulation, mainly occurs at the cytosine of a cytosine-phosphate-guanine (CpG) dinucleotide in differentiated mammalian cells (Bird, 2007). Candidate approach studies have found certain stroke-associated aberrant DNA methylation patterns, such as altered methylation in LINE-1, ABCB1, and CBS genes in peripheral blood, but mainly in case-control studies with small sample sizes (Lin et al., 2014; Yang et al., 2015; Wang et al., 2019). So far, no data are available about the association between blood-based ACTB methylation and stroke, especially in prospective studies.

This study aimed to explore the relationship between DNA methylation of the ACTB gene in peripheral blood and stroke risk in a nested case-control study from a prospective cohort with a total of 11,151 subjects. The blood samples were collected at the time point of enrollment when all individuals were reported to be stroke-free. The subjects who later developed stroke within 2 years after enrollment in the cohort were defined as cases, and those who remained stroke-free during a median follow-up of 2.71 years were selected as controls matched by age and sex.

 

d-dimer Level as a Predictor of Recurrent Stroke in Patients With Embolic Stroke of Undetermined Source

Useless. This prediction does absolutely no good without a protocol implemented that will alleviate this risk.  The reason for stroke research is to help stroke survivors or actually prevent strokes. This does neither.  What were the mentors and senior researchers thinking? 

d-dimer Level as a Predictor of Recurrent Stroke in Patients With Embolic Stroke of Undetermined Source

Kang-Ho Choi

,

Ja-Hae Kim

,

Jae-Myung Kim

,

Kyung-Wook Kang

,

Changho Lee

,

Joon-Tae Kim

,

Seong-Min Choi

,

Man-Seok Park

,

Ki-Hyun Cho

Originally published11 May 2021https://doi.org/10.1161/STROKEAHA.120.033217Stroke. ;0:STROKEAHA.120.033217

Abstract

Background and Purpose:

This study aimed to investigate the value of d-dimer levels in predicting recurrent stroke in patients with embolic stroke of undetermined source. We also evaluated the underlying causes of recurrent stroke according to d-dimer levels.

Methods:

A total of 1431 patients with undetermined source were enrolled in this study and divided into quartiles according to their baseline plasma d-dimer levels. The primary outcome measure was the occurrence of recurrent stroke (ischemic or hemorrhagic) in the year following the stroke event.

Results:

The risk of recurrent stroke increased significantly with the increasing d-dimer quartile (log-rank P=0.001). Patients in the higher d-dimer quartiles had a higher probability of recurrent embolic stroke because of covert atrial fibrillation, hidden malignancy, or undetermined sources. Most recurrent strokes in Q3 and Q4 were embolic but not in Q1 or Q2. Multivariate analysis revealed that patients in Q3 and Q4 had a significantly increased risk of recurrent stroke compared with those in Q1 (hazard ratio, 3.12 [95% CI, 1.07−9.07], P=0.036; hazard ratio, 7.29 [95% CI, 2.59−20.52], P<0.001, respectively; P_trend<0.001). Binary analyses showed a significant association between a high d-dimer level above normal range and the risk of recurrent stroke (hazard ratio, 2.48 [95% CI, 1.31−4.70], P=0.005). In subgroup analyses, a high d-dimer level was associated with a significantly higher risk of recurrent stroke in men than in women (P=0.039).

Conclusions:

Our findings suggest that d-dimer levels can be a useful risk assessment biomarker for predicting recurrent stroke, especially embolic ischemic stroke, in patients with undetermined source.

• Download figure
• Download PowerPoint

 

 

d-dimer Level as a Predictor of Recurrent Stroke in Patients With Embolic Stroke of Undetermined Source

 Unless you have followup research that will reduce that risk to zero, there is nothing useful here for survivors.

d-dimer Level as a Predictor of Recurrent Stroke in Patients With Embolic Stroke of Undetermined Source

Kang-Ho Choi

,

Ja-Hae Kim

,

Jae-Myung Kim

,

Kyung-Wook Kang

,

Changho Lee

,

Joon-Tae Kim

,

Seong-Min Choi

,

Man-Seok Park

,

Ki-Hyun Cho

Originally published11 May 2021https://doi.org/10.1161/STROKEAHA.120.033217Stroke. ;0:STROKEAHA.120.033217

Abstract

Background and Purpose:

This study aimed to investigate the value of d-dimer levels in predicting recurrent stroke in patients with embolic stroke of undetermined source. We also evaluated the underlying causes of recurrent stroke according to d-dimer levels.

Methods:

A total of 1431 patients with undetermined source were enrolled in this study and divided into quartiles according to their baseline plasma d-dimer levels. The primary outcome measure was the occurrence of recurrent stroke (ischemic or hemorrhagic) in the year following the stroke event.

Results:

The risk of recurrent stroke increased significantly with the increasing d-dimer quartile (log-rank P=0.001). Patients in the higher d-dimer quartiles had a higher probability of recurrent embolic stroke because of covert atrial fibrillation, hidden malignancy, or undetermined sources. Most recurrent strokes in Q3 and Q4 were embolic but not in Q1 or Q2. Multivariate analysis revealed that patients in Q3 and Q4 had a significantly increased risk of recurrent stroke compared with those in Q1 (hazard ratio, 3.12 [95% CI, 1.07−9.07], P=0.036; hazard ratio, 7.29 [95% CI, 2.59−20.52], P<0.001, respectively; P_trend<0.001). Binary analyses showed a significant association between a high d-dimer level above normal range and the risk of recurrent stroke (hazard ratio, 2.48 [95% CI, 1.31−4.70], P=0.005). In subgroup analyses, a high d-dimer level was associated with a significantly higher risk of recurrent stroke in men than in women (P=0.039).

Conclusions:

Our findings suggest that d-dimer levels can be a useful risk assessment biomarker for predicting recurrent stroke, especially embolic ischemic stroke, in patients with undetermined source.

• Download figure
• Download PowerPoint

 

Analysis finds multiple social disadvantages magnify stroke risk

Great, blame the patient for their social disadvantages. Instead what would really, really be useful. SOLVE ALL THESE PROBLEMS IN STROKE. Get the hell out of stroke if this is the best you can do. Stroke risk prediction crapola.

Useless shit here:

Analysis finds multiple social disadvantages magnify stroke risk

Stroke Journal Report

Research Highlights:

• Living with multiple social disadvantages, including low education, low annual household income, social isolation, living in a neighborhood with high poverty or with poor public health infrastructure, lack of health insurance or being Black, collectively increases the risk of stroke.
• Younger individuals with multiple social disadvantages, such as Black women living in impoverished neighborhoods in the Southeast United States with inadequate access to healthcare, may be excellent candidates for focused, early interventions to help reduce strokes.

Embargoed until 4 a.m. CT/5 a.m. ET Thursday, July 16, 2020
DALLAS, July 16, 2020 — Having more social disadvantages can nearly triple your risk of stroke, particularly if you are under the age of 75, according to new research published today in Stroke, a journal of the American Stroke Association, a division of the American Heart Association.
Researchers already know that some social disadvantages, such as living in an impoverished or rural area, having a low education or income level, lacking health insurance or being Black, may contribute to increased stroke risk. In this study, researchers investigated if there’s a cumulative effect from having multiple social disadvantages – known as social determinants of health (SDOH).
“We were focused on understanding how having multiple social determinants of health affect stroke risk, and we found significant health disparities that have a profound impact on people’s lives, especially in vulnerable populations,” said co-first study author Evgeniya Reshetnyak, Ph.D., a senior research data analyst at Weill Cornell Medicine in New York City.
“Our study shows that the risk of stroke is amplified among individuals with multiple social determinants of health factors, especially for those who are younger than 75 years old. There is a cumulative effect of multiple social determinants of health. In fact, every additional disadvantage further increases stroke risk.”
Using data from participants in the REasons for Geographic And Racial Differences in Stroke (REGARDS) study, researchers examined information for 27,813 Black and white adults (average age 64.7) who lived in the contiguous U.S. (48 states) and the District of Columbia and were followed for 10 years. Data from America’s Health Ranking, which ranks public health infrastructure by state, were used to define states with poor public health infrastructure. During the REGARDS study, 1,470 incidents of stroke were reported among the participants.
Researchers found, among those younger than 75 years old compared to people with no social determinants of health factors:

• there is a cumulative effect of multiple social determinants of health - the risk of stroke is increased among those individuals with multiple social determinants of health;
• people with three or more social determinants of health were nearly two and a half times more likely to have incident stroke; and
• after adjusting for other risk factors, stroke risk remained 50% higher among those with three or more social determinants of health.

In addition, researchers noted:

• Black women specifically were more likely to have a greater number of social disadvantages; people with more social determinants of health were also more likely to have more traditional risk factors such as hypertension or Type 2 diabetes; and
• residents in the Southeastern part of the U.S. (North and South Carolina, Georgia, Tennessee, Mississippi, Alabama, Louisiana and Arkansas) were at higher risk for stroke due to poor dietary habits and less investment in social safety nets.

“There is a need for policies and interventions that specifically target younger vulnerable populations,” Reshetnyak said. “Early interventions are crucial for reducing stroke disparities. Although social determinants of health are difficult to change, their effect can be mitigated with timely interventions. However, programs may not be as effective at later ages when physiological factors may begin to dominate over social factors. “
“Health care professionals should pay special attention to those patients with multiple social determinants of health. Physicians should emphasize the importance of lifestyle changes, more aggressively control risk factors, and recommend available outreach and educational programs that could help reduce stroke risk.”
Further research is needed to identify which social determinants of health contribute the most to stroke risk so future policy decisions could be prioritized.
Limitations of the study include that some data was self-reported, and tobacco use and exposure was limited to traditional cigarettes. Other social determinants of health, including perceived discrimination, police brutality, racial discrimination in the penal system and environmental factors, were not included in the study. Additionally, Latinos and Asians and other vulnerable populations were not included in this study.
Co-authors are Mariella Ntamatungiro, M.D.; Laura Pinheiro, Ph.D., M.P.H.; Virginia Howard, Ph.D.; April Carson, Ph.D.; Kimberly Martin, Ph.D.; and Monika Safford, M.D. Author disclosures are in the manuscript.
The National Institute of Neurological Disorders and Stroke (NINDS) and the National Institute on Aging (NIA) of the National Institutes of Health and the U.S. Department of Health and Human Services supported the study.
Additional Resources:

• Available multimedia is on right column of release link - https://newsroom.heart.org/news/analysis-finds-multiple-social-disadvantages-magnify-stroke-risk?preview=0d3652c95e025bed9d1c74fd19aa72b1
• After July 16, 2020, view the manuscript online.
• Stroke risk factors
• Environment, culture, other social determinants play big role in heart health
• AHA video: Social Determinants of Health
• American Heart Association making first investments to tackle social determinants of health nationally, banking on health impact returns
• Follow AHA/ASA news on Twitter @HeartNews
• Follow news from Stroke, the ASA/AHA journal @StrokeAHA_ASA

Statements and conclusions of study authors published in American Heart Association scientific journals are solely those of the study authors and do not necessarily reflect the Association’s policy or position. The Association makes no representation or guarantee as to their accuracy or reliability. The Association receives funding primarily from individuals; foundations and corporations (including pharmaceutical, device manufacturers and other companies) also make donations and fund specific Association programs and events. The Association has strict policies to prevent these relationships from influencing the science content. Revenues from pharmaceutical and device corporations and health insurance providers are available at https://www.heart.org/en/about-us/aha-financial-information.
About the American Heart Association
The American Heart Association is a relentless force for a world of longer, healthier lives. We are dedicated to ensuring equitable health in all communities. Through collaboration with numerous organizations, and powered by millions of volunteers, we fund innovative research, advocate for the public’s health and share lifesaving resources. The Dallas-based organization has been a leading source of health information for nearly a century. Connect with us on heart.org, Facebook, Twitter or by calling 1-800-AHA-USA1.

###

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A score to predict one-year risk of recurrence after acute ischemic stroke

Totally fucking useless. Survivors don't give a shit about prediction, they want EXACT PROTOCOLS that prevent that possible stroke.  Does no one in stroke actually think?

A score to predict one-year risk of recurrence after acute ischemic stroke

 

Show all authors

Davide Strambo*, Alexandros Zachariadis*, Dimitris Lambrou, ...

First Published June 17, 2020 Research Article

https://doi.org/10.1177/1747493020932787

Article information

Abstract

Background

An acute ischemic stroke carries a substantial risk of further recurrences. We aimed at developing and validating a prognostic tool to predict one-year stroke recurrence after acute ischemic stroke.

Methods

An integer score was derived by Cox regression analysis on a hospital-referred cohort of 3246 acute ischemic stroke patients from Switzerland, and tested for external validity in three similar independent cohorts from Athens (n = 2495), Milan (n = 1279), and Helsinki (n = 714) by means of calibration and discrimination.

Results

In the derivation cohort, the recurrence rate was 7% (n = 228/3246). We developed a nine-point score comprising: previous stroke or transient ischemic attack (1-point), stroke mechanism (small vessel disease and unknown mechanism: 0-points; rare stroke mechanism: 3-points; other mechanisms: 1-point), pre-stroke antiplatelets (1-point), active malignancy (2-points), chronic cerebrovascular lesions on imaging (1-point) and absence of early ischemic changes on first imaging (1-point). In the derivation cohort, the one-year risk of re-stroke was 3.0% (95%CI 1.9–4.1) in 932 (29%) patients with a score 0–1, 7.2% (6.1–8.3) in 2038 (63%) with a score 2–4, and 19.2% (14.6–23.9) in 276 (8%) with a score ≥ 5. The score calibrated well in the Athens (recurrences = 208/2495), but not in the Helsinki (recurrences = 15/714) or Milan (recurrences = 65/1279) cohorts. The AUC was 0.67 in the derivation cohort, and 0.56, 0.70, and 0.63 in the Athens, Helsinki, and Milan cohorts, respectively.

Conclusion

We developed a score to predict one-year stroke recurrence risk in patients with acute ischemic stroke. Since the score was not completely validated when applied to external datasets where it displayed poor to fair calibration and discrimination, additional efforts are required to ameliorate our accuracy for predicting stroke recurrence, by better refining this prognostic tool or developing new ones. Clinical and radiological markers of established cerebrovascular disease and stroke etiology were better predictors than the usual demographic vascular risk factors.

Keywords Antithrombotic, cerebral infarction, ischemic stroke, prevention, risk factors, stroke

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Genomic risk score may predict ischemic stroke

Useless, No protocol given to prevent that risk of stroke.  

Genomic risk score may predict ischemic stroke

Abraham G, et al. Nat Commun. 2019;doi:10.1038/s41467-019-13848-1.

January 3, 2020

A genomic risk score developed with a meta-scoring approach may predict the risk for ischemic stroke, according to a study published in Nature Communications.
“The sequencing of the human genome has revealed many insights,” Michael Inouye, PhD, head of the systems genomics lab at Baker Heart and Diabetes Institute in Melbourne, Australia, said in a press release. “For common diseases such as stroke, it is clear that genetics is not destiny; however, each person does have their own innate risk for any particular disease. The challenge is now how we best incorporate this risk information into clinical practice so that the public can live healthier and longer.”

Gad Abraham , PhD, group leader of systems genomics at Baker Heart and Diabetes Institute, and colleagues developed a genomic risk score to predict ischemic stroke using a meta-scoring approach. The risk score was then evaluated in a validation group of 395,393 patients (mean age, 57 years; 46% men) from the UK Biobank who had an ischemic stroke event by age 75 years.
The genomic risk score was linked to ischemic stroke with a HF of 1.26 per standard deviation (95% CI, 1.22-1.31). This association was stronger compared with any individual genetic risk score that made up the meta-scoring approach risk score (HR = 1.18; 95% CI, 1.15-1.22). It was also twice as effective as the 90-SNP ischemic stroke score (HR = 1.13; 95% CI, 1.1-1.17).
Patients who comprised the top 0.25% of the population had a threefold increased risk for ischemic stroke compared with patients who were in the 45% to 55% of the population (HR = 3; 95% CI, 1.96-4.59).
The genomic risk score developed with a meta-scoring approach (incident ischemic stroke HR = 1.25 per standard deviation) had similar HRs to current smoking (incident ischemic stroke HR = 1.25 per standard deviation) and systolic BP (incident ischemic stroke HR = 1.28 per standard deviation). This developed score had a greater C-index compared with a family history of stroke (C = 0.558; 95% CI, 0.544-0.572).
Using the risk score, reductions in modifiable risk factors including systolic BP, smoking and BMI to match guideline targets may reduce the risk for ischemic stroke to 2.8% in men (95% CI, 1.7-3.9) and to 1.7% in women (95% CI, 1-2.4).(These are guidelines NOT protocols.)
“Taken together, despite challenges in phenotypic heterogeneity and corresponding genome-wide association study power, our study presents the most powerful ischemic stroke genomic risk score to date and assesses its potential for risk stratification in the context of established risk factors and clinical guidelines,” Abraham and colleagues wrote. “It lays the groundwork for larger genome-wide association study of stroke and its multiple subtypes as well as analyses which leverage the totality of information available for stroke genomic risk prediction.” – by Darlene Dobkowski
Disclosures: Inouye and Abraham report no relevant financial disclosures. Please see the study for all other authors’ relevant financial disclosures.

Predicting stroke in patients without atrial fibrillation

You lazy fuckers. We don't need more prediction crapola. We need solutions to all the problems in stroke.  I blame our fucking failures of stroke associations

for not setting up and following a stroke strategy leading to 100% recovery.

We want 100% recovery. GET THERE!

Predicting stroke in patients without atrial fibrillation

European Journal of Clinical Investigation — Steensig K, et al. | April 15, 2019

In patients without atrial fibrillation, researchers ascertained if the CHA₂DS₂-VASc score can predict the risk of atrial fibrillation and thromboembolic events. For this investigation, coronary angiography patients were grouped according to CHA₂DS₂-VASc score between 2004 and 2012. A total of 78,233 patients with group sizes ranging between 8,299 (CHA₂DS₂-VASc >4) and 19,882 (CHA₂DS₂-VASc 2) were included. The CHA₂DS₂-VASc score predicted a future diagnosis of atrial fibrillation and the composite risk of ischemic stroke, transient ischemic attack, or systemic embolism in patients without atrial fibrillation undergoing coronary angiography. In a patient with atrial fibrillation, a CHA₂DS₂-VASc score of 3 was related to a risk that would justify prophylactic oral anticoagulation treatment.

Read the full article on European Journal of Clinical Investigation

UMN researchers show how to improve prediction of stroke in patients with AFib

But if you have a stroke anyway you'll have to hope you have the proper intersection of these two statistics. Good luck with that.  I'm sure you really don't care about predictions and are much more interested in recovery statistics. Well then fuck off since stroke associations and researchers care much more about prediction than actually solving all the problems in stroke.

Solving stroke is too hard!

 tPA has been known to be a failure at complete recovery 88% of the time since approved in 1996. 

Full recovery from stroke only occurs 10% of the time. NSA statistic.

 It seems that there is some discrepancy between these statistics. I'm sure your stroke association is correcting that right now. But explainable via hemorrhagic strokes.

 

UMN researchers show how to improve prediction of stroke in patients with AFib

• Download PDF Copy

Reviewed by Kate Anderton, B.Sc.Jan 25 2019

Atrial fibrillation (AFib) is associated with a 5-fold increased risk of stroke. Nearly 3 million Americans are living with AFib. For years, researchers have been looking for ways to reduce the risk of stroke for this patient population. In a recent article published in Circulation, Lin Yee Chen, MD, MS, Associate Professor with tenure, Cardiovascular Division, in the Department of Medicine with the University of Minnesota Medical School demonstrates how to improve the prediction of stroke in patients with AFib.
The CHA2DS2-VASc score is a prediction tool that is commonly used to stratify the risk of stroke in patients with AFib. In this article, Dr. Chen and colleagues reported that in people with AFib, abnormal P-wave indices during sinus rhythm are associated with stroke independent of CHA2DS2- VASc variables. They did this by investigating groups of P-wave indices and tested their association with the risk of stroke in two population-based cohort studies known as ARIC and MESA. They also came up with a scoring system known as P2-CHA2DS2-VASc score.

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"We now possibly have a better scoring system that we can use to more accurately classify which patients with AFib are at higher risk of stroke, and who may require treatment to prevent stroke," explained Dr. Chen.
This discovery was a culmination of years of work, which grant funding (R01HL126637 and R01HL141288 from the National Heart, Lung and Blood Institute) allowed Chen to take to the next level.
"We hope to transform care for patients with AFib, but there is still additional research to be done," said Chen. "For example, we need more studies to confirm the reproducibility of P wave axis. In other words, if I did an ECG today and I repeat the ECG one week later, will it report the same number for the P wave axis?"
This discovery by Chen and colleagues is an important step and one which could have a big impact on the management of AFib because the P2-CHA2DS2-VASc score is easy to use and can be applied to a very wide community.

Source:

https://www.med.umn.edu/news-events/university-minnesota-medical-school-researchers-refine-ability-predict-atrial-fibrillation-related-stroke

Clinical and Imaging Predictors of Recurrent Ischemic Stroke: A Systematic Review and Meta-Analysis

You'll have to digest this one yourself. In my case since I had zero chance of having a stroke in the first place I don't trust anything here. No clue if I had large artery atherosclerosis. I suppose I'm supposed to trust that my doctor knows what to do here. Hard to trust since my doctor knew zilch about stroke recovery.  But I've been 12 years without another stroke. 

Clinical and Imaging Predictors of Recurrent Ischemic Stroke: A Systematic Review and Meta-Analysis

Kauw F.· de Jong H.W.A.M.a · Velthuis B.K.a · Kappelle L.J.b · Dankbaar J.W.a

Author affiliations

Corresponding Author

Keywords: Ischemic strokeRecurrent strokePredictors of outcomeImaging in strokeMeta-analysis

Cerebrovasc Dis 2018;45:279–287

https://doi.org/10.1159/000490422

Abstract

Background: Predictors of recurrent ischemic stroke are less well known in patients with a recent ischemic stroke than in patients with transient ischemic attack (TIA). We identified clinical and radiological factors for predicting recurrent ischemic stroke in patients with recent ischemic stroke. Methods: A systematic search in PubMed, Embase, Cochrane Library, and CINAHL was performed with the terms “ischemic stroke,” “predictors/determinants,” and “recurrence.” Quality assessment of the articles was performed and the level of evidence was graded for the articles included for the meta-analysis. Pooled risk ratios (RR) and heterogeneity (I²) were calculated using inverse variance random effects models.
Results: Ten articles with high-quality results were identified for meta-analysis. Past medical history of stroke or TIA was a predictor of recurrent ischemic stroke (pooled RR 2.5, 95% CI 2.1–3.1). Small vessel strokes were associated with a lower risk of recurrence than large vessel strokes (pooled RR 0.3, 95% CI 0.1–0.7). Patients with stroke of an undetermined cause had a lower risk of recurrence than patients with large artery atherosclerosis (pooled RR 0.5, 95% CI 0.2–1.1). We found no studies using CT or ultrasound for the prediction of recurrent ischemic stroke. The following MRI findings were predictors of recurrent ischemic stroke: multiple lesions (pooled RR 1.7, 95% CI 1.5–2.0), multiple stage lesions (pooled RR 4.1, 95% CI 3.1–5.5), multiple territory lesions (pooled RR 2.9, 95% CI 2.0–4.2), chronic infarcts (pooled RR 1.5, 95% CI 1.2–1.9), and isolated cortical lesions (pooled RR 2.2, 95% CI 1.5–3.2).  
Conclusions: In patients with a recent ischemic stroke, a history of stroke or TIA and the subtype large artery atherosclerosis are associated with an increased risk of recurrent ischemic stroke. Predictors evaluated with MRI include multiple ischemic changes and isolated cortical lesions. Predictors of recurrent ischemic stroke concerning CT or ultrasound have not been published.

Serum Hepatocyte Growth Factor Is Probably Associated With 3-Month Prognosis of Acute Ischemic Stroke

No clue what use this is to your recovery or prediction of stroke risk.
http://stroke.ahajournals.org/content/49/2/377?etoc=

Zhengbao Zhu, Tan Xu, Daoxia Guo, Xinfeng Huangfu, Chongke Zhong, Jingyuan Yang, Aili Wang, Chung-Shiuan Chen, Yanbo Peng, Tian Xu, Jinchao Wang, Yingxian Sun, Hao Peng, Qunwei Li, Zhong Ju, Deqin Geng, Jing Chen, Yonghong Zhang, Jiang He

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https://doi.org/10.1161/STROKEAHA.117.019476

Stroke. 2018;49:377-383

Originally published January 10, 2018

Zhengbao Zhu

From the Department of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China (Z.Z., T.X., D.G., X.H., C.Z., J.Y., A.W., T.X., H.P., Y.Z.); Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (C.Z., C.-S.C., J.C., J.H.); Department of Epidemiology, School of Public Health, Guizhou Medical University, Guiyang, China (J.Y.); Department of Neurology, Affiliated Hospital of North China University of Science and Technology, Hebei, China (Y.P.); Department of Neurology, Affiliated Hospital of Nantong University, Jiangsu, China (T.X.); Department of Neurology, Yutian County Hospital, Hebei, China (J.W.); Department of Cardiology, the First Affiliated Hospital of China Medical University, Liaoning (Y.S.); Department of Epidemiology, School of Public Health, Taishan Medical College, Shandong, China (Q.L.); Department of Neurology, Kerqin District First People’s Hospital of Tongliao City, Inner Mongolia, China (Z.J.); Department of Neurology, Affiliated Hospital of Xuzhou Medical College, Jiangsu, China (D.G.); and Department of Medicine, Tulane University School of Medicine, New Orleans, LA (J.C., J.H.).

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Tan Xu

From the Department of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China (Z.Z., T.X., D.G., X.H., C.Z., J.Y., A.W., T.X., H.P., Y.Z.); Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (C.Z., C.-S.C., J.C., J.H.); Department of Epidemiology, School of Public Health, Guizhou Medical University, Guiyang, China (J.Y.); Department of Neurology, Affiliated Hospital of North China University of Science and Technology, Hebei, China (Y.P.); Department of Neurology, Affiliated Hospital of Nantong University, Jiangsu, China (T.X.); Department of Neurology, Yutian County Hospital, Hebei, China (J.W.); Department of Cardiology, the First Affiliated Hospital of China Medical University, Liaoning (Y.S.); Department of Epidemiology, School of Public Health, Taishan Medical College, Shandong, China (Q.L.); Department of Neurology, Kerqin District First People’s Hospital of Tongliao City, Inner Mongolia, China (Z.J.); Department of Neurology, Affiliated Hospital of Xuzhou Medical College, Jiangsu, China (D.G.); and Department of Medicine, Tulane University School of Medicine, New Orleans, LA (J.C., J.H.).

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Daoxia Guo

From the Department of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China (Z.Z., T.X., D.G., X.H., C.Z., J.Y., A.W., T.X., H.P., Y.Z.); Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (C.Z., C.-S.C., J.C., J.H.); Department of Epidemiology, School of Public Health, Guizhou Medical University, Guiyang, China (J.Y.); Department of Neurology, Affiliated Hospital of North China University of Science and Technology, Hebei, China (Y.P.); Department of Neurology, Affiliated Hospital of Nantong University, Jiangsu, China (T.X.); Department of Neurology, Yutian County Hospital, Hebei, China (J.W.); Department of Cardiology, the First Affiliated Hospital of China Medical University, Liaoning (Y.S.); Department of Epidemiology, School of Public Health, Taishan Medical College, Shandong, China (Q.L.); Department of Neurology, Kerqin District First People’s Hospital of Tongliao City, Inner Mongolia, China (Z.J.); Department of Neurology, Affiliated Hospital of Xuzhou Medical College, Jiangsu, China (D.G.); and Department of Medicine, Tulane University School of Medicine, New Orleans, LA (J.C., J.H.).

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Xinfeng Huangfu

From the Department of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China (Z.Z., T.X., D.G., X.H., C.Z., J.Y., A.W., T.X., H.P., Y.Z.); Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (C.Z., C.-S.C., J.C., J.H.); Department of Epidemiology, School of Public Health, Guizhou Medical University, Guiyang, China (J.Y.); Department of Neurology, Affiliated Hospital of North China University of Science and Technology, Hebei, China (Y.P.); Department of Neurology, Affiliated Hospital of Nantong University, Jiangsu, China (T.X.); Department of Neurology, Yutian County Hospital, Hebei, China (J.W.); Department of Cardiology, the First Affiliated Hospital of China Medical University, Liaoning (Y.S.); Department of Epidemiology, School of Public Health, Taishan Medical College, Shandong, China (Q.L.); Department of Neurology, Kerqin District First People’s Hospital of Tongliao City, Inner Mongolia, China (Z.J.); Department of Neurology, Affiliated Hospital of Xuzhou Medical College, Jiangsu, China (D.G.); and Department of Medicine, Tulane University School of Medicine, New Orleans, LA (J.C., J.H.).

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Chongke Zhong

From the Department of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China (Z.Z., T.X., D.G., X.H., C.Z., J.Y., A.W., T.X., H.P., Y.Z.); Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (C.Z., C.-S.C., J.C., J.H.); Department of Epidemiology, School of Public Health, Guizhou Medical University, Guiyang, China (J.Y.); Department of Neurology, Affiliated Hospital of North China University of Science and Technology, Hebei, China (Y.P.); Department of Neurology, Affiliated Hospital of Nantong University, Jiangsu, China (T.X.); Department of Neurology, Yutian County Hospital, Hebei, China (J.W.); Department of Cardiology, the First Affiliated Hospital of China Medical University, Liaoning (Y.S.); Department of Epidemiology, School of Public Health, Taishan Medical College, Shandong, China (Q.L.); Department of Neurology, Kerqin District First People’s Hospital of Tongliao City, Inner Mongolia, China (Z.J.); Department of Neurology, Affiliated Hospital of Xuzhou Medical College, Jiangsu, China (D.G.); and Department of Medicine, Tulane University School of Medicine, New Orleans, LA (J.C., J.H.).

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Jingyuan Yang

From the Department of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China (Z.Z., T.X., D.G., X.H., C.Z., J.Y., A.W., T.X., H.P., Y.Z.); Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (C.Z., C.-S.C., J.C., J.H.); Department of Epidemiology, School of Public Health, Guizhou Medical University, Guiyang, China (J.Y.); Department of Neurology, Affiliated Hospital of North China University of Science and Technology, Hebei, China (Y.P.); Department of Neurology, Affiliated Hospital of Nantong University, Jiangsu, China (T.X.); Department of Neurology, Yutian County Hospital, Hebei, China (J.W.); Department of Cardiology, the First Affiliated Hospital of China Medical University, Liaoning (Y.S.); Department of Epidemiology, School of Public Health, Taishan Medical College, Shandong, China (Q.L.); Department of Neurology, Kerqin District First People’s Hospital of Tongliao City, Inner Mongolia, China (Z.J.); Department of Neurology, Affiliated Hospital of Xuzhou Medical College, Jiangsu, China (D.G.); and Department of Medicine, Tulane University School of Medicine, New Orleans, LA (J.C., J.H.).

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Aili Wang

From the Department of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China (Z.Z., T.X., D.G., X.H., C.Z., J.Y., A.W., T.X., H.P., Y.Z.); Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (C.Z., C.-S.C., J.C., J.H.); Department of Epidemiology, School of Public Health, Guizhou Medical University, Guiyang, China (J.Y.); Department of Neurology, Affiliated Hospital of North China University of Science and Technology, Hebei, China (Y.P.); Department of Neurology, Affiliated Hospital of Nantong University, Jiangsu, China (T.X.); Department of Neurology, Yutian County Hospital, Hebei, China (J.W.); Department of Cardiology, the First Affiliated Hospital of China Medical University, Liaoning (Y.S.); Department of Epidemiology, School of Public Health, Taishan Medical College, Shandong, China (Q.L.); Department of Neurology, Kerqin District First People’s Hospital of Tongliao City, Inner Mongolia, China (Z.J.); Department of Neurology, Affiliated Hospital of Xuzhou Medical College, Jiangsu, China (D.G.); and Department of Medicine, Tulane University School of Medicine, New Orleans, LA (J.C., J.H.).

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Chung-Shiuan Chen

From the Department of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China (Z.Z., T.X., D.G., X.H., C.Z., J.Y., A.W., T.X., H.P., Y.Z.); Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (C.Z., C.-S.C., J.C., J.H.); Department of Epidemiology, School of Public Health, Guizhou Medical University, Guiyang, China (J.Y.); Department of Neurology, Affiliated Hospital of North China University of Science and Technology, Hebei, China (Y.P.); Department of Neurology, Affiliated Hospital of Nantong University, Jiangsu, China (T.X.); Department of Neurology, Yutian County Hospital, Hebei, China (J.W.); Department of Cardiology, the First Affiliated Hospital of China Medical University, Liaoning (Y.S.); Department of Epidemiology, School of Public Health, Taishan Medical College, Shandong, China (Q.L.); Department of Neurology, Kerqin District First People’s Hospital of Tongliao City, Inner Mongolia, China (Z.J.); Department of Neurology, Affiliated Hospital of Xuzhou Medical College, Jiangsu, China (D.G.); and Department of Medicine, Tulane University School of Medicine, New Orleans, LA (J.C., J.H.).

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Yanbo Peng

From the Department of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China (Z.Z., T.X., D.G., X.H., C.Z., J.Y., A.W., T.X., H.P., Y.Z.); Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (C.Z., C.-S.C., J.C., J.H.); Department of Epidemiology, School of Public Health, Guizhou Medical University, Guiyang, China (J.Y.); Department of Neurology, Affiliated Hospital of North China University of Science and Technology, Hebei, China (Y.P.); Department of Neurology, Affiliated Hospital of Nantong University, Jiangsu, China (T.X.); Department of Neurology, Yutian County Hospital, Hebei, China (J.W.); Department of Cardiology, the First Affiliated Hospital of China Medical University, Liaoning (Y.S.); Department of Epidemiology, School of Public Health, Taishan Medical College, Shandong, China (Q.L.); Department of Neurology, Kerqin District First People’s Hospital of Tongliao City, Inner Mongolia, China (Z.J.); Department of Neurology, Affiliated Hospital of Xuzhou Medical College, Jiangsu, China (D.G.); and Department of Medicine, Tulane University School of Medicine, New Orleans, LA (J.C., J.H.).

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Tian Xu

From the Department of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China (Z.Z., T.X., D.G., X.H., C.Z., J.Y., A.W., T.X., H.P., Y.Z.); Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (C.Z., C.-S.C., J.C., J.H.); Department of Epidemiology, School of Public Health, Guizhou Medical University, Guiyang, China (J.Y.); Department of Neurology, Affiliated Hospital of North China University of Science and Technology, Hebei, China (Y.P.); Department of Neurology, Affiliated Hospital of Nantong University, Jiangsu, China (T.X.); Department of Neurology, Yutian County Hospital, Hebei, China (J.W.); Department of Cardiology, the First Affiliated Hospital of China Medical University, Liaoning (Y.S.); Department of Epidemiology, School of Public Health, Taishan Medical College, Shandong, China (Q.L.); Department of Neurology, Kerqin District First People’s Hospital of Tongliao City, Inner Mongolia, China (Z.J.); Department of Neurology, Affiliated Hospital of Xuzhou Medical College, Jiangsu, China (D.G.); and Department of Medicine, Tulane University School of Medicine, New Orleans, LA (J.C., J.H.).

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Jinchao Wang

From the Department of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China (Z.Z., T.X., D.G., X.H., C.Z., J.Y., A.W., T.X., H.P., Y.Z.); Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (C.Z., C.-S.C., J.C., J.H.); Department of Epidemiology, School of Public Health, Guizhou Medical University, Guiyang, China (J.Y.); Department of Neurology, Affiliated Hospital of North China University of Science and Technology, Hebei, China (Y.P.); Department of Neurology, Affiliated Hospital of Nantong University, Jiangsu, China (T.X.); Department of Neurology, Yutian County Hospital, Hebei, China (J.W.); Department of Cardiology, the First Affiliated Hospital of China Medical University, Liaoning (Y.S.); Department of Epidemiology, School of Public Health, Taishan Medical College, Shandong, China (Q.L.); Department of Neurology, Kerqin District First People’s Hospital of Tongliao City, Inner Mongolia, China (Z.J.); Department of Neurology, Affiliated Hospital of Xuzhou Medical College, Jiangsu, China (D.G.); and Department of Medicine, Tulane University School of Medicine, New Orleans, LA (J.C., J.H.).

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Yingxian Sun

From the Department of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China (Z.Z., T.X., D.G., X.H., C.Z., J.Y., A.W., T.X., H.P., Y.Z.); Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (C.Z., C.-S.C., J.C., J.H.); Department of Epidemiology, School of Public Health, Guizhou Medical University, Guiyang, China (J.Y.); Department of Neurology, Affiliated Hospital of North China University of Science and Technology, Hebei, China (Y.P.); Department of Neurology, Affiliated Hospital of Nantong University, Jiangsu, China (T.X.); Department of Neurology, Yutian County Hospital, Hebei, China (J.W.); Department of Cardiology, the First Affiliated Hospital of China Medical University, Liaoning (Y.S.); Department of Epidemiology, School of Public Health, Taishan Medical College, Shandong, China (Q.L.); Department of Neurology, Kerqin District First People’s Hospital of Tongliao City, Inner Mongolia, China (Z.J.); Department of Neurology, Affiliated Hospital of Xuzhou Medical College, Jiangsu, China (D.G.); and Department of Medicine, Tulane University School of Medicine, New Orleans, LA (J.C., J.H.).

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Hao Peng

From the Department of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China (Z.Z., T.X., D.G., X.H., C.Z., J.Y., A.W., T.X., H.P., Y.Z.); Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (C.Z., C.-S.C., J.C., J.H.); Department of Epidemiology, School of Public Health, Guizhou Medical University, Guiyang, China (J.Y.); Department of Neurology, Affiliated Hospital of North China University of Science and Technology, Hebei, China (Y.P.); Department of Neurology, Affiliated Hospital of Nantong University, Jiangsu, China (T.X.); Department of Neurology, Yutian County Hospital, Hebei, China (J.W.); Department of Cardiology, the First Affiliated Hospital of China Medical University, Liaoning (Y.S.); Department of Epidemiology, School of Public Health, Taishan Medical College, Shandong, China (Q.L.); Department of Neurology, Kerqin District First People’s Hospital of Tongliao City, Inner Mongolia, China (Z.J.); Department of Neurology, Affiliated Hospital of Xuzhou Medical College, Jiangsu, China (D.G.); and Department of Medicine, Tulane University School of Medicine, New Orleans, LA (J.C., J.H.).

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Qunwei Li

From the Department of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China (Z.Z., T.X., D.G., X.H., C.Z., J.Y., A.W., T.X., H.P., Y.Z.); Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (C.Z., C.-S.C., J.C., J.H.); Department of Epidemiology, School of Public Health, Guizhou Medical University, Guiyang, China (J.Y.); Department of Neurology, Affiliated Hospital of North China University of Science and Technology, Hebei, China (Y.P.); Department of Neurology, Affiliated Hospital of Nantong University, Jiangsu, China (T.X.); Department of Neurology, Yutian County Hospital, Hebei, China (J.W.); Department of Cardiology, the First Affiliated Hospital of China Medical University, Liaoning (Y.S.); Department of Epidemiology, School of Public Health, Taishan Medical College, Shandong, China (Q.L.); Department of Neurology, Kerqin District First People’s Hospital of Tongliao City, Inner Mongolia, China (Z.J.); Department of Neurology, Affiliated Hospital of Xuzhou Medical College, Jiangsu, China (D.G.); and Department of Medicine, Tulane University School of Medicine, New Orleans, LA (J.C., J.H.).

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Zhong Ju

From the Department of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China (Z.Z., T.X., D.G., X.H., C.Z., J.Y., A.W., T.X., H.P., Y.Z.); Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (C.Z., C.-S.C., J.C., J.H.); Department of Epidemiology, School of Public Health, Guizhou Medical University, Guiyang, China (J.Y.); Department of Neurology, Affiliated Hospital of North China University of Science and Technology, Hebei, China (Y.P.); Department of Neurology, Affiliated Hospital of Nantong University, Jiangsu, China (T.X.); Department of Neurology, Yutian County Hospital, Hebei, China (J.W.); Department of Cardiology, the First Affiliated Hospital of China Medical University, Liaoning (Y.S.); Department of Epidemiology, School of Public Health, Taishan Medical College, Shandong, China (Q.L.); Department of Neurology, Kerqin District First People’s Hospital of Tongliao City, Inner Mongolia, China (Z.J.); Department of Neurology, Affiliated Hospital of Xuzhou Medical College, Jiangsu, China (D.G.); and Department of Medicine, Tulane University School of Medicine, New Orleans, LA (J.C., J.H.).

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Deqin Geng

From the Department of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China (Z.Z., T.X., D.G., X.H., C.Z., J.Y., A.W., T.X., H.P., Y.Z.); Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (C.Z., C.-S.C., J.C., J.H.); Department of Epidemiology, School of Public Health, Guizhou Medical University, Guiyang, China (J.Y.); Department of Neurology, Affiliated Hospital of North China University of Science and Technology, Hebei, China (Y.P.); Department of Neurology, Affiliated Hospital of Nantong University, Jiangsu, China (T.X.); Department of Neurology, Yutian County Hospital, Hebei, China (J.W.); Department of Cardiology, the First Affiliated Hospital of China Medical University, Liaoning (Y.S.); Department of Epidemiology, School of Public Health, Taishan Medical College, Shandong, China (Q.L.); Department of Neurology, Kerqin District First People’s Hospital of Tongliao City, Inner Mongolia, China (Z.J.); Department of Neurology, Affiliated Hospital of Xuzhou Medical College, Jiangsu, China (D.G.); and Department of Medicine, Tulane University School of Medicine, New Orleans, LA (J.C., J.H.).

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Jing Chen

From the Department of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China (Z.Z., T.X., D.G., X.H., C.Z., J.Y., A.W., T.X., H.P., Y.Z.); Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (C.Z., C.-S.C., J.C., J.H.); Department of Epidemiology, School of Public Health, Guizhou Medical University, Guiyang, China (J.Y.); Department of Neurology, Affiliated Hospital of North China University of Science and Technology, Hebei, China (Y.P.); Department of Neurology, Affiliated Hospital of Nantong University, Jiangsu, China (T.X.); Department of Neurology, Yutian County Hospital, Hebei, China (J.W.); Department of Cardiology, the First Affiliated Hospital of China Medical University, Liaoning (Y.S.); Department of Epidemiology, School of Public Health, Taishan Medical College, Shandong, China (Q.L.); Department of Neurology, Kerqin District First People’s Hospital of Tongliao City, Inner Mongolia, China (Z.J.); Department of Neurology, Affiliated Hospital of Xuzhou Medical College, Jiangsu, China (D.G.); and Department of Medicine, Tulane University School of Medicine, New Orleans, LA (J.C., J.H.).

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Yonghong Zhang

From the Department of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China (Z.Z., T.X., D.G., X.H., C.Z., J.Y., A.W., T.X., H.P., Y.Z.); Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (C.Z., C.-S.C., J.C., J.H.); Department of Epidemiology, School of Public Health, Guizhou Medical University, Guiyang, China (J.Y.); Department of Neurology, Affiliated Hospital of North China University of Science and Technology, Hebei, China (Y.P.); Department of Neurology, Affiliated Hospital of Nantong University, Jiangsu, China (T.X.); Department of Neurology, Yutian County Hospital, Hebei, China (J.W.); Department of Cardiology, the First Affiliated Hospital of China Medical University, Liaoning (Y.S.); Department of Epidemiology, School of Public Health, Taishan Medical College, Shandong, China (Q.L.); Department of Neurology, Kerqin District First People’s Hospital of Tongliao City, Inner Mongolia, China (Z.J.); Department of Neurology, Affiliated Hospital of Xuzhou Medical College, Jiangsu, China (D.G.); and Department of Medicine, Tulane University School of Medicine, New Orleans, LA (J.C., J.H.).

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Jiang He

From the Department of Epidemiology, School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China (Z.Z., T.X., D.G., X.H., C.Z., J.Y., A.W., T.X., H.P., Y.Z.); Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (C.Z., C.-S.C., J.C., J.H.); Department of Epidemiology, School of Public Health, Guizhou Medical University, Guiyang, China (J.Y.); Department of Neurology, Affiliated Hospital of North China University of Science and Technology, Hebei, China (Y.P.); Department of Neurology, Affiliated Hospital of Nantong University, Jiangsu, China (T.X.); Department of Neurology, Yutian County Hospital, Hebei, China (J.W.); Department of Cardiology, the First Affiliated Hospital of China Medical University, Liaoning (Y.S.); Department of Epidemiology, School of Public Health, Taishan Medical College, Shandong, China (Q.L.); Department of Neurology, Kerqin District First People’s Hospital of Tongliao City, Inner Mongolia, China (Z.J.); Department of Neurology, Affiliated Hospital of Xuzhou Medical College, Jiangsu, China (D.G.); and Department of Medicine, Tulane University School of Medicine, New Orleans, LA (J.C., J.H.).

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Abstract

Background and Purpose—Serum hepatocyte growth factor (HGF) is positively associated with poor prognosis of heart failure and myocardial infarction, and it can also predict the risk of ischemic stroke in population. The goal of this study was to investigate the association between serum HGF and prognosis of ischemic stroke.

Methods—A total of 3027 acute ischemic stroke patients were included in this post hoc analysis of the CATIS (China Antihypertensive Trial in Acute Ischemic Stroke). The primary outcome was composite outcome of death or major disability (modified Rankin Scale score ≥3) within 3 months.

Results—After multivariate adjustment, elevated HGF levels were associated with an increased risk of primary outcome (odds ratio, 1.50; 95% confidence interval, 1.10–2.03; P_trend=0.015) when 2 extreme quartiles were compared. Each SD increase of log-transformed HGF was associated with 14% (95% confidence interval, 2%–27%) increased risk of primary outcome. Adding HGF quartiles to a model containing conventional risk factors improved the predictive power for primary outcome (net reclassification improvement: 17.50%, P<0.001; integrated discrimination index: 0.23%, P=0.022). The association between serum HGF and primary outcome could be modified by heparin pre-treatment (P_interaction=0.001), and a positive linear dose–response relationship between HGF and primary outcome was observed in patients without heparin pre-treatment (P_linearity<0.001) but not in those with heparin pre-treatment.

Conclusions—Serum HGF levels were higher in the more severe stroke at baseline, and elevated HGF levels were probably associated with 3-month poor prognosis independently of stroke severity among ischemic stroke patients, especially in those without heparin pre-treatment. Further studies from other samples of ischemic stroke patients are needed to validate our findings.