AI turns routine ECGs into stroke risk predictors

 Is your competent? doctor up-to-date and bringing this into the hospital?

AI turns routine ECGs into stroke risk predictors

Using 12-lead ECGs from thousands of patients, researchers developed and validated an artificial intelligence (AI) model, dubbed ECG2Stroke, that accurately predicted 10-year risk of ischaemic stroke and demonstrated discrimination comparable to the established Framingham Stroke Risk Profile.

The model’s risk signals were strongly associated with atrial electrical abnormalities -- particularly P-wave features -- and showed a specific link to cardioembolic stroke.

The findings were published in the Journal of the American College of Cardiology.

“Existing tools to identify which patients are at the highest risk of stroke often require cumbersome clinical score calculations, are not easily scalable, and are therefore not used widely in routine practice,” said Rahul Mahajan, MD, Mass General Brigham, Boston, Massachusetts.

To find an alternative, the researchers developed and validated a deep learning model (ECG2Stroke) using 12-lead ECG data from more than 101,000 patients at Massachusetts General Hospital to estimate 10-year risk of ischemic stroke, integrating neural network outputs with age and sex in a survival model. The model was then externally tested across independent cohorts from Brigham and Women’s Hospital and Beth Israel Deaconess Medical Center, where its performance was assessed for discrimination, calibration, and comparison against the established Framingham Stroke Risk Profile.

Across validation datasets totalling tens of thousands of patients and thousands of stroke events, ECG2Stroke demonstrated moderate predictive performance with area under the receiver operating characteristic curve values around 0.77 to 0.80 and low calibration error, performing similarly to the Framingham score.

Model interpretability analyses indicated that risk predictions were driven in part by P-wave features on the ECG and were strongly associated with cardioembolic stroke, suggesting the system may be capturing markers of underlying atrial pathology relevant to stroke risk stratification.

“If confirmed after prospective, real-world studies, tools like this could identify which patients should be prioritised for intensive prevention efforts,” said coauthor Shaan Khurshid, MD, Mass General Brigham Heart and Vascular Institute. “The tool could also be helpful in driving future mechanistic research into abnormalities in the upper chambers of the heart and links to stroke.”

Reference: https://www.jacc.org/doi/10.1016/j.jacc.2026.03.084

SOURCE: Mass General Brigham

AI uses 12-lead ECGs to predict long-term stroke risk

 Do you really think your competent? doctor can get this into your hospital and accurately predict your next stroke without being confused by the factors that created your current stroke?

AI uses 12-lead ECGs to predict long-term stroke risk

Researchers have developed a new artificial intelligence (AI) model capable of reading 12-lead electrocardiograms (ECGs) and predicting a patient’s long-term stroke risk, sharing their findings in JACC.[1]

"Stroke remains a leading cause of death and disability worldwide,” wrote first author Rahul Mahajan MD, PhD, a researcher with the department of neurology at Brigham and Women’s Hospital (BWH), and colleagues. “Although age-adjusted rates of stroke have declined, the absolute number of strokes occurring annually continues to increase. Recurrent strokes account for only one-fourth of stroke events, and ischemic stroke is the most common type, highlighting a gap in primary prevention.”

The group trained its AI model, a convolutional neural network, with ECG data from more than 100,000 patients. The mean age was 57 years old, and 52% of patients were men. Patients were treated at BWH, Massachusetts General Hospital (MGH) or Beth Israel Deaconess Medical Center (BIDMC).

Overall, the AI model—ECG2Stroke—was associated with an area under the ROC curve (AUC) of 0.795 for MGH patients, 0.774 for BWH patients and 0.772 for BIDMC patients. In all cases, those AUC totals were comparable to the Framingham Stroke Risk Profile. 

In addition, the authors noted that their AI model was effective when looking at patients with or those without atrial fibrillation. 

“ECG2Stroke demonstrated consistent predictive utility across three test sets, including two large independent datasets spanning clinically and demographically varied populations,” the authors wrote. “Among individuals with available clinical data, ECG2Stroke provided similar discrimination of stroke risk compared with the validated FSRP clinical risk model. ECG2Stroke showed strong associations with clinical and ECG correlates of atrial dysfunction as well as cardio-embolic stroke, pointing to plausible risk mechanisms related to atrial cardiopathy.”

Mahajan et al. also explored which ECG features appeared to influence a patient’s stroke risk the most. 

“Examination of ECG neural network saliency maps demonstrated that variation in the ECG waveform near the region of the P-wave had the greatest effect on model predictions,” the authors wrote. “We examined associations of the ECG neural network component of ECG2Stroke with available ECG-based atrial substrate markers and found moderate correlation with PR interval and modest, but significant correlations with P-wave duration and P-wave axis.”

Reviewing these data, the group highlighted the potential of using ECG-based AI algorithms for “scalable stroke risk stratification.”

“Although all patients may benefit from lifestyle choices to promote cerebrovascular health, long-term stroke risk estimation offers the potential to efficiently prioritize individuals for intensification of general cardiovascular and stroke-specific primary prevention efforts, including improved control of known atherosclerotic cardiovascular disease risk factors or consideration of rhythm monitoring.”

Click here for the full study in JACC, an American College of Cardiology journal.

Echocardiographic correlates of MRI imaging markers of cerebral small-vessel disease in patients with atrial-fibrillation-related ischemic stroke

 

I see nothing here that is going to get survivors recovered or EXACTLY prevent dementia. So I would fire everyone involved. Solve the problems of stroke survivors, not just tell us they exist.

Echocardiographic correlates of MRI imaging markers of cerebral small-vessel disease in patients with atrial-fibrillation-related ischemic stroke

Kaili Ye^1†, Wendan Tao^1†, Zhetao Wang², Dayan Li³, Mangmang Xu¹, Junfeng Liu¹ and Ming Liu^1*

• ¹Department of Neurology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
• ²Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
• ³Cardiac Ultrasound Office, Department of Cardiology, West China Hospital, Sichuan University, Chengdu, China

Background and objectives: Atrial fibrillation (AF) has been linked to dementia risk, partly explained by cerebral small vessel disease (CSVD). Since AF and cardiovascular comorbidities were associated with cardiac dysfunction, we aimed to determine the association between echocardiographic parameters and neuroimaging markers of CSVD in patients with AF-related ischemic stroke.

Methods: This cross-sectional study enrolled patients with AF-related ischemic stroke from March 2013 to December 2019 who underwent transthoracic echocardiography and brain 3T MRI, including T1, T2, Flair, and SWI imaging sequences. We assessed the presence of lacunes and cerebellar microbleeds (CMBs), the severity of white matter hyperintensity (WMH) scored by the Fazekas scale (0-6), and the severity of enlarged perivascular spaces (EPVS) in basal ganglia (BG) and centrum semiovale (CSO) classified into three categories (0–10, 10–25, and >25). CSVD burden was rated on a 0-to-4 ordinal scale. Generalized linear regression analysis and post hoc comparisons with Bonferroni correction were performed to assess the association between various echocardiographic parameters and these lesions, adjusted for demographics and potential confounders.

Results: 119 patients (68.38 ± 12.692 years; male 45.4 %) were included for analysis, of whom 55 (46.2%) had lacunes, 40 (33.6%) had CMBs, and median severity for WMH, BG-EPVS, CSO-EPVS, and CSVD burden were 2 (IQR: 1–3), 1 (IQR: 1–2), 1 (IQR: 0–1), and 1 (IQR: 1–2) respectively. In multivariable, fully adjusted models, left ventricular posterior wall thickness (LVPW) was associated with a higher risk of lacunes (RR 1.899, 95% CI: 1.342–2.686) and CSVD burden (RR = 2.081, 95%CI: 1.562–2.070). Right atrial diameter (RAD) was associated with greater CSO-EPVS (RR = 2.243, 95%CI: 1.234–4.075). No echocardiographic parameters were revealed to be associated with CMBs and WMH.

Conclusion: In patients with AF-related ischemic stroke, LVPW is associated with a higher risk of lacunes and CSVD burden, while RAD was associated with greater CSO-EPVS. Larger studies are required to determine these associations and to elucidate if these associations can help facilitate cognitive evaluation and brain MRI screening.

Introduction

Increasing evidence suggests that atrial fibrillation (AF) appears to be correlated with cognitive decline independent of clinical stroke (1, 2). Cerebral small vessel disease (CSVD) is one of the pathological mechanisms through which AF might lead to cognitive impairment. A more recent large sample of study (3) has demonstrated that nearly 25% of patients with AF-related ischemic stroke or transient ischemic attack have preexisting cognitive impairment. They found imaging markers of CSVD were independently associated with cognitive impairment prior to ischemic events.

There is no complete explanation of the mechanism that links AF and CSVD, but chronic cerebral hypoperfusion, inflammation, and shared vascular risk factors, such as hypertension and diabetes mellitus, may all be involved. Patients with AF are more likely to develop cardiac dysfunction, for example, many patients with AF develop an enlarged left atrium (LA) and enlarged left ventricular (LV), and are also associated with an increased incidence of heart failure. Conversely, patients with cardiac dysfunction are known to contribute to AF development and maintenance (4–6). According to previous studies, some cardiac subclinical indicators, such as left ventricular structure and LV systolic dysfunction, may contribute to greater white matter hyperintensity (WMH) (6), and LA volume, may contribute to silent brain infarcts (7) even in the absence of AF. However, none of them investigated whether cardiac structural or functional abnormalities correlated with neuroimaging markers of CSVD in patients with AF-related ischemic stroke. Thus, we evaluated the cross-sectional association of the echocardiographic parameters of cardiac structure or function with the neuroimaging markers of CSVD on MRI in patients with AF-related ischemic stroke.

More at link.

Want to prevent a stroke? Combine wearables

Wrong, wrong, blitheringly wrong.  These are only going to notify you to take preventive measures.

Want to prevent a stroke? Combine wearables

AliveCor says their new combination of ECG and blood pressure can laser target stroke.
The first iOS app to roll up FDA-approved electrocardiogram (ECG) and blood pressure readings in a single display was introduced Thursday by AliveCor. It's the latest example of the increasing importance of health and medical insights in a field dominated by simpler heart rate bands.
AliveCor's Kardia Mobile app pulls data from the $99 Kardia handheld device, which records ECG from your fingertips, and blood pressure data from any Bluetooth wireless blood pressure cuff made by medical device giant Omron.
AliveCor portrays the app as more than just a dual-device information display. "We not only have FDA approval for our physical device, but this app's instant analysis that tells you if we've detected atrial fibrillation is a separate FDA clearance," says Vic Gundotra, AliveCor's CEO and former senior executive at Google and Microsoft. The FDA has clearance authority for consumer medical devices that read ECG or render a potential diagnosis.

"Somebody with high blood pressure has almost twice the risk of stroke than somebody without high blood pressure," wrote Ralph L. Sacco, M.D., professor and chairman of neurology at the University of Miami, in a recent American Heart Association brief. "But someone with atrial fibrillation has more than five times the risk of stroke."
The app user will certainly want an expert opinion of anything suspect. Gundotra says the Kardia app can share data via an emailed PDF or an API that allows electronic medical records systems to pull from it. That doesn't mean all physicians are ready to embrace patient-generated health data when their practices are based on a fee-for-service model billed by the visit and procedure.
While US consumers will have to pay $99 to get the AliveCor Kardia ECG device and its new app, Britain's National Health Service has decided to start paying for it and other medical devices for some patients starting in April 2017.
In addition to their current handheld style ECG device, AliveCor also has one in the form of an Apple Watch band, but it is still pending FDA approval. Similarly, Omron is working on its Project Zero, a blood pressure monitor in the form of a watch that is slated for release later this year.

Smartphone ECG could prevent strokes

I bet its 10 years before your doctor tells you about this. Prove me wrong.
http://bigpondnews.com/articles/Health/2013/08/10/Smartphone_ECG_could_prevent_strokes_895241.html