In the BWH primary care cohort, individuals in the top quintile of ECG2CAD risk faced a 5–10× higher hazard of myocardial infarction or heart failure, and nearly 3× higher all-cause mortality over ~8 years.

We observed a enhanced performance versus clinical risk scores such as PCE. This was particular seen in better average precision, which tells us how good a model flags high-risk individuals.

Across three test sets, ECG2CAD achieved AUROCs of 0.78 (MGH), 0.75 (BWH) and 0.76 (UK Biobank), outperforming models based on aage and sex. When combined with age and sex, the minimal information a clinican would have, performance improved further.

We developed ECG2CAD, a convolutional neural network trained on 764,670 12-lead ECGs from 137,036 patients at MGH, to predict prevalent CAD at the time of ECG. Validation was performed on independent cohorts from @MGH, Brigham & Women’s Hospital, and the UK Biobank.

Can a single ECG tell us who has coronary artery disease?

I’m excited to share the publication of our paper on using electrocardiogram-based artificial intelligence to predict prevalent coronary artery disease in @jaccjournals.bsky.social Advances.

Im very honored to have been awarded with the Young Investigator Award "Arrhythmia" at the 91st annual meeting of the German Cardiac Society for our work on using clinical, genetic and AI risk for prediction of AF risk. Thanks for all co-authors and my mentors Shaan Khurshid and Patrick Ellinor.

But what if this is only related to MRI and does not hold for the clinical standard diagnostic (Echo)? To answer this, we looked at the National Echo Database Australia (NEDA, N=365,870) where ASproposed criteria predicted increased all-cause mortality (HR 1.25) and CV mortality.

Crucially, this definition identified almost all people at risk for AV replacement. The cumulative incidence of AV surgery by year 6 among those without AS (n = 46,988) was 0.032% compared with 1.8% with mild ASproposed , 17.4% with moderate AS (n = 271), and 56.2% in severe AS (n = 30).

Applying these thresholds, 5.8% of participants were classified with mild ASproposed. They had a higher risk for AV procedures (HR 31.7), a risk of atrial fibrillation (HR 1.86), and heart failure (HR 2.37) compared with individuals without AS over ~4 years of follow-up.

In a healthy subcohort (n=41,859), we derived reference ranges for AV function by age&sex. We defined “mild ASproposed” (outside the 95th percentile >any age group) as any 1 of these criteria: peak velocity >1.65 m/s, mean gradient >4.8 mmHg, or AVA <2.1 cm² (men) / <1.7 cm² (women).

However, the UK Biobank MRIs don't come with clinical reports. So, we developed a deep learning model to segment the blood pool above the asc.aorta which allowed us to quantify aortic valve area, peak velocity, and mean gradient from velocity-encoded MRI data in 62,902 UK Biobank participants.

Happy to share our study “New Threshold for Defining Mild Aortic Stenosis Derived From Velocity-Encoded MRI in 60,000 Individuals” in which we identify a new threshold for Mild AS and the consequences of early aortic valve dysfunction. @jamespirruccello.com

authors.elsevier.com/a/1ks2B2d9GI...

These results support the notion that achieving the recommended MVPA is key for favorable adipose tissue profiles and cardiometabolic health, irrespective of whether the activity is concentrated or spread out, even for fat tissue deposition!

We observed that both active patterns were associated with sig lower VAT and EPAT compared to inactivity. Ranging to almost a liter less VAT! After accounting for total MVPA, the differences between activity patterns were no longer significant.

Participants were categorized as inactive, “weekend warriors” (concentrating MVPA in 1–2 days), or regularly active. We used previously derived visceral adipose tissue (VAT) (N = 14,903) and epicardial and pericardial adipose tissue (EPAT) (N = 17,146) from @ukbiobank.bsky.social MRIs.