Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2024 Oct;17(10):e012959.
doi: 10.1161/CIRCEP.124.012959. Epub 2024 Aug 28.

Electrocardiogram-Based Artificial Intelligence to Discriminate Cardioembolic Stroke and Stratify Risk of Atrial Fibrillation After Stroke

Shaan Khurshid  1   2   3 , Samuel F Friedman  4 , Shinwan Kany  5 , Rahul Mahajan  3   6 , Ashby C Turner  7   3 , Steven A Lubitz  1   2   3 , Mahnaz Maddah  4 , Patrick T Ellinor  1   2   3 , Christopher D Anderson  3   6
Affiliations

Electrocardiogram-Based Artificial Intelligence to Discriminate Cardioembolic Stroke and Stratify Risk of Atrial Fibrillation After Stroke

Shaan Khurshid et al. Circ Arrhythm Electrophysiol. 2024 Oct.
No abstract available

Keywords: anticoagulants; atrial fibrillation; deep learning; risk factors; stroke.

PubMed Disclaimer

Conflict of interest statement

Dr Lubitz is employed at Novartis as of July 2022. Dr Lubitz previously received sponsored research support from Bristol Myers Squibb, Pfizer, Boehringer Ingelheim, Fitbit, Medtronic, Premier, and IBM and has consulted for Bristol Myers Squibb, Pfizer, Blackstone Life Sciences, and Invitae. Dr Ellinor receives sponsored research support from Bayer AG, IBM Research, Bristol Myers Squibb, Pfizer, and Novo Nordisk; he has also served on advisory boards or consulted for MyoKardia and Bayer AG. Dr Anderson has received sponsored research support from Bayer AG. The other authors report no conflicts.

Figures

Figure.
Figure.
Summary of analysis and results. Panel A depicts an overview of the current study. A total of 964 individuals with acute stroke and paired ECG were included. A total of three AF risk estimation schemes were compared: a) CHARGE-AF, a validated clinical risk score, b) ECG-AI, a deep learning model using 12-lead ECG, and c) CH-AI a combination of CHARGE-AF and ECG-AI. Panel B depicts discrimination performance for each model, using univariable odds ratios on the left and receiver operating characteristic curves on the right. Panel C depicts the cumulative risk of AF among 606 individuals with no AF before or within 30 days after stroke and longitudinal follow-up available after stroke. The left panel depicts the cumulative risk of AF stratified by an ECG-AI score above the 90th percentile, and the right panel depicts the cumulative risk of AF stratified by an ECG-AI score below the 10th percentile. Shaded regions denote 95% confidence intervals.
See this image and copyright information in PMC

References

    1. Khurshid S, Trinquart L, Weng L-C, Hulme OL, Guan W, Ko D, Schwab K, Rost NS, Al-Alusi MA, Benjamin EJ, et al. Atrial Fibrillation Risk and Discrimination of Cardioembolic From Noncardioembolic Stroke. Stroke. 2020;51:1396–1403. - PMC - PubMed
    1. Alonso A, Krijthe BP, Aspelund T, Stepas KA, Pencina MJ, Moser CB, Sinner MF, Sotoodehnia N, Fontes JD, Janssens ACJW, et al. Simple risk model predicts incidence of atrial fibrillation in a racially and geographically diverse population: the CHARGE-AF consortium. J Am Heart Assoc. 2013;2:e000102. - PMC - PubMed
    1. Khurshid S, Friedman S, Reeder C, Di Achille P, Diamant N, Singh P, Harrington LX, Wang X, Al-Alusi MA, Sarma G, et al. Electrocardiogram-based Deep Learning and Clinical Risk Factors to Predict Atrial Fibrillation. Circulation. 2021; - PMC - PubMed
    1. Khurshid S, Keaney J, Ellinor PT, Lubitz SA. A Simple and Portable Algorithm for Identifying Atrial Fibrillation in the Electronic Medical Record. Am J Cardiol. 2016;117:221–225. - PMC - PubMed
    1. Khurshid S, Li X, Ashburner JM, Lipsanopoulos ATT, Lee PR, Lin AK, Ko D, Ellinor PT, Schwamm LH, Benjamin EJ, et al. Usefulness of Rhythm Monitoring Following Acute Ischemic Stroke. Am J Cardiol. 2021;147:44–51. - PubMed