Artificial Intelligence-Enabled ECG to Identify Silent Atrial Fibrillation in Embolic Stroke of Unknown Source

Rabinstein, Alejandro A.; Yost, Micah D.; Faust, Louis; Kashou, Anthony H.; Latif, Omar; Graff‐Radford, Jonathan; Attia, Zachi I.; Yao, Xiaoxi; Noseworthy, Peter A.; Friedman, Paul A.

doi:10.1016/j.jstrokecerebrovasdis.2021.105998

Cited by 20 publications

(10 citation statements)

References 25 publications

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“…Among the 226 patients with ESUS who underwent ambulatory cardiac monitoring, a higher AI‐ECG AF prediction model output was associated with a higher likelihood of AF detected by cardiac monitoring (OR 1.04 [95% CI 1.02, 1.06], p = 0.004). Also, an AF prediction model output of >20% was strongly associated with confirmed AF detected by cardiac monitoring (OR 5.47 [95% CI 1.51, 22.51], p = 0.01) 22 . Of note, the risk of future AF estimated from the AF prediction model output in our current study might not be the same as the above studies due to different patient demographics.…”

Section: Discussionmentioning

confidence: 46%

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Migraine with aura associates with a higher artificial intelligence: ECG atrial fibrillation prediction model output compared to migraine without aura in both women and men

et al. 2022

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Objective To compare the artificial intelligence‐enabled electrocardiogram (AI‐ECG) atrial fibrillation (AF) prediction model output in patients with migraine with aura (MwA) and migraine without aura (MwoA). Background MwA is associated with an approximately twofold risk of ischemic stroke. Longitudinal cohort studies showed that patients with MwA have a higher incidence of developing AF compared to those with MwoA. The Mayo Clinic Cardiology team developed an AI‐ECG algorithm that calculates the probability of concurrent paroxysmal or impending AF in ECGs with normal sinus rhythm (NSR). Methods Adult patients with an MwA or MwoA diagnosis and at least one NSR ECG within the past 20 years at Mayo Clinic were identified. Patients with an ECG‐confirmed diagnosis of AF were excluded. For each patient, the ECG with the highest AF prediction model output was used as the index ECG. Comparisons between MwA and MwoA were conducted in the overall group (including men and women of all ages), women only, and men only in each age range (18 to <35, 35 to <55, 55 to <75, ≥75 years), and adjusted for age, sex, and six common vascular comorbidities that increase risk for AF. Results The final analysis of our cross‐sectional study included 40,002 patients (17,840 with MwA, 22,162 with MwoA). The mean (SD) age at the index ECG was 48.2 (16.0) years for MwA and 45.9 (15.0) years for MwoA (p < 0.001). The AF prediction model output was significantly higher in the MwA group compared to MwoA (mean [SD] 7.3% [15.0%] vs. 5.6% [12.4%], mean difference [95% CI] 1.7% [1.5%, 2.0%], p < 0.001). After adjusting for vascular comorbidities, the difference between MwA and MwoA remained significant in the overall group (least square means of difference [95% CI] 0.7% [0.4%, 0.9%], p < 0.001), 18 to <35 (0.4% [0.1%, 0.7%], p = 0.022), and 35 to <55 (0.5% [0.2%, 0.8%], p < 0.001), women of all ages (0.6% [0.3%, 0.8%], p < 0.001), men of all ages (1.0% [0.4%, 1.6%], p = 0.002), women 35 to <55 (0.6% [0.3%, 0.9%], p < 0.001), and men 18 to <35 (1.2% [0.3%, 2.1%], p = 0.008). Conclusions Utilizing a novel AI‐ECG algorithm on a large group of patients, we demonstrated that patients with MwA have a significantly higher AF prediction model output, implying a higher probability of concurrent paroxysmal or impending AF, compared to MwoA in both women and men. Our results suggest that MwA is an independent risk factor for AF, especially in patients <55 years old, and that AF‐mediated cardioembolism may play a role in the migraine–stroke association for some patients.

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Section: Discussionmentioning

confidence: 46%

“…Another study evaluated the performance of the AI‐ECG AF prediction model in patients with embolic stroke of undetermined source (ESUS), 22 a population with a high risk of undiagnosed AF. Prolonged cardiac monitoring is often arranged as part of the stroke evaluation for patients with ESUS.…”

Section: Discussionmentioning

confidence: 99%

Migraine with aura associates with a higher artificial intelligence: ECG atrial fibrillation prediction model output compared to migraine without aura in both women and men

et al. 2022

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“…Another area for AI implementation is the identification of patients who may benefit from anticoagulation following an embolic stroke of unknown source (ESUS). A probability greater than 0.2, of underlying atrial fibrillation (AF) determined by AI-ECG analysis, in patients with ESUS was associated with increased AF detection during ambulatory cardiac rhythm monitoring [ 22 ]. Recent work has demonstrated that ML algorithms can detect AF by restitution analysis of normal sinus rhythm ECGs with k-nearest neighbour (k-NN) algorithm [ 23 ].…”

Section: Clinical Significancementioning

confidence: 99%

Clinical significance, challenges and limitations in using artificial intelligence for electrocardiography-based diagnosis

Chung¹,

Lee²,

King³

et al. 2022

Int J Arrhythm

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Cardiovascular diseases are one of the leading global causes of mortality. Currently, clinicians rely on their own analyses or automated analyses of the electrocardiogram (ECG) to obtain a diagnosis. However, both approaches can only include a finite number of predictors and are unable to execute complex analyses. Artificial intelligence (AI) has enabled the introduction of machine and deep learning algorithms to compensate for the existing limitations of current ECG analysis methods, with promising results. However, it should be prudent to recognize that these algorithms also associated with their own unique set of challenges and limitations, such as professional liability, systematic bias, surveillance, cybersecurity, as well as technical and logistical challenges. This review aims to increase familiarity with and awareness of AI algorithms used in ECG diagnosis, and to ultimately inform the interested stakeholders on their potential utility in addressing present clinical challenges.

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“…Note that the AI-ECG AF prediction model output indicates the probability of undetected paroxysmal AF and not the future risk of AF, though previous studies have confirmed that a higher AI-ECG AF prediction model output associated with a higher risk of future AF. 8,9 The strength of our cross-sectional study is that we included many patients with MwA and MwoA, which allowed us to directly compare the AF prediction model output in different age and sex groups. The limitations include the use of International Classification of Disease codes to extract the information on MwA, MwoA, and vascular comorbidities.…”

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confidence: 99%

“…When we stratified the comparison with age and sex, we found that the AI‐ECG AF prediction model output was significantly higher in MwA compared to MwoA, even after adjustment for age and vascular risk factors, in women of all ages, women 35–55 years old, men of all ages, men 18–35 years old, women and men of all ages, and women and men 18–35 and 35–55 years old. Note that the AI‐ECG AF prediction model output indicates the probability of undetected paroxysmal AF and not the future risk of AF, though previous studies have confirmed that a higher AI‐ECG AF prediction model output associated with a higher risk of future AF 8,9 …”

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confidence: 99%

Exploring the association between migraine and atrial fibrillation utilizing a novel artificial intelligence‐ECG algorithm

2022

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Artificial Intelligence-Enabled ECG to Identify Silent Atrial Fibrillation in Embolic Stroke of Unknown Source

Cited by 20 publications

References 25 publications

Migraine with aura associates with a higher artificial intelligence: ECG atrial fibrillation prediction model output compared to migraine without aura in both women and men

Migraine with aura associates with a higher artificial intelligence: ECG atrial fibrillation prediction model output compared to migraine without aura in both women and men

Clinical significance, challenges and limitations in using artificial intelligence for electrocardiography-based diagnosis

Exploring the association between migraine and atrial fibrillation utilizing a novel artificial intelligence‐ECG algorithm

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