Screening for Atrial Fibrillation—More Data Still Needed

Greenland, Philip

doi:10.1001/jama.2021.23727

Cited by 5 publications

(3 citation statements)

References 9 publications

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“…Although ECG-based screening for AF is currently a topic of great clinical interest 31,32 it would not be a proper comparison for this study because ECG is primarily used for the detection of prevalent AF not for prediction of future AF. However, recent studies suggest that AI-enabled ECG could play a role in predicting future AF 33,34,35 .A study by Christopoulos et al that compared the performance of AI-enabled ECG with CHARGE-AF Risk Score, there was no significant difference between the cumulative incidence of AF in the top quartile of the two methods 34 whereas in our study the top percentiles of AI-estimated LA volume detected a significantly higher percentage of AF versus CHARGE-AF.…”

Section: Discussionmentioning

confidence: 99%

AI-enabled Left Atrial Volumetry in Cardiac CT Scans Improves CHARGE-AF and Outperforms NT-ProBNP for Prediction of Atrial Fibrillation in Asymptomatic Individuals: Multi-Ethnic Study of Atherosclerosis

Naghavi,

Yankelevitz,

Reeves

et al. 2024

Preprint

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Background: Coronary artery calcium (CAC) scans contain actionable information beyond CAC scores that is not currently reported. Methods: We have applied artificial intelligence-enabled automated cardiac chambers volumetry to CAC scans (AI-CAC) of 5535 asymptomatic individuals (52.2% women, ages 45-84) that were previously obtained for CAC scoring in the baseline examination (2000-2002) of the Multi-Ethnic Study of Atherosclerosis (MESA). We then used the 5-year outcomes data and compared the C-statistic of the AI-CAC LA volume with known predictors of atrial fibrillation (AF), the CHARGE-AF Risk Score and NT-proBNP (BNP) for prediction of incident AF. The AI-CAC automated cardiac chambers volumetry took on average 21 seconds per CAC scan. Results: At 1,2,3,4, and 5 years follow up 36, 77, 123, 182, and 236 cases of AF were identified respectively. The C-statistic AUC for AI-CAC LA volume was significantly higher than CHARGE-AF or BNP at year 1 (0.836, 0.742, 0.742), year 2 (0.842, 0.807,0.772), and year 3 (0.811, 0.785, 0.745) (p<0.02). For year 4 (0.785, 0.769, 0.725) and year 5 (0.781, 0.767, 0.734) respectively (p>0.05). AI-CAC LA volume significantly improved the continuous Net Reclassification Index for prediction of AF at year 1-5 when added to CAC score (0.74, 0.49, 0.53, 0.39, 0.44), CHARGE-AF Risk Score (0.60, 0.28, 0.32, 0.19, 0.24), and BNP (0.68, 0.44, 0.42, 0.30, 0.37) respectively (p<0.01). Conclusion: AI-CAC LA volume enabled prediction of AF as early as one year and significantly improved on risk classification of CHARGE-AF Risk Score and BNP.

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Section: Discussionmentioning

confidence: 99%

AI-enabled Left Atrial Volumetry in Cardiac CT Scans Improves CHARGE-AF and Outperforms NT-ProBNP for Prediction of Atrial Fibrillation in Asymptomatic Individuals: Multi-Ethnic Study of Atherosclerosis

Naghavi,

Yankelevitz,

Reeves

et al. 2024

Preprint

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“…AF is the most common sustained cardiac arrhythmia with an increased risk of cardiovascular disease outcomes including a five-fold higher risk of stroke 25 . However, it often goes undiagnosed in approximately 30% of cases due to its paroxysmal nature and minimal or even absent symptoms 26 .…”

Section: Discussionmentioning

confidence: 99%

Coronary Artery Calcium Scans Powered by Artificial Intelligence Predicts Atrial Fibrillation Comparably to Cardiac Magnetic Resonance Imaging: The Multi-Ethnic Study of Atherosclerosis (MESA)

Naghavi,

Reeves,

Atlas

et al. 2024

Preprint

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BackgroundApplying artificial intelligence to coronary artery calcium computed tomography scan (AI-CAC) provides more actionable information beyond the Agatston coronary artery calcium (CAC) score. We have recently shown that AI-CAC automated left atrial (LA) volumetry enabled prediction of atrial fibrillation (AF) in as early as one year. In this study we evaluated the performance of AI-CAC automated LA volumetry versus LA volume measured by human experts using cardiac magnetic resonance imaging (CMRI) for predicting AF, and compared them with CHARGE-AF risk score, Agatston score, and NT-proBNP (BNP).MethodsWe used 15-year outcome data from 3552 asymptomatic individuals (52.2% women, ages 45-84 years) who underwent both CAC scans and CMRI in the baseline examination (2000-2002) of the Multi-Ethnic Study of Atherosclerosis (MESA). AI-CAC took on average 21 seconds per scan. CMRI LA volume was previously measured by human experts. Data on BNP, CHARGE-AF risk score and the Agatston score were obtained from MESA.ResultsOver 15 years follow-up, 562 cases of AF accrued. The ROC AUC for AI-CAC versus CMRI and CHARGE-AF were not significantly different (AUC 0.807, 0.808, 0.800 respectively, p=0.60). The AUC for BNP (0.707) and Agatston score (0.694) were significantly lower than the rest (p<.0001). AI-CAC and CMRI significantly improved the continuous Net Reclassification Index (NRI) for prediction of AF when added to CHARGE-AF risk score (0.28, 0.31), BNP (0.43, 0.32), and Agatston score (0.69, 0.41) respectively (p for all<0.0001).ConclusionAI-CAC automated LA volumetry and CMRI LA volume measured by human experts similarly predicted incident AF over 15 years.

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“…The 2022 U.S. Preventive Services Task Force Recommendations for Screening for Atrial Fibrillation point out that digital health technologies have led to even greater detection of intermittent AF of short duration, which has unclear treatment benefits given the potential risks of oral anticoagulation in older adults. 11 , 12 These important questions were not addressed in VITAL-AF or the follow-up study from Khurshid et al. 1 Importantly, Khurshid et al 1 reported that the proportion of equivocal results increased with age, and approximately one-quarter of tracings were equivocal for adults aged 80 and older.…”

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confidence: 99%