BACKGROUND: An artificial intelligence (AI) algorithm applied to electrocardiography during sinus rhythm has recently been shown to detect concurrent episodic atrial fibrillation (AF). We sought to characterize the value of AI-enabled electrocardiography (AI-ECG) as a predictor of future AF and assess its performance compared with the CHARGE-AF score (Cohorts for Aging and Research in Genomic Epidemiology-AF) in a population-based sample. METHODS: We calculated the probability of AF using AI-ECG, among participants in the population-based Mayo Clinic Study of Aging who had no history of AF at the time of the baseline study visit. Cox proportional hazards models were fit to assess the independent prognostic value and interaction between AI-ECG AF model output and CHARGE-AF score. C statistics were calculated for AI-ECG AF model output, CHARGE-AF score, and combined AI-ECG and CHARGE-AF score. RESULTS: A total of 1936 participants with median age 75.8 (interquartile range, 70.4-81.8) years and median CHARGE-AF score 14.0 (IQR, 13.2-14.7) were included in the analysis. Participants with AI-ECG AF model output of >0.5 at the baseline visit had cumulative incidence of AF 21.5% at 2 years and 52.2% at 10 years. When included in the same model, both AI-ECG AF model output (hazard ratio, 1.76 per SD after logit transformation [95% CI, 1.51-2.04]) and CHARGE-AF score (hazard ratio, 1.90 per SD [95% CI, 1.58-2.28]) independently predicted future AF without significant interaction (P=0.54). C statistics were 0.69 (95% CI, 0.66-0.72) for AI-ECG AF model output, 0.69 (95% CI, 0.66-0.71) for CHARGE-AF, and 0.72 (95% CI, 0.69-0.75) for combined AI-ECG and CHARGE-AF score. CONCLUSIONS: In the present study, both the AI-ECG AF model output and CHARGE-AF score independently predicted incident AF. The AI-ECG may offer a means to assess risk with a single test and without requiring manual or automated clinical data abstraction.
BACKGROUND: An artificial intelligence (AI) algorithm applied to electrocardiography during sinus rhythm has recently been shown to detect concurrent episodic atrial fibrillation (AF). We sought to characterize the value of AI-enabled electrocardiography (AI-ECG) as a predictor of future AF and assess its performance compared with the CHARGE-AF score (Cohorts for Aging and Research in Genomic Epidemiology-AF) in a population-based sample. METHODS: We calculated the probability of AF using AI-ECG, among participants in the population-based Mayo Clinic Study of Aging who had no history of AF at the time of the baseline study visit. Cox proportional hazards models were fit to assess the independent prognostic value and interaction between AI-ECG AF model output and CHARGE-AF score. C statistics were calculated for AI-ECG AF model output, CHARGE-AF score, and combined AI-ECG and CHARGE-AF score. RESULTS: A total of 1936 participants with median age 75.8 (interquartile range, 70.4-81.8) years and median CHARGE-AF score 14.0 (IQR, 13.2-14.7) were included in the analysis. Participants with AI-ECG AF model output of >0.5 at the baseline visit had cumulative incidence of AF 21.5% at 2 years and 52.2% at 10 years. When included in the same model, both AI-ECG AF model output (hazard ratio, 1.76 per SD after logit transformation [95% CI, 1.51-2.04]) and CHARGE-AF score (hazard ratio, 1.90 per SD [95% CI, 1.58-2.28]) independently predicted future AF without significant interaction (P=0.54). C statistics were 0.69 (95% CI, 0.66-0.72) for AI-ECG AF model output, 0.69 (95% CI, 0.66-0.71) for CHARGE-AF, and 0.72 (95% CI, 0.69-0.75) for combined AI-ECG and CHARGE-AF score. CONCLUSIONS: In the present study, both the AI-ECG AF model output and CHARGE-AF score independently predicted incident AF. The AI-ECG may offer a means to assess risk with a single test and without requiring manual or automated clinical data abstraction.
Authors: Renate B Schnabel; Thor Aspelund; Guo Li; Lisa M Sullivan; Astrid Suchy-Dicey; Tamara B Harris; Michael J Pencina; Ralph B D'Agostino; Daniel Levy; William B Kannel; Thomas J Wang; Richard A Kronmal; Philip A Wolf; Gregory L Burke; Lenore J Launer; Ramachandran S Vasan; Bruce M Psaty; Emelia J Benjamin; Vilmundur Gudnason; Susan R Heckbert Journal: Arch Intern Med Date: 2010-11-22
Authors: Jennifer L St Sauver; Brandon R Grossardt; Barbara P Yawn; L Joseph Melton; Joshua J Pankratz; Scott M Brue; Walter A Rocca Journal: Int J Epidemiol Date: 2012-11-18 Impact factor: 7.196
Authors: Jonas Bille Nielsen; Adrian Pietersen; Claus Graff; Bent Lind; Johannes Jan Struijk; Morten Salling Olesen; Stig Haunsø; Thomas Aalexander Gerds; Patrick Thomas Ellinor; Lars Køber; Jesper Hastrup Svendsen; Anders Gaarsdal Holst Journal: Heart Rhythm Date: 2013-04-19 Impact factor: 6.343
Authors: David M German; Muammar M Kabir; Thomas A Dewland; Charles A Henrikson; Larisa G Tereshchenko Journal: Ann Noninvasive Electrocardiol Date: 2015-11-02 Impact factor: 1.468
Authors: Rosebud O Roberts; Yonas E Geda; David S Knopman; Ruth H Cha; V Shane Pankratz; Bradley F Boeve; Robert J Ivnik; Eric G Tangalos; Ronald C Petersen; Walter A Rocca Journal: Neuroepidemiology Date: 2008-02-07 Impact factor: 3.282
Authors: Renate B Schnabel; Xiaoyan Yin; Philimon Gona; Martin G Larson; Alexa S Beiser; David D McManus; Christopher Newton-Cheh; Steven A Lubitz; Jared W Magnani; Patrick T Ellinor; Sudha Seshadri; Philip A Wolf; Ramachandran S Vasan; Emelia J Benjamin; Daniel Levy Journal: Lancet Date: 2015-05-07 Impact factor: 79.321
Authors: Albert Hofman; Monique M B Breteler; Cornelia M van Duijn; Harry L A Janssen; Gabriel P Krestin; Ernst J Kuipers; Bruno H Ch Stricker; Henning Tiemeier; André G Uitterlinden; Johannes R Vingerling; Jacqueline C M Witteman Journal: Eur J Epidemiol Date: 2009 Impact factor: 8.082
Authors: Ingrid E Christophersen; Xiaoyan Yin; Martin G Larson; Steven A Lubitz; Jared W Magnani; David D McManus; Patrick T Ellinor; Emelia J Benjamin Journal: Am Heart J Date: 2016-05-17 Impact factor: 4.749
Authors: Shaan Khurshid; Samuel Friedman; Christopher Reeder; Paolo Di Achille; Nathaniel Diamant; Pulkit Singh; Lia X Harrington; Xin Wang; Mostafa A Al-Alusi; Gopal Sarma; Andrea S Foulkes; Patrick T Ellinor; Christopher D Anderson; Jennifer E Ho; Anthony A Philippakis; Puneet Batra; Steven A Lubitz Journal: Circulation Date: 2021-11-08 Impact factor: 29.690
Authors: Akhil Vaid; Kipp W Johnson; Marcus A Badgeley; Sulaiman S Somani; Mesude Bicak; Isotta Landi; Adam Russak; Shan Zhao; Matthew A Levin; Robert S Freeman; Alexander W Charney; Atul Kukar; Bette Kim; Tatyana Danilov; Stamatios Lerakis; Edgar Argulian; Jagat Narula; Girish N Nadkarni; Benjamin S Glicksberg Journal: JACC Cardiovasc Imaging Date: 2021-10-13
Authors: Daniel Sierra-Lara Martinez; Peter A Noseworthy; Oguz Akbilgic; Joerg Herrmann; Kathryn J Ruddy; Abdulaziz Hamid; Ragasnehith Maddula; Ashima Singh; Robert Davis; Fatma Gunturkun; John L Jefferies; Sherry-Ann Brown Journal: Am Heart J Plus Date: 2022-04-01
Authors: Erika L Weil; Peter A Noseworthy; Camden L Lopez; Alejandro A Rabinstein; Paul A Friedman; Zachi I Attia; Xiaoxi Yao; Konstantinos C Siontis; Walter K Kremers; Georgios Christopoulos; Michelle M Mielke; Prashanthi Vemuri; Clifford R Jack; Bernard J Gersh; Mary M Machulda; David S Knopman; Ronald C Petersen; Jonathan Graff-Radford Journal: Mayo Clin Proc Date: 2022-05 Impact factor: 11.104
Authors: Arun R Sridhar; Zih-Hua Chen Amber; Jacob J Mayfield; Alison E Fohner; Panagiotis Arvanitis; Sarah Atkinson; Frieder Braunschweig; Neal A Chatterjee; Alessio Falasca Zamponi; Gregory Johnson; Sanika A Joshi; Mats C H Lassen; Jeanne E Poole; Christopher Rumer; Kristoffer G Skaarup; Tor Biering-Sørensen; Carina Blomstrom-Lundqvist; Cecilia M Linde; Mary M Maleckar; Patrick M Boyle Journal: Cardiovasc Digit Health J Date: 2021-12-31