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• W4308590003 abstract "Screening for left ventricular systolic dysfunction (LVSD), defined as reduced left ventricular ejection fraction (LVEF), deserves renewed interest as the medical treatment for the prevention and progression of heart failure improves. We aimed to review the updated literature to outline the potential and caveats of using artificial intelligence–enabled electrocardiography (AIeECG) as an opportunistic screening tool for LVSD. We searched PubMed and Cochrane for variations of the terms “ECG,” “Heart Failure,” “systolic dysfunction,” and “Artificial Intelligence” from January 2010 to April 2022 and selected studies that reported the diagnostic accuracy and confounders of using AIeECG to detect LVSD. Out of 40 articles, we identified 15 relevant studies; eleven retrospective cohorts, three prospective cohorts, and one case series. Although various LVEF thresholds were used, AIeECG detected LVSD with a median AUC of 0.90 (IQR from 0.85 to 0.95), a sensitivity of 83.3% (IQR from 73 to 86.9%) and a specificity of 87% (IQR from 84.5 to 90.9%). AIeECG algorithms succeeded across a wide range of sex, age, and comorbidity and seemed especially useful in non-cardiology settings and when combined with natriuretic peptide testing. Furthermore, a false-positive AIeECG indicated a future development of LVSD. No studies investigated the effect on treatment or patient outcomes. This systematic review corroborates the arrival of a new generic biomarker, AIeECG, to improve the detection of LVSD. AIeECG, in addition to natriuretic peptides and echocardiograms, will improve screening for LVSD, but prospective randomized implementation trials with added therapy are needed to show cost-effectiveness and clinical significance." @default.
• W4308590003 created "2022-11-12" @default.
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• W4308590003 date "2022-11-08" @default.
• W4308590003 modified "2023-09-26" @default.
• W4308590003 title "Artificial intelligence enabled ECG screening for left ventricular systolic dysfunction: a systematic review" @default.
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• W4308590003 doi "https://doi.org/10.1007/s10741-022-10283-1" @default.
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