Artificial Intelligence-Enhanced Electrocardiography Identifies Patients With Normal Ejection Fraction at Risk of Worse Outcomes.

Autor:	Naser JA; Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA., Lee E; Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA., Lopez-Jimenez F; Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA., Noseworthy PA; Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA., Latif OS; Department of Internal Medicine, Mayo Clinic, Rochester, Minnesota, USA., Friedman PA; Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA., Lin G; Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA., Oh JK; Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA., Scott CG; Department of Quantitative Health and Biostatistics, Mayo Clinic, Rochester, Minnesota, USA., Pislaru SV; Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA., Attia ZI; Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA., Pellikka PA; Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA.
Jazyk:	angličtina
Zdroj:	JACC. Advances [JACC Adv] 2024 Aug 28; Vol. 3 (9), pp. 101179. Date of Electronic Publication: 2024 Aug 28 (Print Publication: 2024).
DOI:	10.1016/j.jacadv.2024.101179
Abstrakt:	Background: An artificial intelligence (AI)-based electrocardiogram (ECG) model identifies patients with a higher likelihood of low ejection fraction (EF). Patients with an abnormal AI-ECG score but normal EF (false positives; FP) more often developed future low EF. Objective: The purpose of this study was to evaluate echocardiographic characteristics and all-cause mortality risk in FP patients. Methods: Patients with transthoracic echocardiography and ECG were classified retrospectively into FP, true negatives (TN) (EF ≥50%, normal AI-ECG), true positives (TP) (EF <50%, abnormal AI-ECG), or false negatives (FN) (EF <50%, normal AI-ECG). Echocardiographic abnormalities included systolic and diastolic left ventricular function, valve disease, estimated pulmonary pressures, and right heart parameters. Cox regression was used to assess factors associated with all-cause mortality. Results: Of 100,586 patients (median age 63 years; 45.5% females), 79% were TN, 7% FP, 5% FN, and 8% TP. FPs had more echocardiographic abnormalities than TN but less than FN or TP patients. An echocardiographic abnormality was present in 97% of FPs. Over median 2.7 years, FPs had increased mortality risk (age and sex-adjusted HR: 1.64 [95% CI: 1.55-1.73]) vs TN. Age and sex-adjusted mortality was higher in FP with abnormal echocardiography than FP with normal echocardiography and to TN regardless of echocardiography result; FP with normal echocardiography had comparable mortality risk to TN with abnormal echocardiography. Conclusions: FP patients were more likely than TNs to have echocardiographic abnormalities with 97% of exams showing an abnormality. FP patients had higher mortality rates, especially when their echocardiograms also had an abnormality; the concomitant use of AI ECG and echocardiography helps in stratifying risk in patients with normal LVEF. Competing Interests: Dr Pellikka is supported as the Betty Knight Scripps-George M. Gura, Jr, MD, Professor of Cardiovascular Diseases Clinical Research, Mayo Foundation. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose. (© 2024 The Authors.)
Databáze:	MEDLINE
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