Abstract
BACKGROUND: Identification of left ventricular (LV) dysfunction following echocardiographic investigations remains problematic, particularly when the ejection fraction (EF) is preserved. OBJECTIVES: The authors examined the operational characteristics of artificial intelligence LV dysfunction (AI-LVD) identification from routinely obtained echocardiographic measurements. METHODS: Following initial training in 126,136 (imputation cohort) and 254,735 (training cohort) cases from the National Echo Database of Australia, the AI-LVD was tested in 81,509 cases (last echo January 1, 2000-May 21, 2019) with no mitral valve intervention or pacemaker. This cohort comprised 41,796 men (51.3%) aged 62.3 ± 17.1 years and 39,713 women aged 63.2 ± 18.4 years, in whom 4,490 (5.5%), 3,734 (4.6%), and 59,297 (72.7%) had reduced, mildly reduced, and preserved EF, while 13,988 (17.2%) had no recorded EF and 39,940 (45.2%) had "indeterminate" filling pressures. RESULTS: Overall, the AI-LVD generated a (sex-specific) output in decile distributions consistent with increasingly higher levels of LV dysfunction and mortality-actual 5-year mortality rising from 5.7% to 66.3% and 2.3% to 64.2% in men and women, respectively. The prognostic capacity of the AI-LVD persisted in preserved EF, when adjusting for age, year of echo, and missing echo parameters-with adjusted hazard for all-cause mortality during 1,541 (812-2,682) days follow-up 4.93-fold (95% CI: 4.35-5.59) and 7.11-fold (95% CI: 5.85-8.64) higher in the highest vs lowest decile group in men and women, respectively. CONCLUSIONS: A new AI-LVD algorithm using only echocardiographic measurements can reliably identify prognostically important LV dysfunction, including in preserved EF, even when key reporting parameters are missing. The AI-LVD can be used in real-time during routine echocardiography reporting.