Table 2:

Test characteristics for AI-ECG for the detection of left ventricular systolic dysfunction.

	EF (%)	N	AUC	Sensitivity	Specificity
Total population, n = 2041	≤ 35	20 (1.0%)	0.981	100.0	93.5
	≤ 40	40 (2.0%)	0.971	90.0	91.9
	≤ 50	123 (6.0%)	0.880	76.4	87.9
High-risk, n = 1391	≤ 35	19 (1.4%)	0.974	100.0	91.9
	≤ 40	37 (2.7%)	0.970	91.9	92.9
	≤ 50	109 (7.8%)	0.884	78.9	86.6
Men, n = 983	≤ 35	16 (1.6%)	0.967	100.0	91.2
	≤ 40	32 (3.3%)	0.962	90.6	92.4
	≤ 50	91 (9.3%)	0.878	73.6	92.7
Women, n = 1058	≤ 35	4 (0.4%)	0.996	100.0	99.4
	≤ 40	8 (0.8%)	0.980	87.5	90.7
	≤ 50	32 (3.0%)	0.866	75.0	87.0
Age >65 years, n = 817	≤ 35	13 (1.6%)	0.976	100.0	89.4
	≤ 40	27 (3.3%)	0.969	92.6	87.2
	≤ 50	74 (9.1%)	0.907	87.8	81.7
High risk, Age >65 years, n = 601	≤ 35	12 (2.0%)	0.971	100.0	87.6
	≤ 40	26 (4.3%)	0.962	92.3	89.4
	≤ 50	70 (11.6%)	0.897	77.1	89.5

^aAbbreviations: AI-ECG = artificial intelligence-augmented electrocardiogram, AUC = area under the curve, EF = ejection fraction;