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. 2021 Mar 17;2(2):109–117. doi: 10.1016/j.cvdhj.2021.03.001

Table 2.

Associations between deep learning segmentation–derived left ventricular mass index and prevalent disease

N events Odds ratio with covariate (95% CI)
LVMI (per 1 SD) LVH LVH (90th percentile)
Hypertension
InlineVF 11,271 1.43 (1.39–1.47) 2.30 (2.15–2.46) 2.33 (2.17–2.50)
Regression 11,271 1.27 (1.24–1.30) 1.67 (1.38–2.01) 1.64 (1.53–1.76)
Segmentation 11,271 1.55 (1.51–1.59) 2.76 (2.51–3.04) 2.39 (2.23–2.57)
Atrial fibrillation
InlineVF 1053 0.99 (0.93–1.05) 1.19 (0.99–1.44) 1.27 (1.04–1.53)
Regression 1053 1.00 (0.93–1.07) 1.13 (0.59–1.93) 0.99 (0.80–1.21)
Segmentation 1053 1.13 (1.06–1.21) 1.75 (1.37–2.20) 1.61 (1.34–1.93)
Heart failure
InlineVF 241 1.45 (1.29–1.63) 2.92 (2.16–3.89) 3.02 (2.23–4.04)
Regression 241 1.39 (1.23–1.57) 3.94 (1.75–7.67) 2.36 (1.71–3.20)
Segmentation 241 1.71 (1.51–1.93) 4.67 (3.28–6.49) 3.73 (2.78–4.95)

LVH = left ventricular hypertrophy; LVMI = left ventricular mass index.

Total N = 37,261 with available phenotypic data and cardiac magnetic resonance–derived left ventricular mass estimates obtained using each method.