JACC. Advances · 2026
Prediction of Pregnancy-Related Cardiovascular Outcomes Using Electrocardiogram-Based Deep Learning Estimation of Cardiorespiratory Fitness.
СтатьяНазвание и аннотация: Перевод в очереди
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Brown Logan, Zhao Yunong, D'Souza Valentina, Pace Danielle, James Kaitlyn, Powe Camille, Churchill Timothy, Economy Katherine E, Maddah Mahnaz, Ellinor Patrick T, Ho Jennifer E, Khurshid Shaan, Guseh James Sawalla, Lau Emily S
Аннотация
BACKGROUND: Peak oxygen consumption (peak VO2), the gold standard measure of cardiorespiratory fitness, may identify women at high risk for pregnancy-related cardiovascular (CV) complications, but ascertainment is not widely scalable. We previously developed and validated a deep learning model to estimate peak VO2 from the resting 12-lead electrocardiogram (ECG).
OBJECTIVES: The purpose of this study was to examine the association of deep learning ECG-predicted peak VO2 with incident pregnancy-related CV complications.
METHODS: We evaluated ECG-estimated peak VO2 among individuals with a clinical 12-lead ECG from 1 year before pregnancy to 13 weeks of gestation in a multi-institutional electronic health record pregnancy cohort. Multivariable-adjusted mixed-effects logistic regression models examined associations between ECG-estimated peak VO2 with pregnancy-related CV complications up to 1 year postpartum (severe hypertensive disorders of pregnancy, major adverse cardiac events, and maternal death).
RESULTS: Among 3,650 pregnancies from 3,437 women (mean age at delivery 33 ± 6 years), median ECG-estimated VO2 was 27.0 mL/kg/min (IQR: 21.9-31.2), and 723 (20%; 95% CI: 19%-21%) experienced a pregnancy-related CV complication over a median follow-up time of 1.7 years (IQR: 1.6-1.8 years). Lower ECG-estimated peak VO2 was associated with a higher complication risk (adjusted OR: 1.09 per 1-metabolic equivalent lower fitness; 95% CI: 1.03-1.17; P < 0.01). Women in the lowest quartile of ECG-estimated peak VO2 had 61% greater odds of CV complications than the highest quartile (OR: 1.61; 95% CI: 1.13-2.30; P = 0.008).
CONCLUSIONS: Lower ECG-estimated cardiorespiratory fitness was associated with a higher risk of pregnancy-related CV complications, supporting artificial intelligence-enabled ECG analysis as a scalable tool for antepartum risk stratification of pregnancy-related CV complications.
OBJECTIVES: The purpose of this study was to examine the association of deep learning ECG-predicted peak VO2 with incident pregnancy-related CV complications.
METHODS: We evaluated ECG-estimated peak VO2 among individuals with a clinical 12-lead ECG from 1 year before pregnancy to 13 weeks of gestation in a multi-institutional electronic health record pregnancy cohort. Multivariable-adjusted mixed-effects logistic regression models examined associations between ECG-estimated peak VO2 with pregnancy-related CV complications up to 1 year postpartum (severe hypertensive disorders of pregnancy, major adverse cardiac events, and maternal death).
RESULTS: Among 3,650 pregnancies from 3,437 women (mean age at delivery 33 ± 6 years), median ECG-estimated VO2 was 27.0 mL/kg/min (IQR: 21.9-31.2), and 723 (20%; 95% CI: 19%-21%) experienced a pregnancy-related CV complication over a median follow-up time of 1.7 years (IQR: 1.6-1.8 years). Lower ECG-estimated peak VO2 was associated with a higher complication risk (adjusted OR: 1.09 per 1-metabolic equivalent lower fitness; 95% CI: 1.03-1.17; P < 0.01). Women in the lowest quartile of ECG-estimated peak VO2 had 61% greater odds of CV complications than the highest quartile (OR: 1.61; 95% CI: 1.13-2.30; P = 0.008).
CONCLUSIONS: Lower ECG-estimated cardiorespiratory fitness was associated with a higher risk of pregnancy-related CV complications, supporting artificial intelligence-enabled ECG analysis as a scalable tool for antepartum risk stratification of pregnancy-related CV complications.