AI-ECG age prediction increased by 3.0 years during pregnancy (p<0.001), while sex discordance score decreased postpartum by 0.03 (p=0.028) in healthy women.
Does serial AI-ECG analysis detect changes in estimated biological age and sex discordance during healthy pregnancies?
Serial AI-ECG assessment demonstrates measurable changes in estimated biological sex and age during healthy pregnancies, suggesting its potential utility as a marker for pregnancy-induced biological stress.
Absolute Event Rate: 0% vs 0%
Abstract Background Artificial intelligence enhanced electrocardiograms (AI-ECG) have matured over the past 10 years. More recently, the AI-ECG estimate of biological sex has shown potential as a novel tool for cardiovascular risk stratification among women. Cardiovascular disease is a leading cause of pregnancy-related mortality; however, there have been limited approaches for estimating the physiological demands of pregnancy and its impact of maternal cardiovascular outcomes. Purpose To estimate pregnancy-related changes on AI-ECG-based sex and age estimation in a prospective cohort of otherwise healthy, complication free pregnant women. Methods This secondary analysis of the SPEC-AI Nigeria Clinical Trial identified pregnant women without any pregnancy-related complications (at baseline or during follow-up) who had at least two 12 lead ECGs recorded during the study. The ECGs were analyzed with previously-developed algorithms to predict age and biological sex. To address bias in the AI-ECG age estimate, a reference cohort of 25,144 healthy patients was analyzed using quantile regression to estimate the 75th percentile of the reference distribution as a function of age. The estimate of biological sex was used to determine a sex discordance score with a discordance of 0.2 considered clinically relevant. To simplify the trends in the data, the ECGs were categorized as during pregnancy (=28 days prior to birth), peripartum (+/- 28 days of birth), and postpartum (28+ days after birth), and mixed effects models with a random intercept were used for analysis. Results A total of 207 females with a median age of 31 years (IQR 28 to 36 years) and normal blood pressures (systolic: 110 (IQR 10 to 120); diastolic: 70 (IQR 60-77); mmHg) were included from 5 clinical sites in Nigeria. The majority (73%, 150/207) of participants were in their second trimester at time of enrollment and the median number of ECGs per participant was 3 (max 7). The sex discordance score was elevated in 38% of the women at baseline and this percentage fell to 23% postpartum; which resulted in a decrease in the mean discordance score of 0.03 (95% CI: -0.06 to 0.0; p=0.028). Conversely, the percentage of patients with elevated age prediction increased from 35% to 41% over the course of pregnancy. This resulted in an increase in the bias-corrected AI-ECG age of 3.0 years (95% CI: 1.5 to 4.6 years; p0.001). Conclusion This study demonstrates the preliminary utility of serial AI-ECG based assessment as a marker for the biological effects of pregnancy. The increase in sex discordance score during pregnancy may be related to changes in cardiac structure, such as increased ventricular mass and chamber dilation which often reverses postpartum. However, the impact on age estimation appeared to persist in the early postpartum period. Further study is warranted to associate any changes observed with long term maternal or fetal outcomes.Figure
Carter et al. (Sat,) reported a other. AI-ECG age prediction increased by 3.0 years during pregnancy (p<0.001), while sex discordance score decreased postpartum by 0.03 (p=0.028) in healthy women.