What question did this study set out to answer?

This research aims to evaluate the association between ECG-estimated cardiorespiratory fitness and pregnancy-related cardiovascular outcomes.

April 28, 2026Open Access

Prediction of Pregnancy-Related Cardiovascular Outcomes Using Electrocardiogram-Based Deep Learning Estimation of Cardiorespiratory Fitness

Key Result

Lower ECG-estimated cardiorespiratory fitness was associated with a higher risk of pregnancy-related cardiovascular complications.

Key Points

This research aims to evaluate the association between ECG-estimated cardiorespiratory fitness and pregnancy-related cardiovascular outcomes.
Utilized electrocardiogram-based deep learning algorithms for fitness estimation
Analyzed cardiovascular complications during pregnancy
Employed risk stratification techniques using AI
Lower ECG-estimated cardiorespiratory fitness linked to increased risk of cardiovascular complications
Supported AI-enabled ECG analysis as a potential tool for risk assessment in pregnancy

Structured PICO

Does electrocardiogram-based deep learning estimation of cardiorespiratory fitness predict pregnancy-related cardiovascular complications?

Population

Pregnant women undergoing antepartum risk stratification

Intervention

Electrocardiogram-based deep learning estimation of cardiorespiratory fitness

Outcome

Pregnancy-related cardiovascular complications

Artificial intelligence-enabled ECG analysis of cardiorespiratory fitness may serve as a scalable tool for antepartum risk stratification of pregnancy-related cardiovascular complications.

Main Result

Absolute Event Rate: 0% vs 0%

Abstract

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.