Artificial intelligence for hemodynamic monitoring with a wearable electrocardiogram monitor.

Summary

A deep neural network trained on single-lead ECG identified elevated left atrial pressure with AUC 0.80 (internal), 0.76 (external), and 0.875 in a prospective patch-ECG cohort near right heart catheterization. This demonstrates feasibility of ambulatory, noninvasive hemodynamic monitoring using wearable ECG data.

Key Findings

• Internal validation AUC for elevated left atrial pressure detection was 0.80; external validation AUC was 0.76.
• Prospective patch-ECG cohort near right heart catheterization achieved AUC 0.875.
• Model leverages single-lead wearable ECG, supporting ambulatory hemodynamic monitoring.

Clinical Implications

This approach could enable remote detection of hemodynamic congestion, guide diuretic titration, and trigger earlier interventions in heart failure management using widely available wearable ECG patches.

Limitations

• Prospective cohort size not specified in the abstract and likely small
• Model interpretability and generalizability to home settings and varied comorbidities require further study

Future Directions

Larger prospective, multi-center studies with longitudinal monitoring to test clinical utility for decompensation prediction and to assess workflow integration and outcome impact.