Deep learning prediction of nocturnal hypertension for patients intolerant to ambulatory blood pressure monitoring

BACKGROUND: Ambulatory blood pressure monitoring (ABPM) plays an irreplaceable role in the diagnosis and management of hypertension. However, more than 100 million of the 1.4 billion people with hypertension worldwide cannot tolerate nighttime ABPM due to noise and arm compression. Previous prediction methods relying on demographic factors and home blood pressure measurements are time-consuming and burdensome while exhibiting limited accuracy for nocturnal hypertension. There is a need for a more accurate, low-burden approach to identify high-risk patients intolerant to nighttime ABPM monitoring. METHODS: We collected 2,874 ABPM records at a regional medical center to conduct a retrospective cohort study. Kernel density estimation based preprocessing was applied to stabilize data fluctuations. A variational autoencoder based deep learning model was developed using daytime blood pressure and heart rate combined with full-day activity and posture states to predict nocturnal hypertension. RESULTS: Here we show that the ABPM-VAE model achieves an AUC of 0.82 (95% CI 0.77-0.88) on the test set, outperforming the ablation model (AUC 0.67; 95% CI 0.61-0.74; p < 0.001) and prior methods based on demographic and home blood pressure data (AUC 0.69). For nocturnal hypertension prediction, the model yields a PPV of 92.12%, NPV of 55.20%, sensitivity of 0.73, and specificity of 0.84. CONCLUSIONS: The entropy reduction preprocessing-enhanced deep learning model predicts nocturnal hypertension risk from ABPM without adding burden to patients or physicians. It serves as an effective screening tool to identify high-risk individuals intolerant to nighttime monitoring, serving as a valuable complement to conventional ABPM.