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AI and Wearables May Predict Hypertensive Crisis Up to 3 Days Early

By FisherVista•
Preliminary research presented at the American Heart Association's Scientific Sessions 2026 suggests that AI analysis of data from a portable blood pressure monitor and wrist-worn device can identify elevated risk of a hypertensive crisis up to 72 hours in advance, potentially enabling earlier intervention.
AI and Wearables May Predict Hypertensive Crisis Up to 3 Days Early

Using artificial intelligence to analyze patterns from a portable cuff-based blood pressure monitor and a wrist-worn fitness tracker may help identify adults with stage 2 high blood pressure who are at increased risk of a hypertensive crisis up to three days before it occurs, according to preliminary research presented at the American Heart Association’s Scientific Sessions 2026 in Chicago. The findings, which have not yet been peer-reviewed, suggest that continuous monitoring combined with AI could give patients and clinicians critical lead time to prevent dangerous spikes.

A hypertensive crisis is defined as blood pressure rising above 180/120 mm Hg with acute end-organ damage, a medical emergency that can lead to stroke, heart attack, kidney damage, aortic dissection or pulmonary edema. According to the American Heart Association/American College of Cardiology joint 2025 High Blood Pressure Guideline, nearly half of U.S. adults have high blood pressure, making it the number one modifiable risk factor for heart attack, stroke and heart failure.

“Everyone’s blood pressure changes throughout the day and night, and a single reading only captures one moment in time,” said Waleed Razzaq Chaudhry, M.D., lead study author and an internal medicine resident at the University of Maryland Capital Region Medical Center. “By continuously tracking blood pressure and identifying patterns, we found that AI analysis of blood pressure may be a valuable addition to help identify early warning signs of dangerously high blood pressure.”

The study followed 4,190 adults ages 40 to 85 with stage 2 hypertension at one hospital in Sindh, Pakistan, from January 2025 to January 2026. Participants wore a portable cuff-based blood pressure monitor and a wrist-worn device with light-based sensors. An AI tool analyzed 47 characteristics, including blood pressure changes during sleep and after waking, as well as heart rate variability. Over 12 months, 312 participants experienced 418 hypertensive crises. The AI tool distinguished higher versus lower risk up to 72 hours in advance. Among participants with a positive alert, about 32% experienced a crisis, while 97.4% of those without an alert remained crisis-free. Abnormal blood pressure patterns during sleep and a sharp post-waking rise of more than 35 mm Hg were among the strongest predictors. When physicians received an alert and adjusted medications within 24 hours, hypertensive crises were about 61% lower and emergency department visits about 45% lower.

“These findings suggest that continuous blood pressure monitoring can help detect these blood pressure changes earlier, which may give patients and healthcare professionals more time to act before it turns into an emergency,” Chaudhry said. Maria Delgado-Lelievre, M.D., a hypertension specialist at the University of Miami Leonard M. Miller School of Medicine, noted that such tools “may help support more personalized care and encourage earlier discussions about treatment, healthy lifestyle changes and medication adherence.”

The research is preliminary and has limitations, including that all participants were from one hospital in Pakistan. A 2025 American Heart Association scientific statement on cuffless devices emphasized that more research is needed before such technologies guide patient care. The Association recommends an automatic, cuff-style, upper arm monitor and provides a list of validated blood pressure devices at validatebp.org. For those monitoring at home, the Association offers guidance on Home Blood Pressure Monitoring and information on the Health Threats from High Blood Pressure. The full abstract is available online.

FisherVista

FisherVista

@fishervista