AI Wearable Monitoring Flags Hypertensive Crisis Risk 72 Hours Ahead in 4,000-Patient Cohort

Researchers followed participants for 12 months using a portable cuff device for multiple daily and nocturnal readings plus a wrist fitness tracker. The AI model incorporated 24-hour blood-pressure trajectories, nocturnal dipping status, and morning surge magnitude. Non-dipping carried nearly fivefold higher odds and post-awakening surges >35 mm Hg nearly fourfold higher odds of crisis. Among 312 patients who experienced 418 events, alerts triggered physician review and medication adjustment within 24 hours, yielding 61% fewer crises and 45% fewer emergency visits versus unalerted controls. The study design was observational and single-arm; no randomized comparison group received identical monitoring without AI alerts. Selection of higher-risk stage 2 patients and reliance on self-reported adherence limit causal inference. Nighttime non-dipping is a known prognostic marker from prior ambulatory monitoring cohorts, yet integration with real-time AI remains untested in multi-center RCTs. If replicated, the approach could shift hypertensive crisis management from reactive emergency care to preemptive titration, but false-positive rates, alert fatigue, and equity across device access must be quantified before guideline incorporation.