Following recent study findings about heart rhythms before and after stroke, researchers are recommending more tech tools, including artificial intelligence, to help identify when older adults and others susceptible to stroke are at greater risk.
Irregular heartbeats, or atrial fibrillation, that occur in post-stroke patients are slightly different from what is seen in people with heart conditions who have never had a stroke, researchers say.
The findings, which were published this month in The Lancet, suggest both a new classification for post-stroke heartbeat irregularities and recommend more rigorous monitoring to ensure more personalized treatment for patients.
Within senior living and care, that could mean using wearables or other monitoring devices to create risk profiles for residents and help prevent a second stroke from occurring.
AI-enabled monitoring was highlighted as a way to aid in distinguishing these heart conditions by the researchers, led by a team from Western University in London, Ontario.
That’s because even current AI technologies can interpret data from sensors to help establish a residents’ baseline symptoms and then flag episodes when there are deviations.
Research to improve both stroke diagnosis and treatment is coming at a time when stroke deaths suddenly are on the rise after decades of decline.
Correct diagnosis of stroke remains a challenge, and clinicians miss stroke in 17.5% of cases, one recent study showed.
One intriguing diagnostic tool for stroke is a brain-wave “swimming cap” that would determine whether someone has had an ischemic stroke — when blood supply to the brain is reduced or blocked — and how large the affected blood vessel is.
The inventors behind this cap, however, recommended that it be used when a patient already has requested an ambulance, and they noted that follow-up studies are ongoing.
