Hypertension is a known risk factor for atrial fibrillation (AF) and strokes, but there is limited research on AF screening in hypertensive patients and its link to blood pressure levels.
A study involving over 4,000 hypertensive patients aged 60 and older in Japan measured their electrocardiograms (ECG) and blood pressure (BP) at home for three months.
The study found that 5.8% of participants had undiagnosed AF, with no significant differences in AF detection rates based on different baseline BP categories or the use of antihypertensive medication.
Segmental arterial mediolysis (SAM) is a rare vascular disease linked to cerebral aneurysms, predominantly affecting East Asian individuals aged 40-50, often presenting as subarachnoid hemorrhage (SAH).
A systematic review identified 41 cases, showing that 75% of the aneurysms were dissecting types, with a significant risk of complications such as intra-abdominal hemorrhage (IAH) and a high mortality rate.
Early diagnosis and timely surgical intervention are crucial for improving patient outcomes, as those who undergo surgery generally have a favorable prognosis.
Atrial fibrillation (AF) is categorized by duration into paroxysmal, persistent, and long-standing persistent types, with longer durations increasing the risk of recurrence during catheter ablation treatment.
The study aims to enhance the diagnosis accuracy of AF duration through a predictive model, utilizing data from 272 patients to train a machine learning algorithm.
Results showed the model achieved 81.8% accuracy in predicting AF duration, significantly improving cardiologists' diagnostic performance from 63.9% to 71.6% after using the model's insights.