Machine learning analysis of heart rate variability identified delayed cerebral ischemia in patients with aneurysmal subarachnoid hemorrhage with 71% sensitivity and 57% specificity.
Cohort (n=64)
No
Does machine learning analysis of heart rate variability detect delayed cerebral ischemia in patients with aneurysmal subarachnoid hemorrhage?
Machine learning analysis of heart rate variability shows acceptable sensitivity but low specificity for detecting delayed cerebral ischemia in patients with subarachnoid hemorrhage.
OBJECTIVES: Approximately 30% of patients with aneurysmal subarachnoid hemorrhage (aSAH) develop delayed cerebral ischemia (DCI). DCI is associated with increased mortality and persistent neurological deficits. This study aimed to analyze heart rate variability (HRV) data from patients with aSAH using machine learning to evaluate whether specific patterns could be found in patients developing DCI. MATERIAL however, whereas the sensitivity in the present study was acceptable, the specificity was low. Possible confounders such as severity of illness and therapy may have affected the result. Future studies should focus on developing a robust method for detecting DCI using real-time HRV data and explore the limits of this technology in terms of its reliability and accuracy.
Hergès et al. (Fri,) conducted a cohort in aneurysmal subarachnoid hemorrhage (aSAH) (n=64). Machine learning analysis of heart rate variability was evaluated on delayed cerebral ischemia (DCI). Machine learning analysis of heart rate variability identified delayed cerebral ischemia in patients with aneurysmal subarachnoid hemorrhage with 71% sensitivity and 57% specificity.