Heart rate variability for preclinical detection of secondary complications after subarachnoid hemorrhage.

Background: We sought to determine if monitoring heart rate variability (HRV) would enable preclinical detection of secondary complications after subarachnoid hemorrhage (SAH).

Methods: We studied 236 SAH patients admitted within the first 48 h of bleed onset, discharged after SAH day 5, and had continuous electrocardiogram records available. The diagnosis and date of onset of infections and DCI events were prospectively adjudicated and documented by the clinical team. Continuous ECG was collected at 240 Hz using a high-resolution data acquisition system. The Tompkins-Hamilton algorithm was used to identify R-R intervals excluding ectopic and abnormal beats. Time, frequency, and regularity domain calculations of HRV were generated over the first 48 h of ICU admission and 24 h prior to the onset of each patient's first complication, or SAH day 6 for control patients. Clinical prediction rules to identify infection and DCI events were developed using bootstrap aggregation and cost-sensitive meta-classifiers.

Results: The combined infection and DCI model predicted events 24 h prior to clinical onset with high sensitivity (87 %) and moderate specificity (66 %), and was more sensitive than models that predicted either infection or DCI. Models including clinical and HRV variables together substantially improved diagnostic accuracy (AUC 0.83) compared to models with only HRV variables (AUC 0.61).

Conclusions: Changes in HRV after SAH reflect both delayed ischemic and infectious complications. Incorporation of concurrent disease severity measures substantially improves prediction compared to using HRV alone. Further research is needed to refine and prospectively evaluate real-time bedside HRV monitoring after SAH.