Heart Rate Variability for Preclinical Detection of Secondary Complications After Subarachnoid Hemorrhage

Abstract

Introduction 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 hours 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 hours of ICU admission and 24 hours 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 hours 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.

Baroreflex Impairment After Subarachnoid Hemorrhage Is Associated With Unfavorable Outcome

“…19 Our results are in line with those of Papaioannou et al who found in 20 brain injured patients from multiple causes, including 3 patients with SAH, an association between decreased BRS and a high mortality rate (18). They are also in line with the results of Schmidt et al (30) who, assessed HR variability in the frequency domain in subarachnoid haemorrhage and identified an association between the LF/HF ratio (a ratio between the low frequency power and the high frequency power in RR intervals time series), which is an indicator of the sympathetic activity, and the occurrence of an infection or of a delayed ischemic deficit. In another study, Schmidt et al highlighted an association between a HR increase above 95 /minute lasting more than 12 hours and the 3-month functional outcome (31).…”

ADARRI: a novel method to detect spurious R-peaks in the electrocardiogram for heart rate variability analysis in the intensive care unit

“…HRV monitoring is a continuous, noninvasive tool increasingly used in the ICU in order to serve as a warning system and early detection application for worsening illness in critically ill patients [ 1 ]−[ 5 ] Recent applications of HRV monitoring include early diagnosis of sepsis in neonates [ 6 , 8 , 9 ] and adults [ 7 ], monitoring the depth of sedation in mechanically ventilated ICU patients [ 10 ] and preclinical detection of major adverse cardiopulmonary events [ 12 ] as well as secondary complications [ 11 ] after SAH. As these applications become crucial in the ICU setting, artifact-free HRV monitoring will be imperative.…”

“…Heart rate variability (HRV) [ 1 – 5 ] monitoring is increasingly used in the intensive care unit (ICU) as a continuous noninvasive index with potential to serve as an early warning signal of worsening illness. Recent successful applications include detection of sepsis in neonates and adults [ 6 – 9 ], tracking the depth of sedation in patients on mechanical ventilation [ 10 ], and detection of delayed cerebral ischemia following subarachnoid hemorrhage (SAH) [ 11 , 12 ].…”

“…Our intended application is monitoring HRV in patients with subarachnoid hemorrhage. [ 11 , 12 ] Thus, we optimized the algorithm parameters and validated its performance on a large set of ICU ECG recordings from a diverse group of critically ill patients with SAH. We present our method and compare the performance with the Berntson and Clifford methods on the same data.…”

IBM’s Health Analytics and Clinical Decision Support

“…We have been using an array of offline analytical platforms such as Weka, R, SPSS, and big data platforms like Hadoop for the mining of large volumes of data. We have created applications of Weka analytics to build models able to predict secondary complication in neuro-ICUs [40]. Patient similarity concepts learned on historical data may allow physicians to make clinical decisions leveraging experiences gathered from data from similar patients observed in the past [41].…”