Heart Rate Variability as a Biomarker of Neurocardiogenic Injury After Subarachnoid Hemorrhage

Murad Megjhani, Farhad Kaffashi, Kalijah Terilli, Ayham Alkhachroum, Behnaz Esmaeili, Kevin William Doyle, Santosh Murthy, Angela G. Velazquez, E. Sander Connolly, David Jinou Roh, Sachin Agarwal, Ken A. Loparo, Jan Claassen, Amelia Boehme, Soojin Park

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Background: The objective of this study was to examine whether heart rate variability (HRV) measures can be used to detect neurocardiogenic injury (NCI). Methods: Three hundred and twenty-six consecutive admissions with aneurysmal subarachnoid hemorrhage (SAH) met criteria for the study. Of 326 subjects, 56 (17.2%) developed NCI which we defined by wall motion abnormality with ventricular dysfunction on transthoracic echocardiogram or cardiac troponin-I > 0.3 ng/mL without electrocardiogram evidence of coronary artery insufficiency. HRV measures (in time and frequency domains, as well as nonlinear technique of detrended fluctuation analysis) were calculated over the first 48 h. We applied longitudinal multilevel linear regression to characterize the relationship of HRV measures with NCI and examine between-group differences at baseline and over time. Results: There was decreased vagal activity in NCI subjects with a between-group difference in low/high frequency ratio (β 3.42, SE 0.92, p = 0.0002), with sympathovagal balance in favor of sympathetic nervous activity. All time-domain measures were decreased in SAH subjects with NCI. An ensemble machine learning approach translated these measures into a classification tool that demonstrated good discrimination using the area under the receiver operating characteristic curve (AUROC 0.82), the area under precision recall curve (AUPRC 0.75), and a correct classification rate of 0.81. Conclusions: HRV measures are significantly associated with our label of NCI and a machine learning approach using features derived from HRV measures can classify SAH patients that develop NCI.

Original languageEnglish (US)
Pages (from-to)162-171
Number of pages10
JournalNeurocritical Care
Volume32
Issue number1
DOIs
StatePublished - Feb 1 2020
Externally publishedYes

Keywords

  • Data mining
  • Heart rate variability
  • Machine learning
  • Myocardial stunning
  • Neurocardiogenic
  • Subarachnoid hemorrhage

ASJC Scopus subject areas

  • Clinical Neurology
  • Critical Care and Intensive Care Medicine

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