Neurocritical Care

, Volume 20, Issue 3, pp 382–389 | Cite as

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

  • J. Michael SchmidtEmail author
  • Daby Sow
  • Michael Crimmins
  • David Albers
  • Sachin Agarwal
  • Jan Claassen
  • E. Sander Connolly
  • Mitchell S. V. Elkind
  • George Hripcsak
  • Stephan A. Mayer
Original Article



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


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.


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).


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.


Subarachnoid hemorrhage Nosocomial infection Delayed cerebral ischemia Cerebral vasospasm Sepsis Heart rate variability 



The Project described was supported by Grant Number KL2 RR024157 (JMS) from the National Center for Research Resources (NCRR), a component of the National Institutes of Health (NIH) and NIH Roadmap for Medical Research, and its contents are solely the responsibility of the authors and do not necessarily represent the official view of NCRR or NIH. Information on NCRR is available at NCRR Website. Information on Re-engineering the Clinical Research Enterprise can be obtained from NIH Roadmap website. Additional support was provided by the Charles A. Dana Foundation (SAM and JMS) and an IBM Faculty Research Award (JMS).


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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • J. Michael Schmidt
    • 1
    Email author
  • Daby Sow
    • 4
  • Michael Crimmins
    • 1
  • David Albers
    • 5
  • Sachin Agarwal
    • 1
    • 2
  • Jan Claassen
    • 1
    • 2
  • E. Sander Connolly
    • 2
  • Mitchell S. V. Elkind
    • 1
    • 3
  • George Hripcsak
    • 5
  • Stephan A. Mayer
    • 1
    • 2
  1. 1.Department of NeurologyColumbia University Medical Center, New YorkNew YorkUSA
  2. 2.Department of NeurosurgeryColumbia University Medical Center, New YorkNew YorkUSA
  3. 3.Department of EpidemiologyMailman School of Public Health, Columbia UniversityNew YorkUSA
  4. 4.IBM Research, T. J. Watson Research Center, YorktownNew YorkUSA
  5. 5.Department of Biomedical InformaticsColumbia University Medical Center, New YorkNew YorkUSA

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