Patient monitoring: Invasive VS non-invasive Hemodynamic instability hard to assess Significant limitations

1. Detection of hemodynamic status using an
analytic based on an ECG lead waveform
Florian F. Schmitzberger, MD, MS; Ashley E. Hall, MD; Morgan E. Hughes, RN
BSN; Ashwin Belle, PhD; Bryce Benson, PhD, Kevin R. Ward, MD; Benjamin S.
Bassin, MD

2. Background

3. Patient monitoring
Invasive VS non-invasive Hemodynamic instability
hard to assess
Significant limitations

4. • Loss of heart rate variability (HRV) reflects the declining health of the autonomic
nervous system and has been used to reflect the state of the cardiovascular
system in acute illness and injury
• Challenging in regard to: signal acquisition, sampling rates, signal noise and
processing of EKG data
• We studied a newly developed heart-rate variability analytic to assess if
continuous HRV monitoring can accurately detect the combination of
hypotension and tachycardia as a proxy for hemodynamic instability without the
need for invasive arterial BP measurement
Clinical decision support systems

5. Methods

6. • FDA approved
• Produced by FifthEye, Inc.
• Continuous measurement of EKG lead II
• Automated analytical steps extract patterns from the continuous ECG data
• Includes signal quality assessment and processing the extracted patterns through
a pre-trained classification model
• Updated every two minutes
AHI - Analytic for Hemodynamic Instability

8. Two standards of hemodynamic instability
1) Hypotension (MAP < 70 or SBP < 90) + tachycardia (HR > 100)
2) Shock index > 1.0
EC3 and ICU patients with EKG data were included from November 2019 to
February 2020
Compare AHI output to 5 minute intervals of EKG data to continuously measured
BP and HR.
Evaluation

9. • Also: subgroups of patients with or without vasopressors/inotropes and patients
with or without beta blockers using only the vital signs based reference standard

10. Results

12. AHI’s observed sensitivity was 96.9% and the observed specificity was 79.0% with
an AUC of 0.90.
For the shock index analysis, AHI’s observed sensitivity was 72.0% and the
observed specificity was 80.3% with an AUC of 0.81.
Subgroup analysis for patients receiving beta blockers and vasopressors/inotropes
showed very similar performance of sensitivity, specificity etc.
Results

13. Discussion

14. Systems in use: Modified Early Warning Score (MEWS), National Early Warning
Score (NEWS), electronic Cardiac Arrest Risk Triage (eCART), Predicting Intensive
Care Transfers and Other Unforeseen Events (PICTURE) and other scores that utilize
intermittent vital signs and other data to assess risks for cardiac arrest, ICU
transfer, or death
Limitations: intermittent data measurements (vitals signs)
Early warning systems

15. Ideal use: detect clinical deterioration early, take appropriate action
AHI is meant as adjunct data, not as an alarm system, good specificity but use as an
alarm system would likely increase alarm fatigue (15.1% type I errors).
Usage scenarios

16. • Retrospective analysis at a single academic healthcare center
• Hard cutoffs for vital signs were used
As a noninvasive monitoring technology, the system may over advantages in the
continuous surveillance of patients and their hemodynamic status
Limitations and Conclusion

17. Thank you
Florian F. Schmitzberger, MD, MS; Ashley E. Hall, MD; Morgan E. Hughes, RN BSN;
Ashwin Belle, PhD; Bryce Benson, PhD, Kevin R. Ward, MD; Benjamin S. Bassin, MD
fschmitz@med.umich.edu