Powerpoint template for scientific posters (Swarthmore College)

1
Novel parameters for predicting the outcome in a
murine model of sepsis Beat-to-beat Volatility
Skaf J, Goel N, Guglielmi M, Lichaa H, Zanotti
S, Parrillo JE, Hollenberg SM Division of
Cardiovascular Disease and Critical Care
Medicine, Cooper University Hospital, Camden, NJ

• Introduction
• Critically-ill patients are traditionally
  followed by linear parameters such as Heart Rate
  (HR) and Blood Pressure (BP).
• Nonlinear analysis of hemodynamic parameters is
  affected by many inputs (sympathetic,
  parasympathetic, metabolic, others) and may
  provide insights not available from standard
  linear measures.
• However artifact and the complexity of Power
  Spectral Analysis (PSA) required to evaluate
  nonlinear parameters poses challenges for routine
  clinical application.
• We propose the use of volatility, a measure of
  the standard deviation variability, as an
  alternative nonlinaer parameter
• We also compare Heart Rate Volatility (HRV), to
  Blood Pressure Volatility (BPV)

Results
The analysis didnt reveal any statistical
significance in the Control Group for both HR At
Baseline SD value at the 5 cutoff 13.59 Post
Sham-Operation 5 of intervals are lt 13.59
(pgt0.01) BP At Baseline SD value at the 5
cutoff 1.74 Post Sham-Operation 13.16 of
intervals are lt 1.74 (pgt0.01) In the Septic
Group, The HR analysis showed after CLP 38.72 of
intervals below the 5 cutoff while the BP
analysis revealed 31.45 below this cutoff. HR
At Baseline SD value at the 5 cutoff 16.28
Post Sham-Operation 38.72 of intervals are lt
16.28 (plt0.01) BP At Baseline SD value at the
5 cutoff 1.64 Post Sham-Operation 31.45 of
intervals are lt 4.64 (plt0.01) Both HRV and BPV
dropped dramatically post-CLP.
For each animal, SD histograms were constructed
and the cutoff that represented the lowest 5 was
calculated for the baseline period. The
percentage of low SDs (representing low
volatility) in the entire experimental periods
was defined by this cutoff. This percentage was
calculated for HR and BP in both Control and
Septic groups.
Materials and Methods

• Time courses over 4H
• Intervals show significant differences between
  controls and septics for both HRV and BPV.
• HRV perturbation preceded measurable changes in
  HR in septic animals but remained abnormal in
  both survivors and non-survivors.
• Survivors and non-survivors had a similar
  decrease in BPV early, with eventual
  normalisation in survivors and further
  perturbation in non survivors.

The SD value at 5 representing low variability
was determined for each animal individually and
the percentage of low volatility periods was
calculated over 4h intervals in order to generate
a time course.

• Radiotelemeters implanted in C57/Bl6 mice (8-12
  weeks, n24).
• After 5-7 days of recovery, baseline data
  collected for 24 hours.
• Sepsis by Cecal Ligation and Puncture (CLP),
  (n20) controls received Sham ligation (n4).
• Resuscitation with fluids and antibiotics.

Using MATLAB v.7.1, mean standard deviations (SD)
of HR and BP were calculated per 5 minute
intervals for baseline and experimental periods.
BP waveforms collected using DSI software
Dataquest ART 3.1 at 500 HZ.

• Conclusions
• _Analysis of volatility is less demanding and
  less susceptible to artifacts as a means of
  expressing HR and BP variability
• _We have shown dramatic differences in HRV and
  BPV between septic and control animals in our
  murine model.
• _We demonstrated that it is possible to combine
  HRV BPV in a time-effective manner, but also
  that HRV and BPV dont behave in the same manner
  after CLP. We theorize that
• HRV is a sensitive leading indicator of
  hemodynamic decompensation. However, BPV could
  be a marker for failure to resolve organ
  function.
• It is possible in the future to apply this
  methodology in the ICU, using a rolling average
  of Volatility.