icu case presentation-english 2016
TRANSCRIPT
Critical Care BioinformaticsElectrophysiology & Autonomic Neurosciences
Vasilios PapaioannouAssistant Professor of Intensive Care Medicine
Democritus University of Thrace2016
Critical illness
Inflammation
sepsis(σήψη) Trauma/Shock
Acute coronary syndrome
Cerebral vascular accidents
The great challenges in the treatment of critical illness
• Early diagnosis
• Outcome prediction
• Development of personalized/individualized treatment– Modulation of inflammatory response
Too much data-lack of information The need for systems integration in healthcare.
Pronovost PJ. JAMA 2011
• Data overload
• Lack of integration of information
• Lack of real time data processing in real time
• Lack of deep understanding of pathophysiology
Common infrastructures for clinical research Complex biological systems Computational biology and informatics New interdisciplinary, translational research teams
http://nihroadmap.nih.gov/
86.000 EURO
International Research Awards-Grants
Monitors9 beds
PC Serversdata collection
Data storageserver
Intensive Care Unit
Survivor Non survivor
0,00
0,25
0,50
0,75
1,00
0,00 0,25 0,50 0,75 1,00
ROC Curve of OUTCOM
1-Specificity
Sen
sitiv
ity
CriterionsVARIANCERATIOTF
Effects of endotoxin on biophysical properties funny current in HEΚ-293 cells after transfection with HCN2 genes using
patch clamp experiments(in vitro experiments)
Papaioannou V, et al. Crit Care 2011, doi: 10.1186/cc9663
Optical mapping of rabbit SAN preparation
SAN view under microscope SAN view with OP camera 15 min after staining with di-4-ANNEPS
Control , CL: 362 msec LPS 3 mg, 15 min, CL: 380 msec
LPS 3 mg, 30 min, CL: 424 msec
LPS 3 mg, 60 min, CL: 477 msec
Color means time of activationRed area means short time
(of activation) and more brownor even green stands for longer time of activation.
Increase in CL, meaning slowerconduction, is shown with reduction
in the amount of red area and increase in the percentage of brown
zone within the SANMapLab software
SAN optical mapping
15,30,60,90 and 120 min after
LPS administration
Optical mappingex vivo
experiments
Computer Methods and Programs in Biomedicine 2014; 113: 210-220, DOI: (10.1016/j.cmpb.2013.07.024)
0
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1 2 3 4 5 6
days
mg/
dL0
0,5
1
1,5
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2,5
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3,5
CRP
LF/HF
0
10
20
30
40
50
60
1 2 3 4 5 6
days
mg/
dL
0
0,005
0,01
0,015
0,02
CRP
SDNN
r=-0.79, p<0.01
r=-0.61, p<0.05
RESULTS(longitudinal
trends)
Lung disease
Normal state
Respiratory complexity in health and critical illness –Weaning outcome prediction
weaning outcome
successfailure
2.0
1.5
1.0
.5
0.0
-.5
HR MSE
DFA α1 exponent
RR SampEn
HR-RR cross entropy
HR MSE fast slope1 - Specificity
1.00.75.50.250.00
Sen
sitiv
ity
1.00
.75
.50
.25
0.00
Reference Line
New model
Conventional model
Box plot of statistically different weaning indices
ROC curves of conventional vsnew model of weaning predictors
Phase map of MV
y = -0.8929x + 22.928R² = 0.5564
0
5
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0 2 4 6 8 10 12 14 16
TAP
SE
(mm
)
duration of mechanical ventilation (days)
y = -0.5725x + 14.043R2 = 0.5278
0
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18
0 2 4 6 8 10 12 14 16
duration of mechanical ventilation (days)
Sm
(cm
/sec
)
0 5 10 15 20 256
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20
SOFAlimit to septic ShockPredicted SOFA
Analgesia Nociceptive Index-ANI
for analgesia quantitative assessment
http://ebooks.benthamscience.com/book/9781681080604
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