icu case presentation-english 2016
TRANSCRIPT
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Critical Care BioinformaticsElectrophysiology & Autonomic Neurosciences
Vasilios PapaioannouAssistant Professor of Intensive Care Medicine
Democritus University of Thrace2016
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Critical illness
Inflammation
sepsis(σήψη) Trauma/Shock
Acute coronary syndrome
Cerebral vascular accidents
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The great challenges in the treatment of critical illness
• Early diagnosis
• Outcome prediction
• Development of personalized/individualized treatment– Modulation of inflammatory response
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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
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Common infrastructures for clinical research Complex biological systems Computational biology and informatics New interdisciplinary, translational research teams
http://nihroadmap.nih.gov/
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86.000 EURO
International Research Awards-Grants
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Monitors9 beds
PC Serversdata collection
Data storageserver
Intensive Care Unit
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Survivor Non survivor
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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
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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
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Optical mapping of rabbit SAN preparation
SAN view under microscope SAN view with OP camera 15 min after staining with di-4-ANNEPS
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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
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SAN optical mapping
15,30,60,90 and 120 min after
LPS administration
Optical mappingex vivo
experiments
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Computer Methods and Programs in Biomedicine 2014; 113: 210-220, DOI: (10.1016/j.cmpb.2013.07.024)
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0
10
20
30
40
50
60
1 2 3 4 5 6
days
mg/
dL0
0,5
1
1,5
2
2,5
3
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)
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Lung disease
Normal state
Respiratory complexity in health and critical illness –Weaning outcome prediction
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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
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Phase map of MV
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y = -0.8929x + 22.928R² = 0.5564
0
5
10
15
20
25
30
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
2
4
6
8
10
12
14
16
18
0 2 4 6 8 10 12 14 16
duration of mechanical ventilation (days)
Sm
(cm
/sec
)
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0 5 10 15 20 256
8
10
12
14
16
18
20
SOFAlimit to septic ShockPredicted SOFA
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Analgesia Nociceptive Index-ANI
for analgesia quantitative assessment
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http://ebooks.benthamscience.com/book/9781681080604
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http://excellence.minedu.gov.gr/draseis/listing/487-icu
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