icu case presentation-english 2016

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

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0,75

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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)

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dL0

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CRP

LF/HF

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mg/

dL

0

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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

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-.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

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TAP

SE

(mm

)

duration of mechanical ventilation (days)

y = -0.5725x + 14.043R2 = 0.5278

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duration of mechanical ventilation (days)

Sm

(cm

/sec

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SOFAlimit to septic ShockPredicted SOFA

Analgesia Nociceptive Index-ANI

for analgesia quantitative assessment

http://ebooks.benthamscience.com/book/9781681080604

http://excellence.minedu.gov.gr/draseis/listing/487-icu