Systems Biology Approaches to ATC and...

Systems Biology Approaches to ATC and RDCR

Mitchell Jay Cohen MD FACS Associate Professor In Residence University of California San Francisco

San Francisco General Hospital

•  San Francisco’s only trauma center •  Cares for all pa7ents with trauma7c injury in San Francisco, regardless of ability to pay

•  Serves 100,000 pa7ents per year •  Cares for 4,900 injured pa7ents per year

SFGH Surgical Research Lab

Problems with our data.

•  Single time point predictions •  No attention to dynamics •  Correlations •  Multivariate data •  Multivariate confounding •  Over fitting •  Dimentionality •  Complexity

Why Models?

•  They allow for hypothesis generation •  They allow for visualization •  They allow for identification of pathways,

and relationships impossible to otherwise visualize

•  They allow for in silico experimentation.

They are the most rigorous and unambiguous representation of our hypotheses and understanding

Kinds of models

Cartoon Models

aPC

T

TM ePCR

PC

T

Vascular Endothelium

PAI-1

D-Dimers

FVa FVIIIa

TAFI

TM

T

TAFIa

Plasmin

Plasminogen

Fibrinolysis

tPA

PAI-1

INJURY / HYPOPERFUSION

Coagulopathy Hyperfibrinolysis

MBL

Factor B Factor D

C1q

C3b

C5

MAC (C5b-9)

C5a

C3b C1r C1s C4 C2 MASP-1

MASP-2 C2, C4

Alternative Pathway

Classical Pathway Lectin

Pathway

Animal Models: Traumatic Coagulopathy

•  Soft-Tissue Trauma

•  Hemorrhagic shock: –  Non-ventilated, fixed-pressure. –  Blood withdrawn via vascular line. –  MAP 35 +/- 5mmHg x 60 min.

•  Resuscitation: –  LR @ 2x shed blood volume

+ shed blood

Statistical Models

•  Multivariate component space

Fac

tor I

I

Factor VIII Factor V

PC2

PC1

Dynamic ODE Models

•  Suppose input is a (unit) impulse, U(s) = 1 and:

•  System transfer func7on, including delay:

( ) 32233 3322

)()(

βββα

βα

+++=

+=

sssssUsY

skn deksksks

ksUsY −

+++=

012

23)(

)(

Statistical Models

The first step…physiologic state recognition.

The problem of physiologic recognition Recognition of resuscitation status and patient

physiology remains elusive. Occult hypoperfusion is common but

difficult to identify.

Clinicians rely on a few favorite variables and developed clinical acumen for treatment decisions. This acumen is threatened.

Heart rate Blood pressure Oxygen saturation

Pulmonary artery pressure Intracranial pressure Temperature

Nursing Documentation Medications Treatments Intake Output Vital signs Blood products IV fluids

Tissue oxygen

Inspired oxygen Tidal volume Peak pressure

End expiratory pressure Respiratory rate Ventilation mode

Cardiac output

Sedation level

ICP wave

This is a data intense environment

The ICU Today

Trauma Informatics

2013

Treat one parameter and to one threshold… We order interventions targeting single parameters

•  Fluid bolus or pressors for SBP < 90 mmHg •  Mannitol for ICP > 20 mmHg

The ICU Today

We treat univariately in a multivariate world

Recognition of transition and trends?

Data not manageable No Attention to Relationship Among Parameters Experience of practitioner

EtCO2

Muscle Oxygen

Ventilator Parameters

Sedation

HR MAP RR SpO2

nursing documentation

& lab results

Aristein Bioinformatics

The first step collect the data…

Fig. 2: Selected medical devices and network connections.

Harnessing the Data

Building the Infrastructure Biomedical Informatics for Critical Care

COMPUTER SCIENCE

ACADEMIC MEDICINE

INDUSTRY

A Multidisciplinary Collaboration

The next step…conjure the simple from the complex At any time point a patient’s state is characterized

by the interactions of all variables.

Patient states that are identified would be impossible to see by brute force or traditional methods.

Examination of only a few is insufficient and provides only partial information about the patient state.

New informatic techniques allows organization of complexity so that all data can be simultaneously used and examined.

0 33 67 100 133 -10 -8 -6 -4 -2 0 2 4 6 8

10 12 14 Base deficit

Muscle Lactate

50 83 117 150 17

Time in hours

What’s wrong with the old way?

-1000 0 1000 2000 3000 4000 5000 6000 7000 8000 9000

-8

-6

-4

-2

0

2

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8

10

12

14

60

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120

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0

100

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900

20

30

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0.000

0.002

0.004

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0.010

0.012

0.014

0.016

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0.020

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0.034

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0.040

BD mlac mgluc

map hr

mpyr

pmo2 mlp

to51

New informatic techniques Come from business data mining, high

throughput genomics, and statistical physics. These include…

Hierarchical clustering, k-means clustering,

self organizing maps, artificial neural networks and network topology.

This is how…

We can make sense of the expression patterns of thousands of genes at one time.

Facebook can know know your next best friend before you know. Companies can market womens’ underwear next to cereal next to beer..

For this analysis… Microdialysis, lab and physiologic data were

collected on 25 patients.

Our data set had 45 variables, 96,000 rows and over 4 million data points.

Data analyzed using traditional techniques which got us only so far…

Turned to complex bioinformatic techniques to make sense of these data.

€

d i, j( ) = xik − x jk( )2

k=1

n

∑Distance metric – Euclidean distance:

Hierarchical Clustering

Calculate distance metric

Merge “closest” clusters

Recalculate affected distances

“Closest” defined by complete linkage:

€

C A,B( ) = maxi∈A , j∈B

d i, j( ){ }

Data row Dendogram and Heatmap

MAP Heart Rate PmO2 PmO2

Temp Lung Comp

PEEP Min Vol

SpO2 fiO2 mLac mGlu mGlut mPyru mLP

Cluster 1

82.7 ±

12.8 99.4

± 16.8

36.5 ±

14.5 37.3

± 0.9

37.2 ±

11.7 7.8 ±

3.9 10.3 ± 2.0

99.0 ±

1.7 51.7

± 19.0

3.7 ± 2.7

7.0 ± 2.3

7.4 ±

5.5 213.2

± 25.2

0.016 ±

0.011

Cluster 2

81.6 ±

13.2 96.2

± 21.3

36.0 ±

10.3 37.0

± 1.6

36.0 ±

14.1 7.6 ±

2.8 9.5 ±

2.2 98.5

± 2.2

54.6 ±

19.1 2.2 ±

0.7 7.2 ± 2.2

9.2 ±

4.4 113.9

± 32.7

0.020 ±

0.014

Cluster 3

75.7 ±

16.0 101.8

± 11.4

43.3 ±

13.1 37.1

± 0.8

199.4 ±

48.5 4.8 ±

0.4 8.3 ±

2.2 99.5

± 0.8

32.2 ±

4.9 6.2 ± 1.3

6.6 ±

0.1 5.6 ±

3.3 533.2

± 28.0

0.012 ±

0.002

Cluster 4

81.5 ±

12.0 105.3

± 28.4

31.4 ±

10.3 36.7

± 0.8

41.4 ±

24.9 6.7 ±

2.5 9.1 ±

2.1 99.0

± 2.0

45.0 ±

13.9 7.3 ±

2.1 6.8 ± 2.2

10.6 ±

15.1 523.4

± 32.9

0.014 ±

0.004

Cluster 5

86.6 ±

16.9 100.6

± 25.8

28.9 ±

11.2 36.4

± 0.8

30.4 ±

11.5 7.8 ±

3.0 9.4 ±

1.8 98.4

± 3.9

52.4 ±

18.5 7.4 ±

2.0 6.1 ±

2.0 12.4 ±

12.7 430.7

± 30.0

0.017 ±

0.005

Cluster 6

83.6 ±

12.7 104.2

± 15.7

31.6 ±

10.9 37.2

± 0.7

33.9 ±

14.6 6.7 ±

2.7 10.1 ± 1.9

99.1 ±

1.7 46.4

± 15.0

5.4 ± 2.1

7.0 ± 2.7

12.1 ±

10.5 322.4

± 39.4

0.017 ±

0.007

Cluster 7

86.4 ±

2.5 88.9

± 1.3

45.3 ±

7.3 37.8

± 0.1

210.2 ±

62.9 4.2 ±

0.4 13.9 ± 1.4

96.0 ±

2.2 40.2

± 0.3

1.1 ± 0.0

7.6 ± 0.1

8.5 ±

0.1 87.2

± 0.6

0.013 ±

0.000

Cluster 8

81.4 ±

13.2 115.4

± 11.1

36.7 ±

7.9 36.6

± 0.4

38.4 ±

22.8 6.6 ±

2.5 8.9 ±

1.3 99.0

± 1.4

41.6 ±

13.1 10.7

± 2.7

6.4 ±

2.6 14.6

± 16.2

697.3 ±

60.0 0.015

± 0.003

Cluster 9

76.4 ±

12.7 109.4

± 10.9

32.5 ±

8.1 36.9

± 0.8

34.5 ±

14.5 5.7 ±

1.4 8.8 ±

1.6 99.0

± 1.6

39.7 ±

9.2 11.8

± 2.8

7.9 ± 4.8

11.6 ±

11.3 847.5

± 51.8

0.013 ±

0.003

Cluster 10

96.2 ±

25.2 103.7

± 3.6

22.9 ±

3.0 36.8

± 0.7

29.5 ±

5.4 5.0 ±

0.0 8.8 ±

1.5 98.6

± 1.3

41.1 ±

8.5 12.3

± 2.6

11.9 ±

2.9 30.4

± 26.5

989.5 ±

48.3 0.012

± 0.003

0.11

0.02

0.13

0.00

0.20

0.17

0.06

0.00

0.13

0.00 0.000.00

0.05

0.10

0.15

0.20

0.25

Baseline Cluster1 Cluster 2 Cluster3 Cluster4 Cluster5 Cluster6 Cluster7 Cluster8 Cluster9 Cluster10

Probablity of Death

0.47

0.31

0.50

0.00

0.52

0.63

0.35

0.00

0.56 0.55

0.84

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90


Probability of MOF

0.73

0.67

0.78

0.00

0.61

0.67

0.75

1.00

0.56 0.55

0.84

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00


Probability of Infection

Cluster Assignment Over Time

Cluster Assignment and probability of death over time

Patient Movement Through State Space

The next step…better inference from our data.

What’s wrong with the statistics in our literature?

Background Super Learner Prognostic indicators for trauma patients Variable Importance Discussion

Super Learner Algorithm

Alan Hubbard (UC Berkeley); Mitch Cohen(UCSF); I. Diaz, A. Decker, M. Kutcher (UCSF) Statistical Learning Methods for Traumatic (Complex, Messy) Data

Superlearner: Prediction

Principal component analysis

¡ Pattern-finding

¡ Data reduction

¡ Correlated variables are decomposed into uncorrelated synthetic multivariables, or ‘principal components’ (PCs)

¡ Each PC explains the highest percentage of overall variance possible that remains independent of the previous PCs

Principal components analysis

•  Simple linear regression Fa

ctor

V

Factor VIII

Fact

or V

Factor VIII

Factor VFactor VIII

Fact

or II


•  Multiple linear regression


•  Multivariate component space

Fac

tor I

I

Factor VIII Factor V

PC2

PC1

Methods

•  Prospective cohort study •  2/2005 - 10/2010 •  163 critically injured trauma patients •  Factor activity levels drawn on admission:

–  Factors II, V, VII, VIII, IX, X –  Anticoagulants Protein C, Antithrombin III –  Fibrinolysis marker D-dimer

•  Non-linear PCA modeling

PCA model construction

PC1 PC2 PC3 Percent variance 43.91 13.45 10.12

Prothrombin Factor V Factor VII Factor VIII Factor IX Factor X D-dimer

aPC Protein C

AT III

¡ Identify factor patterns


Prothrombin -0.86 Factor V -0.78 Factor VII -0.62 Factor VIII -0.35 Factor IX -0.69 Factor X -0.88 D-dimer 0.25

aPC 0.20 Protein C -0.80

AT III -0.74


Prothrombin -0.86 -0.04 Factor V -0.78 0.01 Factor VII -0.62 0.01 Factor VIII -0.35 0.34 Factor IX -0.69 0.07 Factor X -0.88 -0.01 D-dimer 0.25 0.80

aPC 0.20 0.74 Protein C -0.80 0.11

AT III -0.74 0.16


Prothrombin -0.86 -0.04 0.11 Factor V -0.78 0.01 -0.11

Factor VII -0.62 0.01 0.47 Factor VIII -0.35 0.34 -0.73 Factor IX -0.69 0.07 0.03 Factor X -0.88 -0.01 0.20 D-dimer 0.25 0.80 0.00

aPC 0.20 0.74 0.39 Protein C -0.80 0.11 -0.05

AT III -0.74 0.16 -0.17


Prothrombin -0.86 -0.04 0.11 Factor V -0.78 0.01 -0.11

Factor VII -0.62 0.01 0.47 Factor VIII -0.35 0.34 -0.73 Factor IX -0.69 0.07 0.03 Factor X -0.88 -0.01 0.20 D-dimer 0.25 0.80 0.00

aPC 0.20 0.74 0.39 Protein C -0.80 0.11 -0.05

AT III -0.74 0.16 -0.17 -1.0

+1.0

0.0

PCA model: PC1

•  43.91% variance •  Negative correlation:

– All numbered factors – Anticoagulants PC & AT3

•  Depletion coagulopathy

PC1 Variance 43.91%

Prothrombin -0.86 Factor V -0.78 Factor VII -0.62 Factor VIII -0.35 Factor IX -0.69 Factor X -0.88 D-dimer 0.25

aPC 0.20 Protein C -0.80

AT III -0.74

-1.0 +1.0

PCA model: PC2

•  13.45% variance •  Positive correlation:

– D-dimer & aPC –  Factor VIII

•  Fibrinolytic coagulopathy

-1.0 +1.0

PC2 Variance 13.45%

Prothrombin -0.04 Factor V 0.01 Factor VII 0.01 Factor VIII 0.34 Factor IX 0.07 Factor X -0.01 D-dimer 0.80

aPC 0.74 Protein C 0.11

AT III 0.16

PCA model: outcomes

PC1 PC2 PC3

Odd

s ra

tio Mortality 1.48 1.62 -

Multiorgan failure - 1.83 - Acute lung injury - 2.24 -

VAP - 1.59 - INR ≥1.3 4.68 - - PTT ≥30 3.10 - 1.44

Summary

•  Principal component scores generated from this model will correlate with outcomes.

•  PC1 describes coagulopathic bleeding and mortality.

•  PC2 describes coagulopathy associated with infectious and inflammatory outcomes.

•  These two components are independent and not identifiable using ‘traditional statistics’.

The next step… In silico

Network Topology

Model Kinetic Equations •  Rate constants

aggregated from literature ranges known in 2002.

•  Initial conditions specify mean plasma concentrations for proteins, with TF variable.

Patient Movement Through State Space

Systems Biology Approaches

•  Allow assessment of a patients physiologic and biologic state.

•  Can solve the curse of dimensionality. •  Can learn?? •  Model and harvest the gestalt of the astute

clinician. •  Are the future in the era of big data.

Systems Biology Approaches to ATC and...

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