gilles clermont: modeling critical illness

54
CRI S M A CriticalCare M edicine the U niversity ofPittsburgh Modeling Critical Illness Center for Inflammation and Regenerative Modeling (CIRM) and The CRISMA Laboratory Critical Care Medicine School of Medicine University of Pittsburgh Gilles Clermont MD, MSc

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Page 1: Gilles Clermont: Modeling Critical Illness

CC∙RR∙II∙SS∙MM∙AACritical Care Medicine

the University of Pittsburgh

Modeling Critical Illness

Center for Inflammation and Regenerative Modeling (CIRM) and The CRISMA Laboratory

Critical Care MedicineSchool of Medicine

University of Pittsburgh

Gilles Clermont MD, MSc

Page 2: Gilles Clermont: Modeling Critical Illness

ObjectivesObjectives

The clinical problems facing Critical The clinical problems facing Critical IllnessIllnessOur work at the Center for Regenerative Our work at the Center for Regenerative and Inflammatory Modelingand Inflammatory ModelingThe challenges We faceThe challenges We face

Page 3: Gilles Clermont: Modeling Critical Illness

The Goal of Critical Care ?The Goal of Critical Care ?

Health

Disease

DeathZone of opportunity

Page 4: Gilles Clermont: Modeling Critical Illness

The big challenges in Critical IllnessThe big challenges in Critical Illness

Timely diagnosisTimely diagnosisOutcome predictionOutcome predictionDevelopment of targetted Development of targetted (personalized) therapies(personalized) therapies

Modulation of the inflammatory responseModulation of the inflammatory response

Page 5: Gilles Clermont: Modeling Critical Illness

Root causes of these challengesRoot causes of these challenges

Insufficient, inaccurate dataInsufficient, inaccurate dataWhat to measureWhat to measurePoint-of-care technologiesPoint-of-care technologies

Insufficient interpretative frameworkInsufficient interpretative frameworkUncertain biological mechanismsUncertain biological mechanismsBiological variabilityBiological variabilityDearth of Dearth of in silicoin silico disease models disease models

Insufficient mathematicsInsufficient mathematics

Page 6: Gilles Clermont: Modeling Critical Illness

Critical IllnessesCritical Illnesses

Inflammation

Severe infections(sepsis)

Trauma/Shock

Acute coronary syndrome

Stroke

Page 7: Gilles Clermont: Modeling Critical Illness

Anti-InflammationInflam-Inflam-

mationmation

OrganInjury

RecoveryRecovery

TimeTime

INS

UL

TIN

SU

LT

Current Paradigm of Injury/RecoveryCurrent Paradigm of Injury/Recovery

•Innate immunity•Coagulation•Metabolism

Stress responseStress response

Page 8: Gilles Clermont: Modeling Critical Illness

The canvas…of opportunititesThe canvas…of opportunitites

Infection•Pathogen•Toxins

Early mediators•TNF•IL-1•IL-10•IL-6•Cells

Detection•Dendritic cells•Macrophages

Coagulation

Physiologicmanifestations

Metabolicmanifestations

Organdysfunction

Late mediators

Death

Page 9: Gilles Clermont: Modeling Critical Illness

Opportunities for Immune SupportOpportunities for Immune Support

Time

IL-10 RECOVERY

INSULT

IL-6TNF

IL-1

Anti-LPSAnti-TNFAnti-IL-1Anti-IL-10

Blood Purification

Immunologic Support

HMGB1

Anti-HMGB1

Page 10: Gilles Clermont: Modeling Critical Illness

What about immunomodulation in What about immunomodulation in sepsis and trauma?sepsis and trauma?

25 years of global disaster25 years of global disasterSimplistic rationalesSimplistic rationalesIneffective productsIneffective productsPoor patient selectionPoor patient selection

>70 phase II trials, > 1B dollars invested>70 phase II trials, > 1B dollars investedTwo possible leadsTwo possible leads

Low dose anti-inflammatory treatmentLow dose anti-inflammatory treatmentActivated protein CActivated protein C

Contradictory results!Contradictory results!

Page 11: Gilles Clermont: Modeling Critical Illness

Treating sepsis: the strength of the Treating sepsis: the strength of the ConsensusConsensus

2532

18

Good or Bad?

Page 12: Gilles Clermont: Modeling Critical Illness

The inflammatory responseThe inflammatory response

Huang Q, Science 2001

Page 13: Gilles Clermont: Modeling Critical Illness

Calvano, Nature 2005

Page 14: Gilles Clermont: Modeling Critical Illness

Modeling Inflammation at the CIRMModeling Inflammation at the CIRM

Multiple models of acute inflammation (sepsis, trauma/hemorrhage, Multiple models of acute inflammation (sepsis, trauma/hemorrhage, biowarfare agents, phonotrauma, wound healing), organ biowarfare agents, phonotrauma, wound healing), organ damage/dysfunction, and healing/regenerationdamage/dysfunction, and healing/regeneration

Qualitative and quantitative predictionsQualitative and quantitative predictionsProbing mechanismsProbing mechanisms

Have simulated device usage and guided device designHave simulated device usage and guided device designHave outlined an iterative strategy for rational drug design and Have outlined an iterative strategy for rational drug design and administrationadministrationHave carried out simulated clinical trials in the settings of sepsis Have carried out simulated clinical trials in the settings of sepsis and trauma, including biowarfare applicationsand trauma, including biowarfare applications

Page 15: Gilles Clermont: Modeling Critical Illness

Small models of inflammationSmall models of inflammation

Top-downTop-down““Understand” the biologyUnderstand” the biology

Biological plausibilityBiological plausibility

High-level “map” of the biologyHigh-level “map” of the biologyBuilding blocs for more complex models Building blocs for more complex models

Page 16: Gilles Clermont: Modeling Critical Illness

Reduced models of inflammationReduced models of inflammation

Page 17: Gilles Clermont: Modeling Critical Illness

Transients for 3 possible regimenTransients for 3 possible regimenHealth

Septic death

Aseptic death

Page 18: Gilles Clermont: Modeling Critical Illness

Bifurcation analysis on KpgBifurcation analysis on Kpg

“Septic death”

“Aseptic death”

“Heatlh”

Page 19: Gilles Clermont: Modeling Critical Illness

2-D bifurcation diagram2-D bifurcation diagram

Opportunity

Page 20: Gilles Clermont: Modeling Critical Illness

Manipulating anti-inflammatoriesManipulating anti-inflammatories

Page 21: Gilles Clermont: Modeling Critical Illness

Why complicate thingsWhy complicate things

To produce a calibratedTo produce a calibratedTo “intervene” in the dynamics in a To “intervene” in the dynamics in a realistic way, more realistic “handles” realistic way, more realistic “handles” are neededare neededNot all “modules” need to be equally Not all “modules” need to be equally detaileddetailedThe analysis of large models:The analysis of large models:

May rapidly become intractable May rapidly become intractable May not yield useful resultsMay not yield useful results

Page 22: Gilles Clermont: Modeling Critical Illness

Simulating Inflammatory Disorders at the Simulating Inflammatory Disorders at the CIRMCIRM

ResearchBiological

Mechanisms

DevelopRepresentative

Models

Collect Biomarker

Data

CalibrateModels to Data

Use Modelfor

Predictions

Page 23: Gilles Clermont: Modeling Critical Illness

A Unified Inflammatory ResponseA Unified Inflammatory Response

Page 24: Gilles Clermont: Modeling Critical Illness

Simulations of infectious agents with Simulations of infectious agents with bioterror potentialbioterror potential

Shock 2007

JTB 2007

Page 25: Gilles Clermont: Modeling Critical Illness

Probing mechanismsProbing mechanisms

Page 26: Gilles Clermont: Modeling Critical Illness

In silicoIn silico design of RCTs design of RCTs

Page 27: Gilles Clermont: Modeling Critical Illness

Anti-TNF treatment for sepsis:Anti-TNF treatment for sepsis:A simulation studyA simulation study

Clermont et al. 2004

Page 28: Gilles Clermont: Modeling Critical Illness

Pat

ho

gen

vir

ule

nce

(Qu

arti

le)

Pat

ho

gen

lo

ad(Q

uar

tile

)

Q1

Q2

Q3

Q4

0% 25% 50% 75% 100%Percent

TN

F r

es

po

ns

ive

ne

ss

(Qu

art

ile

)

Q1

Q2

Q3

Q4

An

ti-i

nfl

amm

ato

ry r

esp

on

sive

nes

s(Q

uar

tile

)Q1

Q2

Q3

Q4

0% 25% 50% 75% 100%Percent

Q1

Q2

Q3

Q4

Helped by treatment Lived irrespective of treatmentDied irrespective of treatment Harmed by treatment

Outcome by subgroupsOutcome by subgroups

Page 29: Gilles Clermont: Modeling Critical Illness

Validating Validating in silicoin silico simulation? simulation?

Face validity of the disease modelFace validity of the disease modelKnowledge of key driving factorsKnowledge of key driving factorsDisease model includes these factorsDisease model includes these factorsIntervention modelIntervention model

Mechanism of action of the proposed treatmentMechanism of action of the proposed treatmentPK/PD dataPK/PD data

Predictive ability on empirical dataPredictive ability on empirical dataControlled experimentsControlled experimentsExisting trial dataExisting trial data

Page 30: Gilles Clermont: Modeling Critical Illness

Disease modelDisease model

Must account for uncertain mechanismsMust account for uncertain mechanismsModel structure recapitulates biologyModel structure recapitulates biology

Predictors in a statistical modelPredictors in a statistical modelEquations/rules in white box modelsEquations/rules in white box models

Must make best use of observations at Must make best use of observations at hand which are often incompletehand which are often incomplete

Within a given model structure, develop an Within a given model structure, develop an understanding of the breath of parameter understanding of the breath of parameter realizations that fit data equally wellrealizations that fit data equally well

Uncertainty in the relative importance of Uncertainty in the relative importance of mechanisms/interactionsmechanisms/interactions

Many model realizations are necessaryMany model realizations are necessary

Page 31: Gilles Clermont: Modeling Critical Illness

Some core theoretical challengesSome core theoretical challenges

The variability problemThe variability problemThe inverse problemThe inverse problemThe “cogent-reduction” problemThe “cogent-reduction” problem

Page 32: Gilles Clermont: Modeling Critical Illness

The variability problemThe variability problem

Can inter-individual variability be Can inter-individual variability be characterized in some fashion?characterized in some fashion?

Sepsis as an exampleSepsis as an example

How can mathematical models to How can mathematical models to capture variability?capture variability?Can meaningful diagnostic and Can meaningful diagnostic and therapeutic insights be achieved in the therapeutic insights be achieved in the presence of such variability?presence of such variability?

Page 33: Gilles Clermont: Modeling Critical Illness

Day 1 cytokine levels in non-septic Day 1 cytokine levels in non-septic patients for prediction of severe sepsispatients for prediction of severe sepsis

TN

F a

t ba

selin

e a

nd 9

5% C

I (ln

pg/

ml)

2.1

2.0

1.9

1.8

1.7

p = 0.0087

Severe sepsis(n=268)

No SS(n=1073)

TNF

Analysis restricted to day 1 levels of those patients who do NOT have severe sepsis on first day

4.0

3.8

3.6

3.4

3.2IL6

at b

ase

line

and

95%

CI

p = 0.0009

Severe sepsis(n=268)

No SS(n=1073)

IL-6

Page 34: Gilles Clermont: Modeling Critical Illness

Day 1 levels and survivalDay 1 levels and survival

Dead(n=212)

Alive(n=1410)

Ba

selin

e T

NF

with

95

% C

I

2.4

2.2

2.0

1.8

p<0.0001

5.0

4.6

4.2

3.8

3.4Dead(n=212)

Alive(n=1410)

Ba

selin

e I

L6

with

95

% C

I

p<0.0001

2.8

2.6

2.4

2.2

Dead(n=212)

Alive(n=1410)

Ba

selin

e I

L1

0 w

ith 9

5%

CI

p<0.0001

TNF

IL-10

IL-6

Page 35: Gilles Clermont: Modeling Critical Illness

Day 1 cytokine levels in patients who Day 1 cytokine levels in patients who develop ARF and those that do notdevelop ARF and those that do not

ARF = RIFLE-I or F

4

4.5

5

5.5

6

6.5

7

7.5TNF

Pg

/ml

SE

M

ARF(n=258)

No ARF(n=1544)

p<0.0001

IL-6P

g/m

l S

EM

ARF(n=258)

No ARF(n=1544)

p<0.0001

20

30

40

50

60

70

80IL-10

Pg

/ml

SE

M

ARF(n=258)

No ARF(n=1544)

p=0.0285

4

4.5

5

5.5

6

6.5

7

7.5

Page 36: Gilles Clermont: Modeling Critical Illness

TrajectoriesTrajectories

Trajectories are consistent within individualsTrajectories are consistent within individuals““Shapes” are often consistent across Shapes” are often consistent across individualsindividuals

LPS 4 ng/kg IV

0

1000

2000

3000

4000

5000

6000

0 1 2 3 4 5 6

Time (h)

#1

#7

#9

#21

LPS (1 ng/kg IV)

0

50

100

150

200

250

300

0 1 2 3 4 5 6

Time (h)

seru

m T

NF

(p

g/m

l)

#2

#5

#13

#14

Suffredini et al. 1996

Page 37: Gilles Clermont: Modeling Critical Illness

Cytokines by Outcome (60 days)Cytokines by Outcome (60 days)

IL-6

0

20

40

60

80

100

120

140

1 2 3 4 5 6 7

Days

pg

/ml

SSD

SSA

NSS

IL-10

0

2

4

6

8

10

12

1 2 3 4 5 6 7

Days

pg

/ml

SSD

SSA

NSS

TNF

0

2

4

6

8

10

1 2 3 4 5 6 7

Days

pg

/ml

SSD

SSA

NSS

Page 38: Gilles Clermont: Modeling Critical Illness

Trajectory AnalysisTrajectory Analysis

1 2 3 4 5 6 7

Day

Log IL-6

Day

Log IL-10

1 2 3 4 5 6 7

HH

MM

LL

Page 39: Gilles Clermont: Modeling Critical Illness

Some good newsSome good news

Standard (although by no means Standard (although by no means elementary) statistical techniques elementary) statistical techniques identify “classes” of patientsidentify “classes” of patients

PhysiologyPhysiologyOmicsOmics

Qualitative patterns, but not magnitude Qualitative patterns, but not magnitude of response, often preserved across of response, often preserved across individualsindividuals

Within speciesWithin species

Page 40: Gilles Clermont: Modeling Critical Illness

One model – one patientOne model – one patient

M

M

M

Page 41: Gilles Clermont: Modeling Critical Illness

Disease modelsDisease models

Must account for uncertain mechanismsMust account for uncertain mechanismsModel structure recapitulates biologyModel structure recapitulates biology

Predictors in a statistical modelPredictors in a statistical modelEquations/rules in white box modelsEquations/rules in white box models

Must make best use of observations at Must make best use of observations at hand which are often incompletehand which are often incomplete

Within a given model structure, develop an Within a given model structure, develop an understanding of the breath of parameter understanding of the breath of parameter realizations that fit data equally wellrealizations that fit data equally well

Uncertainty in the relative importance of Uncertainty in the relative importance of mechanisms/interactionsmechanisms/interactions

Many model realizations are necessaryMany model realizations are necessary

Page 42: Gilles Clermont: Modeling Critical Illness

Patient-specific metamodelPatient-specific metamodel

M1

M2

Mn

E(Mn) ≡ Metamodel or Ensemble

Where the individual models vary in theirmathematical structure and parameters

Page 43: Gilles Clermont: Modeling Critical Illness

Population-level EnsemblePopulation-level Ensemble

E(Mn)E(Mp)

E(Mq)

• Empirical rather than phenomenological

Page 44: Gilles Clermont: Modeling Critical Illness

Targetted TherapyTargetted TherapyModel Predictive Control (MPC)Model Predictive Control (MPC)

• The desired output is “health” • The MPC method uses actual data and model simulations

to estimate output: the discrepancy is estimated (Sensor)• The MPC method suggest an optimal intention strategy

which is time dependent (Actuator)

“Base” System• Real patient• MetamodelINPUT

•Actual dataOUTPUT•Actual data•Predicted data

Sensor• Error between actual/desired

Actuator

Controller

Page 45: Gilles Clermont: Modeling Critical Illness

Horizon

k k+1 k+2 k+m-1 k+h

Past Future

R(k-2)

p(k-2)

M(k-2)

R(k)

p(k)

M(k)

R(k+1)R(k+2)

u(k+m-1)

uk

p(k+1)

Reference Trajectory, R

PredictedOutput, p

Measured Output, M

Control Action, u

MPC Schematic

ΔU

Babatunde A. Ogunnaike and W. Harmon Ray. Process Dynamics, Modeling, and Control (Topics in Chemical Engineering). Oxford University Press: New York, 1994. pg. 997.

m – move horizonh – prediction horizonk – current simulation time step

Page 46: Gilles Clermont: Modeling Critical Illness

Patient 20

Patient 405

TailoredStandard

Early treatment, frequent titration are key Early treatment, frequent titration are key Measurement frequency congruent with the time Measurement frequency congruent with the time scale of the process being modulated is keyscale of the process being modulated is key

Page 47: Gilles Clermont: Modeling Critical Illness

The inverse problemThe inverse problem

Page 48: Gilles Clermont: Modeling Critical Illness

time

Observe Low Bloodpressure

Observe Bloodpressure

Give afluidchallenge

Low

Normal

Mid-range

Page 49: Gilles Clermont: Modeling Critical Illness

Model reduction – Bottom-up modelsModel reduction – Bottom-up models

Map “lumped observables” to Map “lumped observables” to biologically relevant functions/modulesbiologically relevant functions/modules

Component aggregationComponent aggregation Identify aggregates (SVD – PCA)Identify aggregates (SVD – PCA)

Time aggregationTime aggregationSpatial segregationSpatial segregation

Data-driven vs. Knowledge-drivenData-driven vs. Knowledge-driven

Page 50: Gilles Clermont: Modeling Critical Illness

Model reduction – Top-down modelsModel reduction – Top-down models

Function vs components may be biasedFunction vs components may be biasedWhat we think we knowWhat we think we knowWhat we can measureWhat we can measure

Which parts of the model can/should be Which parts of the model can/should be abstracted abstracted Could this be driven by constraints Could this be driven by constraints imposed by biological laws?imposed by biological laws?

Page 51: Gilles Clermont: Modeling Critical Illness

Knowledge and successful translationKnowledge and successful translation

EmpiricalEpicycles

- to XVII century

Kepler/Newton Ellipses

First interactionbetween a physicallaw and empiricobservation

GR - EinsteinPrecessing ellipses

Discrepancy betweenpredictions and empiric observation

Depth of knowledge

Discrepancy betweengravity and otherforces of nature

QG - ??Black hole physics

Page 52: Gilles Clermont: Modeling Critical Illness

Inflammation Modeling is a Team SportInflammation Modeling is a Team Sport

Mathematics (Pitt)Carson ChowBard ErmentroutJonathan RubinBeatrice RiviereIvan YotovDavid SwigonJudy DayQi Mi

Mathematics (CMU)Shlomo Ta’asanRima Gandlin

Statistics (Pitt)Greg Constantine

Immunetrics, Inc.John BartelsSteve ChangArie BarattJoyce Wei

IBMFred Busche

Cook County HospitalGary An

University of CologneEddy NeugebauerRolf Lefering

Ludwig Boltzmann InstituteHeinz Redl

SUNY-UpstateGary NiemanDavid Carney

Surgery (Pitt)Tim BilliarRuben ZamoraRosie HoffmanDavid HackamRobert KormosDavid SteedEdith TzengJuan OchoaClaudio LagoaAndres TorresBinnie BittenDerek BarclayThierry Clermont

Critical Care Medicine (Pitt)Gilles ClermontMitchell FinkJohn KellumRuss DeludeJuan Carlos Puyana

McGowan Institute (Pitt)Alan RussellJohn MurphyWilliam FederspielWilliam Wagner

SHRS (Pitt)Cliff BrubakerKittie Verdolini

Medicine (Pitt)David WhitcombMarc Roberts

Children’s Hospital of PittsburghDavid HackamJeffrey UppermanPat HebdaRaphael Hirsch

Page 53: Gilles Clermont: Modeling Critical Illness

www.iccai.org

Page 54: Gilles Clermont: Modeling Critical Illness

CC∙RR∙II∙SS∙MM∙AACritical Care Medicine

the University of PittsburghInflammation Modeling is a Team Sport

Mathematics (Pitt)Mathematics (Pitt)Carson ChowCarson ChowBard ErmentroutBard ErmentroutJonathan RubinJonathan RubinBeatrice RiviereBeatrice RiviereIvan YotovIvan YotovDavid SwigonDavid SwigonJudy DayJudy DayQi MiQi Mi

Mathematics (CMU)Mathematics (CMU)Shlomo Ta’asanShlomo Ta’asanRima GandlinRima Gandlin

Statistics (Pitt)Statistics (Pitt)Greg ConstantineGreg Constantine

Immunetrics, Inc.Immunetrics, Inc.John BartelsJohn BartelsSteve ChangSteve ChangArie BarattArie BarattJoyce WeiJoyce Wei

IBMIBMFred BuscheFred Busche

Cook County HospitalCook County HospitalGary AnGary An

University of CologneUniversity of CologneEddy NeugebauerEddy NeugebauerRolf LeferingRolf Lefering

Ludwig Boltzmann InstituteLudwig Boltzmann InstituteHeinz RedlHeinz Redl

SUNY-UpstateSUNY-UpstateGary NiemanGary NiemanDavid CarneyDavid Carney

Surgery (Pitt)Surgery (Pitt)Tim BilliarTim BilliarRuben ZamoraRuben ZamoraRosie HoffmanRosie HoffmanDavid HackamDavid HackamRobert KormosRobert KormosDavid SteedDavid SteedEdith TzengEdith TzengJuan OchoaJuan OchoaClaudio LagoaClaudio LagoaAndres TorresAndres TorresBinnie BittenBinnie BittenDerek BarclayDerek BarclayThierry ClermontThierry Clermont

Critical Care Medicine (Pitt)Critical Care Medicine (Pitt)Gilles ClermontGilles ClermontMitchell FinkMitchell FinkJohn KellumJohn KellumRuss DeludeRuss DeludeJuan Carlos PuyanaJuan Carlos Puyana

McGowan Institute (Pitt)McGowan Institute (Pitt)Alan RussellAlan RussellJohn MurphyJohn MurphyWilliam FederspielWilliam FederspielWilliam WagnerWilliam Wagner

SHRS (Pitt)SHRS (Pitt)Cliff BrubakerCliff BrubakerKittie VerdoliniKittie Verdolini

Medicine (Pitt)Medicine (Pitt)David WhitcombDavid WhitcombMarc RobertsMarc Roberts

Children’s Hospital of PittsburghChildren’s Hospital of PittsburghDavid HackamDavid HackamJeffrey UppermanJeffrey UppermanPat HebdaPat HebdaRaphael HirschRaphael Hirsch

Developing Trans-disciplinary Mathematical Modeling Teams for the study of Acute Inflammation: A review of the Experiences of Four Research Groups Gary An MD (1), C. Antony Hunt PhD (2), Gilles Clermont MD (3, 6), Edmund Neugebauer, PhD (4) and Yoram Vodovotz PhD (5, 6)

1) Department of Surgery, Northwestern University Feinberg School of Medicine 2) Biosystems Research Group, University of California, San Francisco 3) Department of Critical Care Medicine, University of Pittsburgh4) Institute for Research in Operative Medicine, University of Witten/Herdecke5) Departments of Surgery and Immunology, University of Pittsburgh 6) Center for Inflammation and Regenerative Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA

J. Crit. Care, submitted for publication

Developing Trans-disciplinary Mathematical Modeling Teams for the study of Acute Inflammation: A review of the Experiences of Four Research Groups Gary An MD (1), C. Antony Hunt PhD (2), Gilles Clermont MD (3, 6), Edmund Neugebauer, PhD (4) and Yoram Vodovotz PhD (5, 6)

1) Department of Surgery, Northwestern University Feinberg School of Medicine 2) Biosystems Research Group, University of California, San Francisco 3) Department of Critical Care Medicine, University of Pittsburgh4) Institute for Research in Operative Medicine, University of Witten/Herdecke5) Departments of Surgery and Immunology, University of Pittsburgh 6) Center for Inflammation and Regenerative Modeling, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA

J. Crit. Care, submitted for publication

International Conference on Complexity in Acute Illness

http://www.scai-med.org