biomarkers identifying sepsis phenotypes · biomarkers identifying sepsis phenotypes k. reinhart...
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Biomarkers identifying sepsis
phenotypes
K. Reinhart
ISF Toronto
October 2015
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A biomarker may serve one or more of
five overlapping roles
Marshall J, Reinhart K CCM 2008
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Potential of sepsis biomarkers
“ Sepsis biomarkers may help to
transform sepsis from a
physiologic syndrome to a group
of distinct biochemical
disorders”
Marshall J, Reinhart K CCM 2008
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Reinhart et al CMR 2012
Potential Sepsis Biomarkers
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Riedemann et al., Nature Med, 2003 May;9(5):517-24
Understanding of the host response a decade
ago
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There is no biphasic or uniform immune
response
K. Reinhart et al Clinical Microbiology Reviews 2012
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Potential of an ideal sepsis marker
•Supports the diagnosis – infectious or other
causes of systemic inflammation
•Prognosis –risk stratification
• Impacts measures for infection control –
antimicrobials
• Contributes to improved patient outcome
• Reflects the course of the immunopathology of
sepsis and identifies suitable patient populations
for sepsis trials
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Müller B, et al. Crit Care Med (2000) 28:977-983 Harbarth S et al, AJRCCM (2001) 164:396-402
1- Specificity
Se
nsit
ivit
y
0 1
1
0
PCT
Laktat
CRP
IL-6
Severe Sepsis
1
1
Se
nsit
ivit
y
PCT + clinical diagnosis
Clinical diagnosis
1- Specificity0
0
Severe Sepsis
Sepsis versus SIRS
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Potential of an ideal sepsis marker
•Support the diagnosis – infectious or other
causes of systemic inflammation
•Prognosis –risk stratification
• Impacts measures for infection control –
antimicrobials
• Contributes to improved patient outcome
• Reflect the course of the immunopathology of
sepsis and identifies suitable patient populations
for sepsis trials
![Page 11: Biomarkers identifying sepsis phenotypes · Biomarkers identifying sepsis phenotypes K. Reinhart ISF Toronto October 2015. A biomarker may serve one or more of five overlapping roles](https://reader030.vdocuments.us/reader030/viewer/2022041302/5e11c87d2c00e7791a213a35/html5/thumbnails/11.jpg)
- Nonalert values = PCT < 1.0 ng/ml or < 1.0 ng/mL
that were not succeeded by values > 1.0 ng/mL
Jensen JU et al. Crit Care Med 2006; 34: online
N = 472 ICU patients; 3672 PCT measurements
PCT as Prognostic Biomarker
- Group 1 = decrease of PCT on day 3 by more than 30% or
PCT on day 3 below 0.25 ng/mL
- Group 2 = increase of PCT or decrease of PCT on day 3 by
less than 30%.
- Alert values = PCT > 1.0 ng/mL with an increase daily,
counting from the day after exceeding 1.0 ng/mL.
Georgopoulou AP et al J Crit Care, 2010: online
N = 289 ICU patients
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Midregional proadrenomedullin for prognosis in
community-acquired pneumonia: A systematic review
Rodrigo Cavallazzi Crit Care 2014
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ADM is associated withseverity of disease
ADM strongly discriminates patients requiring vasopressor
therapy from the others
correlation with APACHE II score: r = 0.46P <0.0001.
ADM is associated withmortality
ADM median (IQR): no vasopressors 48 (32 to 75) pg/mL; with vasopressors 129 (83 to 264) pg/MlP <0.0001
ADM median (IQR): survivors: 50 (31 to 77) pg/mL; non-survivors: 84 (48 to 232) pg/mLP <0.001
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pro-ENK outperforms creatinine clearance in predicting 7-day mortality
pro-ENK increases with severity of AKI as determined by RIFLE stage
Despite the high correlation between pro-ENK and creatinine clearance, pro-ENK was a better predictor of mortality within 7 days (pro-ENK: χ2 13.4, p < 0.001, AUC 0.69 vs. creatinine clearance: χ2 4, p = 0.045, AUC 0.61)
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Potential of an ideal sepsis marker
•Support the diagnosis – infectious or other
causes of systemic inflammation
•Prognosis –risk stratification
• Impacts measures for infection control –
antimicrobials
• Contributes to improved patient outcome
• Reflect the course of the immunopathology of
sepsis identifies suitable patient populations for
sepsis trials
![Page 16: Biomarkers identifying sepsis phenotypes · Biomarkers identifying sepsis phenotypes K. Reinhart ISF Toronto October 2015. A biomarker may serve one or more of five overlapping roles](https://reader030.vdocuments.us/reader030/viewer/2022041302/5e11c87d2c00e7791a213a35/html5/thumbnails/16.jpg)
Meta-analysis 14 RCTs comparing PCT guided antibiotic use v.s.
standard care Impact of PCT guided antibiotic therapy.
Schuetz P et al Schuetz et al Arch Intern Med. 2011
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•Number of total patients: > 4500
Meta-analysis 14 RCTs comparing PCT guided antibiotic use v.s.
standard care Impact of PCT guided antibiotic therapy.
Schuetz P et al Procalcitonin algorithmsfor antibiotic therapy decisions: a systematic review of randomizedcontrolled trials and recommendations for clinical algorithms.Schuetz et al Arch Intern Med. 2011;171(15):1322–31.
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PCT-guided AB-Therapy in Sepsis on the ICU
Nobre V, AJRCCM 07
6d 10d
Shorter Antibiotic Duration
Shorter ICU stay
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Potential of an ideal sepsis marker
•Support the diagnosis – infectious or other
causes of systemic inflammation
•Prognosis –risk stratification
• Impacts measures for infection control –
antimicrobials
• Contributes to improved patient outcome
• Reflect the course of the immunopathology of
sepsis and identifies suitable patient populations
for sepsis trials
![Page 20: Biomarkers identifying sepsis phenotypes · Biomarkers identifying sepsis phenotypes K. Reinhart ISF Toronto October 2015. A biomarker may serve one or more of five overlapping roles](https://reader030.vdocuments.us/reader030/viewer/2022041302/5e11c87d2c00e7791a213a35/html5/thumbnails/20.jpg)
PCT-Guided Algorithm
PCT to Increase Early Appropriate Antibiotics
and Improve Survival – the PASS Trial
“alert PCT value” = 1.0 ng/mL and not
decreasing by at least 10% from the previous day
Deescalation only when PCT <1.0 ng/ml for at
least 3 days
Jensen JU et al. Crit Care Med 2011 Vol. 39, No. 9
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Jensen JU et al. Crit Care Med 2011 Vol. 39, No. 9
PCT to Increase Early Appropriate Antibiotics
and Improve Survival – the PASS Trial
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Jensen JU et al. Crit Care Med 2011 Vol. 39, No. 9
PCT to Increase Early Appropriate Antibiotics
and Improve Survival – the PASS Trial
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The SISPCT-study, a multicenter,
randomized, controlled, bifactorial trial
patient n= 1089
•Primary hyopthesis: High dose selenium and
PCT guided sepsis therapy result in
reduction in 28 day all cause mortality
•Secondary hypothesis: PCT monitoring
results in a reduction in antibiotic exposure
and more effective detection of the source of
sepsis
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PCT-Algorithm SepNet SISPCT
Trial Baseline PCTmax. value of day 0 and day 1
after randomization
Assessment day 4 Assessments day 7, 10, 14
Stopping of antibioticsrecommended
Diagnostic work-up/ change in antibiotics recommended
Decrease PCT <50%from baseline?
PCT <1 ng/ml ordecrease ≥50% from last measurement?
No changein therapy
yes
yes
no
no
Bloos et al submitted
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Overall Survival – PCT
0 14 28 42 56 70 84 98
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Overa
ll S
urv
ival
Day after randomisation
log rank test p = 0.6273
Patients at risk
not PCT guided
PCT guided
537 427 381 355 338 321 0334
552 454 413 368 354 336 1342
PCT guided
not PCT guided
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Secondary outcomes
• PCT-guidance did not affect resource allocation measured
by frequency of diagnostic or therapeutic procedures
• PCT-guidance did result in a reduction of antibiotic
exposure per 1000 patient-days by 4.5% (p=0.021).
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Subgroup Analysis – PCT28 day all cause mortality
0.38 1 2.71 7.39 20.09 54.60
Adjusted Odds Ratio
Intention to treat
PPS Selen
IMP for at least 48 hours
Medical Patients
Surgical Patients
APACHE II Score < 25 points
APACHE II Score >= 25 points
Severe Sepsis
Septic Shock
Less then 3 OD at inclusion
At least 3 OD at inclusion
Primary Focus pulmonal
Primary Focus Abdominal
0.9 ( 0.7; 1.2) 0.625
adjusted
OR (95% CI) p-value
1.0 ( 0.7; 1.3) 0.880
0.9 ( 0.6; 1.2) 0.406
0.8 ( 0.5; 1.2) 0.264
1.1 ( 0.7; 1.6) 0.722
1.0 ( 0.6; 1.6) 0.959
0.9 ( 0.6; 1.3) 0.526
2.2 ( 0.9; 5.6) 0.086
0.8 ( 0.6; 1.1) 0.246
1.1 ( 0.5; 2.1) 0.839
0.9 ( 0.7; 1.3) 0.626
1.0 ( 0.6; 1.5) 0.961
1.0 ( 0.6; 1.6) 0.914
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Compliance with PCT algorithm
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Potential of an ideal sepsis marker
•Support the diagnosis – infectious or other
causes of systemic inflammation
•Prognosis –risk stratification
• Impacts measures for infection control –
antimicrobials
• Contributes to improved patient outcome
• Reflects the course of the immunopathology of
sepsis and identifies suitable patient
populations for sepsis trials
![Page 30: Biomarkers identifying sepsis phenotypes · Biomarkers identifying sepsis phenotypes K. Reinhart ISF Toronto October 2015. A biomarker may serve one or more of five overlapping roles](https://reader030.vdocuments.us/reader030/viewer/2022041302/5e11c87d2c00e7791a213a35/html5/thumbnails/30.jpg)
MAK 195F 008 (RAMSES)
Survival by IL-6 Test Result - RAMSES Trial
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Placebo
Afelimomab
MONARCS TRIAL:Probability of Mortality Versus Baseline IL-6 by ELISA
Baseline Medgenix IL-6 (pg/mL)
0 10,000 20,000
Pro
bab
ilit
y o
f M
ort
ality
(Ad
juste
d M
ort
ality
)
0.70
0.65
0.60
0.55
0.50
0.45
0.40
0.35
0.30
0.25
Population: ITT
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Approaches to the identification
of biomarkers
• Selection on the basis of a biologically compelling
association with a disease state or a candidate therapeutic
intervention i.e endotoxin, TNF, IL 6
• Serendipitously on the basis of an apparent association
with a disease in the absence of a biologically plausible link
i.e PCT
• Unbiased approaches using high dimensional “omic”
approaches to identify those species that are
differentially expressed in the population of interest.
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Bauer et al in revison
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Bauer et al in revision
Verification data set
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Gene Description Function Implicated pathway Expression in severe
inflammation
TLR 5 Toll-like receptor 5
(flagellin)
Pathogen
recognition
Toll-like receptor
Signaling
Up-regulation
CD59 Protectin Complement
regulatory protein
Fcy receptor
mediated
phagocytosis
Up-regulation
CLU Clusterin Complement lysis
inhibitor
Integrin signaling Up-regulation
FGL2 Fibrinogen-like 2 Immune
regulator,
Prothrombinase
Nur77 signaling in T
lymphocytes
Down-regulation
IL7R Interleukin-7 receptor Lymphocyte
development
CD28 signaling in T
Helper Cells
Down-regulation
HLA-DPA1 Major histocompatibility
complex class II, DP
alpha1
Antigen
presentation
Allograft rejection
signaling
Down-regulation
CPVL Carboxypeptidase,
vitellogenic-like
Macrophages,
inflammatory
protease cascade,
phagocytosis
Calcium-induced T
lymphocyte
apoptosis
Down-regulation
Bauer et al in revision
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Bauer et al in revision
Verification data set
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Bauer et al in revisoin
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Days on the ICU
GES-Score versus conventional biomarkers
0 50 1
00 1
50
0 2
0 40 6
0 8
0
GE
S
0,5
1
,0 1
,5 2
,0 2,5
PC
T [
ng/m
l]
CR
P [
mg/m
l]
2 4 6 8 10
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Confirmation data set
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Summary
•Single sepsis biomarkers help identifying sepsisphenotypes: severity, differentiation SIRS/Sepsis, control of infection
•Gene expressions scores and other „omics“ basedapproaches may help to better understand theimmune pathology of sepsis
•May help to guide sepsis specific therapies
•Multiplexed markers hold the promise to improve the evaluation of novel adjunctive sepsis therapies
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“ Sepsis biomarkers may help to
transform sepsis from a
physiologic syndrome to a group
of distinct biochemical
disorders”
![Page 42: Biomarkers identifying sepsis phenotypes · Biomarkers identifying sepsis phenotypes K. Reinhart ISF Toronto October 2015. A biomarker may serve one or more of five overlapping roles](https://reader030.vdocuments.us/reader030/viewer/2022041302/5e11c87d2c00e7791a213a35/html5/thumbnails/42.jpg)
Join
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