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The Genomics of Septic Shock. Hector R. Wong, MD Division of Critical Care Medicine Cincinnati Children’s Hospital Medical Center Cincinnati Children’s Hospital Research Foundation.

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the genomics of septic shock

The Genomics of Septic Shock

Hector R. Wong, MD

Division of Critical Care Medicine

Cincinnati Children’s Hospital Medical Center

Cincinnati Children’s Hospital Research Foundation

1st International Symposium on AKI in Children at the 7th International Conference on Pediatric Continuous Renal Replacement Therapy

September 2012

disclosures
Disclosures
  • The Cincinnati Children’s Hospital Research Foundation and the Speaker have submitted patent applications for biomarker-based stratification model presented in this lecture.
  • The Speaker serves on the Scientific Advisory Board for DxTerity and is compensated with stock options.
nine years of genome level expression profiling in pediatric septic shock
Nine years of genome-level expression profiling in pediatric septic shock…..

Discovery-oriented, exploratory genome-wide expression studies in children with septic shock

FUNDAMENTAL OBSERVATIONS REGARDING THE GENOME-LEVEL BIOLOGY OF PEDIATRIC SEPTIC SHOCK

  • DISCOVERY OF NOVEL BIOMARKERS
  • STRATIFICATION
  • DIAGNOSIS

DISCOVERY OF GENE EXPRESSION-BASED CLASSES OF SEPTIC SHOCK WITH CLINICALLY RELEVANT PHENOTYPIC DIFFERENCES

DISCOVERY OF NOVEL CANDIDATE THERAPEUTIC TARGETS

nine years of genome level expression profiling in pediatric septic shock1
Nine years of genome-level expression profiling in pediatric septic shock…..

Discovery-oriented, exploratory genome-wide expression studies in children with septic shock

FUNDAMENTAL OBSERVATIONS REGARDING THE GENOME-LEVEL BIOLOGY OF PEDIATRIC SEPTIC SHOCK

  • DISCOVERY OF NOVEL BIOMARKERS
  • STRATIFICATION
  • DIAGNOSIS

DISCOVERY OF GENE EXPRESSION-BASED CLASSES OF SEPTIC SHOCK WITH CLINICALLY RELEVANT PHENOTYPIC DIFFERENCES

DISCOVERY OF NOVEL CANDIDATE THERAPEUTIC TARGETS

stratification
Stratification
  • Early assessment (i.e. within 24 hours of admission) of who is at risk for good or poor outcome.
why do we care
Why Do We Care?
  • Reliable outcome risk stratification is fundamental for effective clinical practice and clinical research.
  • The oncology paradigm.
  • Stratification for clinical trials.
  • Informing individual patient decision making.
  • Allocation of ICU resources.
  • Quality metric.
  • There is no reliable and validated outcome risk stratification tool for septic shock.
discovery of candidate stratification biomarkers for septic shock
Discovery of candidate stratification biomarkers for septic shock

Mining of genome-wide expression data to identify genes associated with 28-day mortality in children with septic shock.

117 genes with predictive capacity for mortality

  • 12 gene products meeting the following criteria:
  • Biological plausibility regarding sepsis biology.
  • Gene product (i.e. protein) can be measured in serum/plasma.
persevere
PERSEVERE
  • PEdiatRic SEpsis biomarkEr Risk modEl.
  • Multi-biomarker-based risk model to predict outcome in septic shock.
derivation of persevere
Derivation of PERSEVERE
  • 220 patients with septic shock.
  • 10.5% mortality.
  • Measured 12 candidate stratification biomarkers from serum.
  • Serum samples represent the first 24 hours of admission to the PICU.
  • “CART” analysis.
cart analysis
CART Analysis
  • Classification and Regression Tree.
  • Decision tree building technique.
  • “Binary recursive partitioning”.
  • Binary: splitting of patients into 2 groups.
  • Recursive: can be done multiple times.
  • Partitioning: entire dataset split into sections.
  • Has the potential to reveal complex interactions between candidate predictor variables not evident using traditional approaches.
derivation cohort cart analysis results overview
Derivation Cohort CART AnalysisResults Overview
  • Included 5 of the 12 candidate biomarkers.
    • CCL3: MIP-1α
    • Heat shock protein-70
    • IL-8
    • Elastase
    • NGAL
  • 5 decision rules
  • 10 daughter nodes
slide22

Test characteristics based on terminal nodes.

All subjects in low risk nodes predicted as survivors.

All subjects in high risk nodes predicted as non-survivors.

slide23

Test characteristics based on terminal nodes.

All subjects in low risk nodes predicted as survivors.

All subjects in high risk nodes predicted as non-survivors.

PPV 43% (CI 29 to 58%)

+LR 6.4 (CI 4.5 to 9.3)

NPV 99% (CI 95 to 100%)

-LR 0.10 (CI 0.03 to 0.4)

Sensitivity

91%

CI 70 to 98%

Specificity

86%

CI 80 to 80%

AUC = 0.885

testing persevere
Testing PERSEVERE
  • 135 different patients with septic shock.
  • 13.3% mortality.
  • Measured the same candidate biomarkers.
  • “Dropped the patients through the tree”.
slide26

Test characteristics in the test cohort

PPV 28% (CI 17 to 41%)

+LR 2.5 (CI 1.8 to 3.3)

NPV 97% (CI 90 to 99%)

-LR 0.18 (CI 0.05 to 0.69)

Sensitivity

89%

CI 64 to 98%

Specificity

64%

CI 55 to 73%

AUC = 0.759

updated model
Updated Model
  • Included 3 of the 5 candidate biomarkers from the initial model.
    • CCL3: MIP-1α
    • Heat shock protein-70
    • IL-8
  • Eliminated 2 of the 5 candidate biomarkers from the original model.
    • Elastase
    • NGAL
  • Added granzyme B, MMP-8, & age as decision rules.
  • 7 decision rules.
  • 14 daughter nodes.
slide30

High risk terminal nodes

N = 119

Death risk: 18.2 to 62.5%

slide31

Low risk terminal nodes

N = 236

Death risk: 0.0 to 2.5%

slide33

Test characteristics of updated model

PPV 32% (CI 24 to 41%)

+LR 3.6 (CI 2.9 to 4.4)

NPV 99% (CI 96 to 100%)

-LR 0.1 (CI 0.0 to 0.3)

Sensitivity

93%

CI 79 to 98%

Specificity

74%

CI 69 to 79%

AUC = 0.883

slide34

Biologically Plausible?

False Positives

True Negatives

False positives should be “sicker” than true negatives.

slide35

Persistence of ≥2 organ failures at 7 days after ICU admission

False Positives: 30%

P < 0.001

True Negatives: 9%

slide36

Median PICU Length of Stay

False Positives: 11 days

P = 0.003

True Negatives: 7 days

potential questions you may have
Potential questions you may have...
  • Manuscript in press: Crit Care.
  • Derived an analogous model in adults.
  • Outperforms PRISM.
  • Have evaluated the performance of the updated tree in 54 new patients (13% mortality).
    • Correctly predicted 6 of 7 deaths (86% sensitivity).
    • 33 of 34 predicted survivors actually survived (97% NPV).
potential applications of persevere
Potential applications of PERSEVERE
  • Stratification for clinical trials.
  • Inform individual patient decision making.
  • Allocation of ICU resources.
  • Quality improvement.
slide39

Acknowledgements: Contributing Centers

  • Natalie Cvijanovich, MD: Children’s Hospital & Research Center Oakland, Oakland, CA.
  • Thomas Shanley, MD: University of Michigan, C.S. Mott Children’s Hospital, Ann Arbor, MI.
  • Geoffrey Allen, MD: Children’s Mercy Hospitals & Clinics, Kansas City, MO.
  • Neal Thomas, MD: Penn State Hershey Children’s Hospital, Hershey, PA.
  • Robert Freishtat, MD: Children’s National Medical Center, Washington, DC.
  • Nick Anas, MD: Children’s Hospital of Orange County, Orange, CA.
  • Keith Meyer, MD: Miami Children’s Hospital, Miami, FL.
  • Paul Checchia, MD: Texas Children’s Hospital, Houston, TX.
  • Richard Lin, MD: The Children’s Hospital of Philadelphia, Philadelphia, PA.
  • Michael Bigham, MD: Akron Children’s Hospital, Akron, OH.
  • Mark Hall, MD: Nationwide Children’s Hospital, Columbus, OH.
  • Anita Sen, MD: New York-Presbyterian, Morgan Stanley Children’s Hospital, Columbia University Medical Center, New York, NY.
  • Jeffery Nowak, MD: Children’s Hospital and Clinics of Minnesota, Minneapolis, MN.
  • Michael Quasney, MD, PhD: Children’s Hospital of Wisconsin, Milwaukee, WI.
  • Jared Henricksen, MD: Primary Children’s Medical Center, Salt Lake, UT.
  • Arun Chopra, MD: St. Christopher’s Hospital for Children, Philadelphia, PA.
slide40

Funding Acknowledgement

  • NIH R01GM064619
  • NIH RC1HL100474
  • NIH R01GM096994