The Genomics of Septic Shock

1. 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

2. 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.

3. 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

4. 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

5. Stratification • Early assessment (i.e. within 24 hours of admission) of who is at risk for good or poor outcome.

6. 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.

7. 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.

8. Final list of candidate stratification biomarkers

9. PERSEVERE • PEdiatRic SEpsis biomarkEr Risk modEl. • Multi-biomarker-based risk model to predict outcome in septic shock.

10. 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.

11. 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.

12. 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

13. Derivation Cohort Tree

14. Derivation Cohort Tree

15. Derivation Cohort Tree

16. Derivation Cohort Tree

17. Derivation Cohort Tree

18. Derivation Cohort Tree

19. Derivation Cohort Tree

20. Low Risk Terminal Nodes N = 171

21. High Risk Terminal Nodes N = 49

22. 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.

23. 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

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

25. Test characteristics in the test cohort

26. 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

27. Updating PERSEVERE using the combined derivation and test cohorts (n = 355).

28. 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.

30. High risk terminal nodes N = 119 Death risk: 18.2 to 62.5%

31. Low risk terminal nodes N = 236 Death risk: 0.0 to 2.5%

32. Test characteristics of updated model

33. 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

34. Biologically Plausible? False Positives True Negatives False positives should be “sicker” than true negatives.

35. Persistence of ≥2 organ failures at 7 days after ICU admission False Positives: 30% P < 0.001 True Negatives: 9%

36. Median PICU Length of Stay False Positives: 11 days P = 0.003 True Negatives: 7 days

37. 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).

38. Potential applications of PERSEVERE • Stratification for clinical trials. • Inform individual patient decision making. • Allocation of ICU resources. • Quality improvement.

39. 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.

40. Funding Acknowledgement • NIH R01GM064619 • NIH RC1HL100474 • NIH R01GM096994

41. Thank you