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A Multibiomarker-Based Outcome Risk Stratification Model for Adult Septic Shock*

Wong, Hector R. MD1,2; Lindsell, Christopher J. PhD3; Pettilä, Ville MD, PhD4; Meyer, Nuala J. MD, MS5; Thair, Simone A. BSc6,7; Karlsson, Sari MD, PhD8; Russell, James A. MD6,7; Fjell, Christopher D. PhD6,7; Boyd, John H. MD6,7; Ruokonen, Esko MD, PhD9; Shashaty, Michael G. S. MD, MS5; Christie, Jason D. MD5,10; Hart, Kimberly W. MS3; Lahni, Patrick BS1; Walley, Keith R. MD6,7

doi: 10.1097/CCM.0000000000000106
Feature Articles

Objectives: Clinical trials in septic shock continue to fail due, in part, to inequitable and sometimes unknown distribution of baseline mortality risk between study arms. Investigators advocate that interventional trials in septic shock require effective outcome risk stratification. We derived and tested a multibiomarker-based approach to estimate mortality risk in adults with septic shock.

Design: Previous genome-wide expression studies identified 12 plasma proteins as candidates for biomarker-based risk stratification. The current analysis used banked plasma samples and clinical data from existing studies. Biomarkers were assayed in plasma samples obtained from 341 subjects with septic shock within 24 hours of admission to the ICU. Classification and regression tree analysis was used to generate a decision tree predicting 28-day mortality based on a combination of both biomarkers and clinical variables. The derived tree was first tested in an independent cohort of 331 subjects, then calibrated using all subjects (n = 672), and subsequently validated in another independent cohort (n = 209).

Setting: Multiple ICUs in Canada, Finland, and the United States.

Subjects: Eight hundred eighty-one adults with septic shock or severe sepsis.

Intervention: None.

Measurements and Main Results: The derived decision tree included five candidate biomarkers, admission lactate concentration, age, and chronic disease burden. In the derivation cohort, sensitivity for mortality was 94% (95% CI, 87–97), specificity was 56% (50–63), positive predictive value was 50% (43–57), and negative predictive value was 95% (89–98). Performance was comparable in the test cohort. The calibrated decision tree had the following test characteristics in the validation cohort: sensitivity 85% (76–92), specificity 60% (51–69), positive predictive value 61% (52–70), and negative predictive value 85% (75–91).

Conclusions: We have derived, tested, calibrated, and validated a risk stratification tool and found that it reliably estimates the probability of mortality in adults with septic shock.

1Division of Critical Care Medicine, Cincinnati Children’s Hospital Medical Center and Cincinnati Children’s Hospital Research Foundation, Cincinnati, OH.

2Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH.

3Department of Emergency Medicine, University of Cincinnati College of Medicine, Cincinnati, OH.

4Intensive Care Units, Division of Anaesthesia and Intensive Care Medicine, Department of Surgery, Helsinki University Central Hospital, Helsinki, Finland.

5Pulmonary, Allergy, and Critical Care Division, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA.

6University of British Columbia, Vancouver, BC, Canada.

7Critical Care Research Laboratories, Centre for Heart Lung Innovation, St. Paul’s Hospital, Vancouver, BC, Canada.

8Department of Intensive Care Medicine, Tampere University Hospital, Tampere, Finland.

9Department of Intensive Care Medicine, Kuopio University Hospital, Kuopio, Finland.

10Department of Epidemiology, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia, PA.

* See also p. 974.

Dr. Wong conceived and developed the study, obtained funding for the study, directly took part in the analyses, and wrote the article. Dr. Lindsell collaborated with Dr. Wong in the initial design of the study and in obtaining funding, oversaw the statistical analyses, and edited the article. Ms. Thair, Dr. Russell, Dr. Fjell, Dr. Boyd, and Dr. Walley are Vasopressin and Septic Shock Trial investigators and edited the article. Drs. Pettilä, Karlsson, and Ruokonen are FINNSEPSIS investigators and edited the article. Drs. Meyer, Shashaty, and Christie are Molecular Epidemiology of Severe Sepsis in the Intensive Care Unit investigators and edited the article. Ms. Hart assisted in data management and statistical analysis and edited the article. Mr. Lahni conducted all biomarker assays and edited the article.

Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal’s website (http://journals.lww.com/ccmjournal).

Supported, in part, by the National Institutes of Health (NIH) (RC1HL100474, RO1GM064619, and RO1GM099773), an Innovation Award from the Center for Technology Commercialization at the Cincinnati Children’s Hospital Research Foundation, and an Institutional Clinical and Translational Science Award (NIH/National Center for Research Resources) (8UL1 TR000077).

Dr. Wong and the Cincinnati Children’s Hospital Research Foundation have submitted a provisional patent application for the stratification model. Dr. Wong’s institution received grant support from the National Institutes of Health (NIH). Dr. Wong has a patent pending for a biomarker model with U.S. Patent Office and received support for article research from the NIH. Dr. Lindsell’s institution received grant support from the NIH (his contributions to this work were supported, in part, by an institutional Clinical and Translational Science Award [CTSA] from the NIH). Dr. Lindsell and his institution have a patent (he was named as coinventor on a patent for a multibiomarker-based risk stratification model for pediatric sepsis). Dr. Lindsell received support for article research from the NIH. Dr. Meyer’s institution received grant support from the NIH (HL102254 and Submitted R01) and GlaxoSmithKline (research grant to support plasma collection). Dr. Meyer received support for article research from the NIH. Ms. Thair consulted for LKL Consulting and received grant support from University of British Columbia (grant funding for PhD graduate studies). Dr. Russell served as board member for Sirius Genomics; consulted for Ferring, Astra Zeneca, Medimmune, Grifols, and Sirius Genomics; has a patent with the University of British Columbia; and has stock options with Sirius Genomics. Dr. Russel’s institution received grant support from Ferring, Astra Zeneca, and Sirius Genomics. Dr. Shashaty’s institution received grant support from the NIH (Career Development Award to study the association of adiposity with acute kidney injury in critically ill trauma patients). Dr. Christie provided expert testimony for various law firms (expert testimony on asbestos litigation in brake workers) and received support for article research from the NIH (salary is supported by NIH HL115354, HL081619, HL087115). Dr. Christie’s institution received grant support from the NIH and GlaxoSmithKline. Ms. Hart received support for article research from the NIH. Mr. Lahni received support for article research from the NIH. Dr. Walley is employed by the University of British Columbia and received grant support from Canadian Institutes of Health Research. The remaining authors have disclosed that they do not have any potential conflicts of interest.

For information regarding this article, E-mail: hector.wong@cchmc.org

© 2014 by the Society of Critical Care Medicine and Lippincott Williams & Wilkins