Methods
Overall Study Design
All analyses of plasma samples and clinical data are based on secondary use of existing data from previous studies, with approval of the respective institutional review boards.
Derivation Cohort
Derivation cohort study subjects (n = 341) were participants in the Vasopressin and Septic Shock Trial (VASST), a randomized, concealed, norepinephrine-controlled trial testing the efficacy of low-dose vasopressin versus norepinephrine in adults with septic shock (Current Controlled Trials number: ISRCTN9485869). The original VASST publication describes all protocol details.
Test Cohort
Test cohort study subjects (n = 331) were pooled from two sources. Two hundred and forty-three subjects were participants in a prospective, observational, multicenter cohort study of prevalence and outcome of severe sepsis and septic shock in Finland (FINNSEPSIS). An additional 88 subjects were participants in a single center, observational study at St. Paul's Hospital in Vancouver, British Columbia.
Validation Cohort
Validation cohort study subjects (n = 209) were participants in the Molecular Epidemiology of Severe Sepsis in the Intensive Care Unit study, an ongoing cohort study at the Hospital of the University of Pennsylvania. Eligible patients with septic shock were enrolled in either the emergency department or the medical ICU, and patients or their proxies provided informed consent. Septic shock was defined using published criteria.
Candidate Stratification Biomarkers
The 12 candidate biomarkers (gene symbols) included C-C chemokine ligand 3 (CCL3), C-C chemokine ligand 4 (CCL4), neutrophil elastase 2 (ELA2), granzyme B (GZMB), heat shock protein 70 kDa 1B (HSPA1B), interleukin-1[alpha] (IL1A), interleukin-8 (IL8), lipocalin 2 (LCN2), lactotransferrin (LTF), matrix metallopeptidase 8 (MMP8), resistin (RETN), and thrombospondin 1 (THBS1). These biomarkers were selected from 117 gene probes previously shown to have predictive strength for poor outcomes in microarray-based studies involving children with septic shock. Final biomarker selection was based on a priori criteria: 1) the gene product (i.e., protein) has biological and mechanistic plausibility regarding the host response to infection, immunity, and/or inflammation, and 2) the gene product is readily measured in the blood compartment.
All plasma samples were collected within the first 24 hours of presentation to the ICU. The plasma concentrations of the candidate biomarkers were measured using a multiplex magnetic bead platform (MILLIPLEX MAP, EMD Millipore Corporation, Billerica, MA) and a Luminex 100/200 System (Luminex Corporation, Austin, TX) according to the manufacturers' specifications. Technical assay performance data were previously published.
Additional Stratification Variables
We abstracted available data elements for consideration in the risk modeling that, based on existing literature, we hypothesized could be associated with poor outcomes: serum lactate concentration (mmol/L) at study entry, age, gender, and Acute Physiology and Chronic Health Evaluation (APACHE) II/III score. We also recorded the presence of the following comorbid conditions: New York Heart Association Class IV congestive heart failure, chronic obstructive pulmonary disease, requirement for chronic dialysis, chronic hepatic failure, hematologic or metastatic solid organ malignancy, and requirement for chronic steroids at study entry. We derived a binary "chronic disease" variable to indicate the presence of any one of these comorbidities.
Statistical Analysis
Initially, data are described using medians, interquartile ranges (IQRs), frequencies, and percentages. Comparisons between survivors and nonsurvivors used the Mann-Whitney U test, chi-square test, or Fisher exact test as appropriate. Descriptive statistics and comparisons used SigmaStat Software (Systat Software, San Jose, CA).
All-cause 28-day mortality is the primary outcome variable for the modeling procedures. To derive the decision tree, we employed a classification and regression tree (CART) approach. The CART analysis procedure considered all 12 candidate biomarkers as well as other potential clinical predictor variables listed above. The procedure selects cut points and ordering of decision nodes that maximally discriminate between survivors and nonsurvivors. The tree was built using Salford Predictive Modeler v6.6 (Salford Systems, San Diego, CA). Performance of the tree is reported using diagnostic test statistics with 95% CIs computed using the VassarStats Website for Statistical Computation. Areas under the receiver operating characteristic (ROC) curves were compared using the method of Hanley and McNeil. The net reclassification improvement (NRI) was also used to estimate the incremental predictive ability of the biomarker-based model compared to using APACHE II scores alone. The NRI was computed using the R-package Hmisc.