Multiplexed Plasma Immune Mediator Signatures Can Differentiate Sepsis From NonInfective SIRS: American Surgical Association 2020 Annual Meeting Paper

Abstract

Objectives: Sepsis and sterile both release "danger signals' that induce the systemic inflammatory response syndrome (SIRS). So differentiating infection from SIRS can be challenging. Precision diagnostic assays could limit unnecessary antibiotic use, improving outcomes.

Methods: After surveying human leukocyte cytokine production responses to sterile damage-associated molecular patterns (DAMPs), bacterial pathogen-associated molecular patterns, and bacteria we created a multiplex assay for 31 cytokines. We then studied plasma from patients with bacteremia, septic shock, "severe sepsis," or trauma (ISS ≥15 with circulating DAMPs) as well as controls. Infections were adjudicated based on post-hospitalization review. Plasma was studied in infection and injury using univariate and multivariate means to determine how such multiplex assays could best distinguish infective from noninfective SIRS.

Results: Infected patients had high plasma interleukin (IL)-6, IL-1α, and triggering receptor expressed on myeloid cells-1 (TREM-1) compared to controls [false discovery rates (FDR) <0.01, <0.01, <0.0001]. Conversely, injury suppressed many mediators including MDC (FDR <0.0001), TREM-1 (FDR <0.001), IP-10 (FDR <0.01), MCP-3 (FDR <0.05), FLT3L (FDR <0.05), Tweak, (FDR <0.05), GRO-α (FDR <0.05), and ENA-78 (FDR <0.05). In univariate studies, analyte overlap between clinical groups prevented clinical relevance. Multivariate models discriminated injury and infection much better, with the 2-group random-forest model classifying 11/11 injury and 28/29 infection patients correctly in out-of-bag validation.

Conclusions: Circulating cytokines in traumatic SIRS differ markedly from those in health or sepsis. Variability limits the accuracy of single-mediator assays but machine learning based on multiplexed plasma assays revealed distinct patterns in sepsis- and injury-related SIRS. Defining biomarker release patterns that distinguish specific SIRS populations might allow decreased antibiotic use in those clinical situations. Large prospective studies are needed to validate and operationalize this approach.

FIGURE 1.

Measurement of cytokines and chemokines in plasma reveals a set of 10 that differed among controls, patients with injury and patients with infection. (A) Heat map of log-transformed group medians for each analyte (log₁₀ pg/mL). Asterisks indicate analytes for which a Kruskal-Wallis test with Benjamini-Hochberg false discovery rate (FDR) correction indicates that all 3 groups are not likely to be drawn from the same distribution. (*FDR <0.05; **FDR <0.01; ***FDR <0.001; ****FDR <0.0001). (B) Box-and-whiskers plots are presented indicating the distributions within each group for analytes with FDR <0.05 by Benjamini-Hochberg-corrected Kruskal-Wallis test. Median, 25^th, and 75^th percentile values for each analyte are indicated by heavy horizontal black bar, and box bottoms and tops, respectively. Whiskers extend to the furthest value from the median on each side that is no more than 1.5 times the interquartile range to the hinge. All individual analyte values are plotted on a log-transformed scale. Brackets indicate the FDR of a Wilcoxon test between pairs of groups, with Benjamini-Hochberg correction across the three intergroup comparisons for each analyte (*FDR <0.05; **FDR <0.01; ***FDR <0.001; ****FDR <0.0001).

FIGURE 2.

Partial least squared discriminant analysis reveals linear combinations of measured cytokines and chemokines (latent variables) that covary maximally with group identity. (A, B)The scores of individual patient samples along latent variables 1 and 2 (A) and 3 and 4 (B) are shown for a PLS-DA model intended to find latent variables separating 3 patient groups: control, injury, and infection. (C) Box-and-whiskers plot (as in Fig. 1B) indicating the distributions within each group for the first 4 latent variables of a 3-population PLS-DA model. (D) The scores of individual patient samples along the latent variables 1 and 2 are shown for a 2-population PLS-DA model intended to find latent variables separating patients with injury from patients with infection. (E) Box-and-whiskers plot (as in Fig. 1B) indicating the distributions within each group for the first 4 latent variables of a 2-population PLS-DA model. (F) Loadings plot detailing the contribution of each analyte to the first latent variable of a 2-population PLSDA model built to distinguish patients with injury-related SIRS from patients with infection. Each analyte is colored based on whether a high value contributes to a prediction of noninfective (green) or infective (yellow) SIRS.

FIGURE 3.

Random forest models use voting by a large number of decision trees to predict the group to which individual patient samples belong. (A, B) Distributions of predictions made on patient samples from each class by random forest models is plotted as a stacked bar, for an RF model built to predict either all three patient groups (A) or only to differentiate injury from infection (B). Only the subset of decision trees not trained on each sample are then used to make a prediction on that sample. The result is an out-of-bag prediction on the rate of errors that would be expected in new samples. This error rate of 97.5% is consistent with that identified by cross-validation of 96.3%. (C) The relative importance of each analyte to a 2-group random forest model for distinguishing samples from patients with injury and patients with infection. For each analyte, importance is calculated as the decrease in Gini index gained by splitting on the analyte, averaged across trees. (D) Here we show 3 (of 500) decision trees created as part of a random forest model for distinguishing samples from patients with injury and patients with infection. Within each tree, a subset of analyte levels are queried sequentially, with the result leading either to a new subquery or to a prediction of what class the sample is from. CON, control; INJ, injury; INF, infection.