Development of a genomic metric that can be rapidly used to predict clinical outcome in severely injured trauma patients

Abstract

Objective: Many patients have complicated recoveries following severe trauma due to the development of organ injury. Physiological and anatomical prognosticators have had limited success in predicting clinical trajectories. We report on the development and retrospective validation of a simple genomic composite score that can be rapidly used to predict clinical outcomes.

Design: Retrospective cohort study.

Setting: Multi-institutional level 1 trauma centers.

Patients: Data were collected from 167 severely traumatized (injury severity score >15) adult (18-55 yr) patients.

Methods: Microarray-derived genomic data obtained from 167 severely traumatized patients over 28 days were assessed for differences in messenger RNA abundance among individuals with different clinical trajectories. Once a set of genes was identified based on differences in expression over the entire study period, messenger RNA abundance from these subjects obtained in the first 24 hours was analyzed in a blinded fashion using a rapid multiplex platform, and genomic data reduced to a single metric.

Results: From the existing genomic dataset, we identified 63 genes whose leukocyte expression differed between an uncomplicated and complicated clinical outcome over 28 days. Using a multiplex approach that can quantitate messenger RNA abundance in less than 12 hours, we reassessed total messenger RNA abundance from the first 24 hours after trauma and reduced the genomic data to a single composite score using the difference from reference. This composite score showed good discriminatory capacity to distinguish patients with a complicated outcome (area under a receiver-operator curve, 0.811; p <0.001). This was significantly better than the predictive power of either Acute Physiology and Chronic Health Evaluation II or new injury severity score scoring systems.

Conclusions: A rapid genomic composite score obtained in the first 24 hours after trauma can retrospectively identify trauma patients who are likely to develop complicated clinical trajectories. A novel platform is described in which this genomic score can be obtained within 12 hours of blood collection, making it available for clinical decision making.

Figure 1

Heat map and calculated DFR for the 63 genes that distinguish clinical trajectory. Using a false discovery adjusted probability of <0.001 and a 2-fold difference in expression, the temporal pattern of expression of 63 genes that differed between patients with the two clinical outcomes is presented. A. Cluster analysis of the two cohorts B. Summary of the difference from reference (DFR) score calculated for each patient in the uncomplicated and complicated cohorts at each time point. Statistical analysis at each time point (0, 1, 4, 7, and 14 days) revealed significant differences in DFR between complicated and uncomplicated patients (p<0.05, Mann Whitney Rank analysis).

Figure 2

Comparison of gene expression patterns and variation in human whole blood ex vivo stimulated with bacterial lipopolysaccharide. A single sample was divided and one-half stimulated with 100 ng/ml of E. coli LPS for two hours at 37° C. Total RNA was isolated from the two preparations and divided into ten equal aliquots, five analyzed by microarray, and five analyzed with the nanoString^™ technology. A and B. Heat maps of the hierarchical clustering of the gene expression data for the two technologies, revealing that both methodologies easily detected the change in gene expression changes induced by LPS (U=Unstimulated samples; S=Stimulated samples) C. Pearson correlation coefficients for the change in gene expression of the 63 genes produced by the LPS stimulation. Values represent fold change over control expression values.

Figure 3

Pearson correlation between the microarray and nanoString^™ expression level of the 63 genes found to be differentially regulated between uncomplicated and complicated patient cohorts. Values represent fold change over control expression values.

Figure 4

Receiver operator curves (ROC) demonstrating the ability of nanoString^™ versus microarray platforms to discriminate between clinical outcome. A. ROC curves for comparison of univariate analysis. B. ROC curves for comparison of multivariate analysis. AUC for nanoString DFR is significantly higher than AUC for ISS, NISS, and microarray models, with p=0.005, 0.0009, and 0.0011 respectively. Adding nanoSpring DFR to APACHE II and ISS model increases AUC significantly from 0.66 to 0.81 (p=0.0026). AUC for model with APACHE II, ISS, and nanoString DFR is significantly higher than AUC of the model with APACHE II, ISS, and mcicroarray DFR (p=0.0008).