Sepsis Diagnostics: From Discovery to Application*
Sweeney and Khatri (4) are to be commended for their innovative approach in this burgeoning new era of “precision medicine” where the goal is to improve treatment, diagnosis, and prevention of disease using massive repositories of “omics” data. The authors have moved the sepsis field forward by capturing terabytes of genomics and clinical data in the public domain originally created for other purposes. For example, much of the data in this meta-analysis came from the Glue Grant, which generated high-throughput genomic, proteomic, and observational clinical data in an effort to define the normal and pathologic human response to trauma and systemic inflammation (8). Using novel statistical and bioinformatics approaches, the authors were able to include 39 different clinical datasets from 2,604 patients into a single unified database that can be parsed for knowledge. Their model allows for the combination of genomics data from multiple platforms while minimizing technical variation.
A significant limitation in the development of biomarkers, especially genomic, has been the inability to reproduce findings, and the failure to validate the initially identified metrics. The reasons are multifactorial but are common to small datasets, and range from differences in the analytical platform, to overfitting of the data by the large number of variables collected. Using statistical approaches to minimize analytical bias, Sweeney and Khatri (4) have created a genomic metric (MetaScore) that appears sufficiently robust to maintain predictive ability across a variety of different clinical conditions using a number of different platforms. The authors are to be congratulated for providing to the public domain, a curated repository of these data and the tools for others to evaluate potentially new sepsis genomic biomarkers (http://khatrilab.stanford.edu/sepsis).
This study represents an outstanding beginning to an understandably long journey to its clinical use in the potentially septic patient. Nevertheless, this process presently remains discovery-driven and must transition to an application-driven approach to achieve its goal. The next steps will require prospective validation in a variety of patient populations to ensure reproducibility and, more importantly, to identify whether or not these diagnostic tools can impact patient management. Furthermore, the current genomic tests favor a high sensitivity at the cost of specificity, increasing the false positive rate. While we agree that a false negative, potentially not treating an infected patient, could have lethal ramifications, a high false positive rate or treating noninfected patients has its own consequences.