Sepsis in the era of data-driven medicine: personalizing risks, diagnoses, treatments and prognoses

Abstract

Sepsis is a series of clinical syndromes caused by the immunological response to infection. The clinical evidence for sepsis could typically attribute to bacterial infection or bacterial endotoxins, but infections due to viruses, fungi or parasites could also lead to sepsis. Regardless of the etiology, rapid clinical deterioration, prolonged stay in intensive care units and high risk for mortality correlate with the incidence of sepsis. Despite its prevalence and morbidity, improvement in sepsis outcomes has remained limited. In this comprehensive review, we summarize the current landscape of risk estimation, diagnosis, treatment and prognosis strategies in the setting of sepsis and discuss future challenges. We argue that the advent of modern technologies such as in-depth molecular profiling, biomedical big data and machine intelligence methods will augment the treatment and prevention of sepsis. The volume, variety, veracity and velocity of heterogeneous data generated as part of healthcare delivery and recent advances in biotechnology-driven therapeutics and companion diagnostics may provide a new wave of approaches to identify the most at-risk sepsis patients and reduce the symptom burden in patients within shorter turnaround times. Developing novel therapies by leveraging modern drug discovery strategies including computational drug repositioning, cell and gene-therapy, clustered regularly interspaced short palindromic repeats -based genetic editing systems, immunotherapy, microbiome restoration, nanomaterial-based therapy and phage therapy may help to develop treatments to target sepsis. We also provide empirical evidence for potential new sepsis targets including FER and STARD3NL. Implementing data-driven methods that use real-time collection and analysis of clinical variables to trace, track and treat sepsis-related adverse outcomes will be key. Understanding the root and route of sepsis and its comorbid conditions that complicate treatment outcomes and lead to organ dysfunction may help to facilitate identification of most at-risk patients and prevent further deterioration. To conclude, leveraging the advances in precision medicine, biomedical data science and translational bioinformatics approaches may help to develop better strategies to diagnose and treat sepsis in the next decade.

Keywords: computational medicine; genome informatics; precision medicine; sepsis; translational bioinformatics.

Figure 1

Translational bioinformatics framework for developing multi-analyte, heterogeneous, data-driven diagnostic aid for sepsis.

Figure 2

Emerging targets, pathways from genome-wide association studies of sepsis phenotypes. (A) Molecular neighborhood of FER protein; red nodes are proteins implicated in regulation of mast cell degranulation. (B) Genome-wide associations of variants implicated in response to sepsis therapy. (C) Biological processes mediated by the molecular neighborhood of STARD3NL. (D) First-degree interactome of STARD3NL.

Figure 3

A personalized genomic framework for developing a polygenic risk for sepsis and associated outcomes.

Figure 4

Homology modeling of FER protein, a putative target for therapeutic response in sepsis.

Figure 5

Emerging immunotherapy opportunities in the setting of sepsis.