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Brief Bioinform. 2019 Jun 11. pii: bbz059. doi: 10.1093/bib/bbz059. [Epub ahead of print]

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

Author information

1
Department of Information Services, Northwell Health, New Hyde Park, NY, USA.
2
Donald and Barbara School of Medicine at Hofstra/Northwell, Northwell Health, Hempstead, NY, USA.
3
Center for Research Informatics and Innovation, Northwell Health, New Hyde Park, NY, USA.
4
Courant Institute of Mathematical Sciences, New York University, New York, NY, USA.
5
Department of Genetics and Genomic Sciences, Mount Sinai Health System, New York, NY, USA.
6
Institute for Next Generation Healthcare, Mount Sinai Health System, New York, NY, USA.
7
Stonybrook University, 100 Nicolls Rd, Stony Brook, NY, USA.
8
Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA.
9
Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.
10
School of Biotechnology and Bioinformatics, D Y Patil University, Navi Mumbai, India.
11
TBC, B3, JKR, Aluva, Kerala, India.
12
University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA.

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

PMID:
31190075
DOI:
10.1093/bib/bbz059

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