Predicting sepsis severity at first clinical presentation: The role of endotypes and mechanistic signatures.

Arjun Baghela,Claudia C Dos Santos,Juan Diego Rivera,Reza Falsafi,Andy An,Amy H Lee,Susan W Farmer,Carlos Eduardo Jimenez-Canizales,Maryam Shojaei,Olga M Pena,Hjalmar R Bouma,Gabriela V Cohen Freue,Alvaro Mondragon-Cardona,Andrew Baker,Beverlie Baquir,Uriel Trahtemberg,Benjamin Tang,Robert E.W Hancock,Andrew Hurlburt

doi:10.1016/j.ebiom.2021.103776

Abstract

SummaryBackgroundInter-individual variability during sepsis limits appropriate triage of patients. Identifying, at first clinical presentation, gene expression signatures that predict subsequent severity will allow clinicians to identify the most at-risk groups of patients and enable appropriate antibiotic use.MethodsBlood RNA-Seq and clinical data were collected from 348 patients in four emergency rooms (ER) and one intensive-care-unit (ICU), and 44 healthy controls. Gene expression profiles were analyzed using machine learning and data mining to identify clinically relevant gene signatures reflecting disease severity, organ dysfunction, mortality, and specific endotypes/mechanisms.FindingsGene expression signatures were obtained that predicted severity/organ dysfunction and mortality in both ER and ICU patients with accuracy/AUC of 77–80%. Network analysis revealed these signatures formed a coherent biological program, with specific but overlapping mechanisms/pathways. Given the heterogeneity of sepsis, we asked if patients could be assorted into discrete groups with distinct mechanisms (endotypes) and varying severity. Patients with early sepsis could be stratified into five distinct and novel mechanistic endotypes, named Neutrophilic-Suppressive/NPS, Inflammatory/INF, Innate-Host-Defense/IHD, Interferon/IFN, and Adaptive/ADA, each based on ∼200 unique gene expression differences, and distinct pathways/mechanisms (e.g., IL6/STAT3 in NPS). Endotypes had varying overall severity with two severe (NPS/INF) and one relatively benign (ADA) groupings, consistent with reanalysis of previous endotype studies. A 40 gene-classification tool (accuracy=96%) and several gene-pairs (accuracy=89–97%) accurately predicted endotype status in both ER and ICU validation cohorts.InterpretationThe severity and endotype signatures indicate that distinct immune signatures precede the onset of severe sepsis and lethality, providing a method to triage early sepsis patients.

Highlights

Sepsis is defined by a dysfunctional, life-threatening response to infection leading toorgan dysfunction and failure
Gene expression signatures can to some extent discriminate between sepsis/acute infection and systemic inflammatory response syndrome (SIRS) in the intensive care unit (ICU).6À8 these approaches typically lack sensitivity due to heterogeneity arising from individual genetic variation, demographic factors, the infection source and agent, therapeutic intervention, comorbidities including pre-existing immunesuppressive conditions, epigenetics, etc.9À11
To capture the full range of severity, all 348 emergency rooms (ER) and ICU patients were included in the analysis, capturing the full spectrum of early septic individuals

Summary

Introduction

Sepsis is defined by a dysfunctional, life-threatening response to infection leading to (multi-)organ dysfunction and failure. Given the clinical variability among sepsis patients there is only moderate consensus on how to accurately define the syndrome, especially at first clinical presentation. Gene expression signatures can to some extent discriminate between sepsis/acute infection and systemic inflammatory response syndrome (SIRS) (or “non-sepsis”) in the intensive care unit (ICU).6À8 these approaches typically lack sensitivity due to heterogeneity arising from individual genetic variation, demographic factors, the infection source and agent, therapeutic intervention, comorbidities including pre-existing immunesuppressive conditions, epigenetics, etc.9À11

Objectives

Methods

Results

Conclusion