Dataset Information

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

ABSTRACT:

Background

Inter-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.

Methods

Blood 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.

Findings

Gene 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.

Interpretation

The 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.

SUBMITTER: Baghela A

PROVIDER: S-EPMC8808161 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

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Publications

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

Baghela Arjun A Pena Olga M OM Lee Amy H AH Baquir Beverlie B Falsafi Reza R An Andy A Farmer Susan W SW Hurlburt Andrew A Mondragon-Cardona Alvaro A Rivera Juan Diego JD Baker Andrew A Trahtemberg Uriel U Shojaei Maryam M Jimenez-Canizales Carlos Eduardo CE Dos Santos Claudia C CC Tang Benjamin B Bouma Hjalmar R HR Cohen Freue Gabriela V GV Hancock Robert E W REW

EBioMedicine 20220110

<h4>Background</h4>Inter-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.<h4>Methods</h4>Blood 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 ...[more]

PMID: 35027333

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Project description:Background: Sepsis involves aberrant immune responses to infection, but the exact nature of this immune dysfunction remains poorly defined. Bacterial endotoxins like lipopolysaccharide (LPS) are potent inducers of inflammation, which has been associated with the pathophysiology of sepsis, but repeated exposure can also induce a suppressive effect known as endotoxin tolerance or cellular reprogramming. It has been proposed that endotoxin tolerance might be associated with the immunosuppressive state that was primarily observed during late-stage sepsis. However, this relationship remains poorly characterised. Here we clarify the underlying mechanisms and timing of immune dysfunction in sepsis. Methods: We defined a gene expression signature characteristic of endotoxin tolerance. Gene-set test approaches were used to correlate this signature with early sepsis, both newly and retrospectively analysing microarrays from 593 patients in 11 cohorts. Then we recruited a unique cohort of possible sepsis patients at first clinical presentation in an independent blinded controlled observational study to determine whether this signature was associated with the development of confirmed sepsis and organ dysfunction. Findings: All sepsis patients presented an expression profile strongly associated with the endotoxin tolerance signature (p < 0.01; AUC 96.1%). Importantly, this signature further differentiated between suspected sepsis patients who did, or did not, go on to develop confirmed sepsis, and predicted the development of organ dysfunction. Interpretation: Our data support an updated model of sepsis pathogenesis in which endotoxin tolerance-mediated immune dysfunction (cellular reprogramming) is present throughout the clinical course of disease and related to disease severity. Thus endotoxin tolerance might offer new insights guiding the development of new therapies and diagnostics for early sepsis. For the RNA-Seq study reported here, 73 patients were recruited with deferred consent at the time of first examination in an emergency ward based on the opinion of physicians that there was a potential for the patient's condition to develop into sepsis. These were retrospectively divided into groups based on clinical features and compared to 11 non-urgent surgical controls.

Dataset Information

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

Background

Methods

Findings

Interpretation

Publications

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

Similar Datasets

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