Sepsis Endotype Classification via Blood RNA-Seq Gene Signatures
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Solution Overview
Problem
Current methods for diagnosing sepsis lack sensitivity due to clinical heterogeneity, leading to delayed recognition of severe cases and inappropriate treatment, contributing to high mortality rates and antibiotic resistance.
Innovation Solution
Identification of sepsis mechanistic endotypes through blood RNA-Seq transcriptomic profiles and machine learning to classify patients into distinct endotypes such as Neutrophilic-Suppressive, Inflammatory, Innate Host Defense, Interferon, and Adaptive endotypes, using specific gene signatures for early triage and prognosis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If broad-spectrum antibiotics are initiated early in all suspected sepsis cases, then the chance of hindering progression to severe sepsis is improved, but antibiotic resistance increases due to overuse in non-sepsis cases
Solution Approach 1:
The patent segments sepsis into distinct endotypes (e.g., inflammatory, immunosuppressive, metabolic) based on gene expression profiles. This segmentation allows clinicians to identify which patients truly have sepsis and which do not, enabling targeted antibiotic use only in appropriate cases rather than blanket treatment of all suspected patients.
Solution Approach 2:
The patent introduces gene expression signatures as an intermediary diagnostic tool between clinical suspicion and antibiotic treatment decision. These molecular markers serve as a mediator to objectively confirm sepsis diagnosis before initiating antibiotics, reducing inappropriate prescribing while maintaining treatment for true cases.
2Measurement precision
If gene expression signatures are used to identify sepsis endotypes, then diagnostic precision and personalized treatment are improved, but test complexity and cost increase
Solution Approach 1:
The patent extracts a specific set of gene expression markers from the complex transcriptome that are most discriminatory for sepsis endotypes. By focusing on a curated panel of genes rather than analyzing the entire transcriptome, the test complexity is reduced while maintaining diagnostic precision.
Solution Approach 2:
The patent changes the measurement parameters from broad clinical observations to specific gene expression levels. This parameter transformation enables more precise classification of sepsis endotypes, and the identified gene panels can be optimized for different platform requirements (RNA-Seq, microarray, qPCR) to balance precision with accessibility.
3Ease of operation
If clinicians rely on non-specific symptomology in the emergency room, then ease of patient screening is maintained, but detection precision of actual sepsis cases deteriorates
Solution Approach 1:
The patent replaces the mechanical/clinical assessment system (symptom evaluation) with a molecular detection system (gene expression analysis). This substitution maintains ease of screening through automated testing while dramatically improving detection precision by objectively identifying sepsis endotypes rather than relying on non-specific symptoms.
Data Source
AI summary
The present disclosure relates to methods for classifying a subject into a sepsis mechanistic endotype as well as methods for predicting severity of sepsis in a subject. The methods can comprise use of a biological sample obtained from the subject at first clinical presentation. The classification of the subject into a sepsis mechanistic endotype and/or prediction of severity of sepsis may, for example, allow for treatment of sepsis using an approach suitable to the particular mechanistic endotype and/or severity.