Sepsis Subtype Classifier Using Whole-Blood Transcriptomic Clustering
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Solution Overview
Problem
Current sepsis groupings based on clinical criteria fail to represent the driving biology of the host response, leading to non-reproducible results and inadequate matching of patients for novel interventions due to disease heterogeneity.
Innovation Solution
A method for determining sepsis subtypes using unsupervised clustering of whole-blood transcriptomic profiles to identify Inflammopathic, Adaptive, and Coagulopathic phenotypes based on gene expression analysis of specific RNA transcripts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If unsupervised clustering is applied to whole-blood transcriptomic profiles to identify sepsis subtypes, then reproducible and clinically relevant sepsis subtypes can be identified, but the complexity of the analysis methodology increases
Solution Approach 1:
The patent uses unsupervised clustering algorithms as intermediary tools to process whole-blood transcriptomic profiles and objectively identify sepsis subtypes without researcher bias. This mediator approach transforms complex gene expression data into reproducible clinical subtypes (Inflammopathic, Adaptive, Coagulopathic) that can be reliably applied across different patient populations.
Solution Approach 2:
The patent replaces traditional manual clinical classification methods with computational clustering analysis of transcriptomic data. This substitution of mechanical/manual classification with automated bioinformatics processing enables reproducible identification of sepsis subtypes based on molecular profiles rather than subjective clinical judgment.
2Ease of operation
If traditional clinical criteria are used to group sepsis patients, then the grouping process is simple and easy to implement, but the groupings fail to represent the driving biology of the host response
Solution Approach 1:
The patent changes the classification parameters from traditional clinical criteria (vital signs, organ failure) to molecular parameters (gene expression profiles). By measuring and analyzing the expression levels of numerous genes in whole blood, the patent captures the biological heterogeneity of sepsis that clinical criteria miss, while still producing actionable patient groupings.
Solution Approach 2:
The patent adds a molecular dimension to sepsis classification by incorporating transcriptomic data alongside or instead of traditional clinical parameters. This dimensional expansion from purely clinical space to molecular-critical space enables identification of biologically distinct patient subtypes that were previously indistinguishable using conventional criteria.
3Ease of manufacture
If clinical criteria such as shock, infection source, or organ failure are used for sepsis groupings, then the classification is straightforward, but it fails to adequately match patients for novel interventions
Solution Approach 1:
The patent applies local quality by identifying distinct molecular profiles within the heterogeneous sepsis population. Rather than treating all sepsis patients uniformly, the clustering analysis reveals locally distinct subtypes (Inflammopathic with high innate immune signal, Adaptive with high adaptive immune signal, Coagulopathic with coagulation dysregulation) that can be matched to specific therapeutic interventions tailored to their molecular characteristics.
Data Source
AI summary
This disclosure provides a gene expression-based method for determining whether a subject having sepsis has an Inflammopathic phenotype, an Adaptive phenotype or a Coagulopathic phenotype. A kit for performing the method is also provided.