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Tools for the Real-Time Assessment of a Pseudomonas aeruginosa Infection Model

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A FHIR-Powered Python Implementation of the SENECA Algorithm for Sepsis Subtyping.

Andrew J King¹, Christopher M Horvat¹, David Schlessinger²

¹University of Pittsburgh School of Medicine, Critical Care Medicine, Pittsburgh, Pennsylvania, United States.

Applied Clinical Informatics

|November 7, 2025

Summary

This summary is machine-generated.

HL7 Fast Healthcare Interoperability Resources (FHIR) enables sepsis subtyping across health systems for clinical trial enrichment. Challenges in data extraction and missing information require further attention for multi-institutional sepsis research.

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A Data-Driven Approach to Quantifying Immune States in Sepsis

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

Area of Science:

Biomedical Informatics
Clinical Research
Critical Care Medicine

Background:

Sepsis is a complex syndrome with high mortality, necessitating effective therapeutic strategies.
Limited progress in sepsis treatment is partly due to the lack of actionable sepsis subtypes.
Clinical trial enrichment requires reliable methods for patient stratification.

Purpose of the Study:

To assess the feasibility of using HL7 Fast Healthcare Interoperability Resources (FHIR) for sepsis subtyping before patient randomization.
To support multi-institutional clinical trial enrichment by standardizing sepsis subtyping.
To evaluate the technical implementation of a sepsis subtyping algorithm using FHIR resources.

Main Methods:

Data from 765 patient encounters across two academic medical centers were analyzed.
FHIR resources were extracted from research data warehouses and electronic health records.
A Python implementation of the Sepsis Endotyping in Emergency Care (SENECA) algorithm was developed to process FHIR data.

Main Results:

Sepsis subtyping was successfully performed across both participating health systems.
The Python implementation of the SENECA algorithm showed concordance with the original R implementation.
Discrepancies were observed between research data warehouse and EHR-integrated FHIR API data, attributed to query limitations.
Missing data were prevalent, influenced by clinical practices and FHIR API constraints.

Conclusions:

HL7 FHIR can facilitate multi-institutional sepsis subtyping and clinical trial enrichment.
Technical and data governance challenges, including data extraction limitations and missing data, need to be addressed.
Recommendations are provided to overcome identified challenges in implementing FHIR-based sepsis subtyping.