Subscribe to RSS
Download PDF
DOI: 10.1055/a-2720-5448
A FHIR-Powered Python Implementation of the SENECA Algorithm for Sepsis Subtyping
Authors
Funding This work was supported in part by the Gordon and Betty Moore Foundation. Additional funding support through the National Institutes of Health was provided for E.B. (grant no.: 2-T32-HL007820-21), C.W.S. (grant no.: K23GM104022), V.L. (grant no.: R35GM128672), C.M.H. (grant nos.: K23HD099331 and R01NS118716), H.H. (grant no.: R01NS118716), and J.N.K. (grant no.: R35GM119519).
Abstract
Background
Sepsis is a heterogeneous syndrome with high morbidity and mortality. Despite extensive clinical trials, therapeutic progress remains limited, in part due to the absence of actionable sepsis subtypes.
Objectives
This study aimed to evaluate the feasibility of using HL7 Fast Healthcare Interoperability Resources (FHIR) for prerandomization sepsis subtyping to support clinical trial enrichment across multiple health systems.
Methods
Data from 765 encounters at two academic medical centers were analyzed. FHIR-based resources were extracted from both research data warehouses (RDWs) and electronic health records (EHRs). A Python implementation of the Sepsis Endotyping in Emergency Care (SENECA) sepsis subtyping algorithm was developed to query and assemble FHIR resources for subtype classification.
Results
Open-source Python code for the SENECA algorithm is provided on GitHub. Experiments demonstrated: (1) successful sepsis subtyping across both health systems; (2) concordance between the original R implementation and the new Python implementation; and (3) discrepancies when comparing RDW-derived versus EHR-integrated FHIR APIs, primarily due to query and filtering limitations. Missing data were common and influenced by both clinical practice and FHIR API constraints. We provide five recommendations to address these challenges.
Conclusion
FHIR can support multi-institutional sepsis subtyping and trial enrichment, though technical and governance challenges remain.
Protection of Human and Animal Subjects
This study was approved by the Institutional Review Boards (IRBs) at the University of Pittsburgh (approval #21080005) and Kaiser Permanente (approval #1533936). As a retrospective analysis of de-identified data, informed consent was waived.
Authors' Contributions
Conceptualization: V.X.L., C.W.S., and D.C.A. Methodology and writing—review and editing: all authors. Software: C.M.H., D.S., K.B., J.N.K., and J.S. Data curation: A.J.K., C.M.H., D.S., H.H., K.B., J.N.K., E.B.B., and J.S. Visualization and writing—original draft: A.J.K. Funding acquisition: V.X.L. and C.W.S.
Publication History
Received: 09 February 2025
Accepted: 09 October 2025
Article published online:
07 November 2025
© 2025. Thieme. All rights reserved.
Georg Thieme Verlag KG
Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany
-
References
- 1 Rudd KE, Johnson SC, Agesa KM. et al. Global, regional, and national sepsis incidence and mortality, 1990-2017: analysis for the Global Burden of Disease Study. Lancet 2020; 395 (10219): 200-211
- 2 Marshall JC. Why have clinical trials in sepsis failed?. Trends Mol Med 2014; 20 (04) 195-203
- 3 Seam N, Suffredini AF. Mechanisms of sepsis and insights from clinical trials. Drug Discov Today Dis Mech 2007; 4 (02) 83-93
- 4 Leligdowicz A, Matthay MA. Heterogeneity in sepsis: new biological evidence with clinical applications. Crit Care 2019; 23 (01) 80
- 5 Seymour CW, Kennedy JN, Wang S. et al. Derivation, validation, and potential treatment implications of novel clinical phenotypes for sepsis. JAMA 2019; 321 (20) 2003-2017
- 6 Kiourtis A, Mavrogiorgou A, Menychtas A, Maglogiannis I, Kyriazis D. Structurally mapping healthcare data to HL7 FHIR through ontology alignment. J Med Syst 2019; 43 (03) 62
- 7 Public Law 114-255: An Act to accelerate the discovery, development, and delivery of 21st century cures, and for other purposes [Internet]. Washington (DC): 114th Congress. Accessed October 27, 2025 at: https://www.congress.gov/114/bills/hr34/BILLS-114hr34enr.pdf
- 8 Hripcsak G, Duke JD, Shah NH. et al. Observational health data sciences and informatics (OHDSI): opportunities for observational researchers. Stud Health Technol Inform 2015; 216: 574-578
- 9 De Brouwer W, Patel CJ, Manrai AK, Rodriguez-Chavez IR, Shah NR. Empowering clinical research in a decentralized world. NPJ Digit Med 2021; 4 (01) 102
- 10 Horvat CM, King AJ, Huang DT. Designing and implementing “living and breathing” clinical trials: an overview and lessons learned from the COVID-19 pandemic. Crit Care Clin 2023; 39 (04) 717-732
- 11 Brant EB, Kennedy JN, King AJ. et al. Developing a shared sepsis data infrastructure: a systematic review and concept map to FHIR. NPJ Digit Med 2022; 5 (01) 44
- 12 Palm J, Meineke FA, Przybilla J, Peschel T. “fhircrackr”: an R package unlocking fast healthcare interoperability resources for statistical analysis. Appl Clin Inform 2023; 14 (01) 54-64
- 13 Turer RW, Gradwohl SC, Stassun J. et al. User-centered design and implementation of an interoperable FHIR application for pediatric pneumonia prognostication in a randomized trial. Appl Clin Inform 2024; 15 (03) 556-568