---
title: "EHR-based automated algorithm improves sepsis surveillance versus ICD-10 coding"
id: "pubmed-42775946"
canonical_url: "https://medichelpline.com/clinical-feed/pubmed-42775946"
content_type: "clinical_feed_article"
specialty: "Critical Care"
source_name: "PubMed / NCBI"
source_url: "https://pubmed.ncbi.nlm.nih.gov/42775946/"
doi: "10.1080/23744235.2026.2731937"
published_at: "2026-09-23T00:00:00.000Z"
evidence_level: "Journal Article"
license: "CC-BY-NC-4.0 / Informational Use"
---
# EHR-based automated algorithm improves sepsis surveillance versus ICD-10 coding
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/pubmed-42775946
- **Specialty:** [Critical Care](https://medichelpline.com/clinical-feed/critical-care.md)
- **Primary Source:** PubMed / NCBI
- **Source URL:** [Original Journal Publication](https://pubmed.ncbi.nlm.nih.gov/42775946/)
- **DOI:** [10.1080/23744235.2026.2731937](https://doi.org/10.1080%2F23744235.2026.2731937)
- **Published At:** 2026-09-23T00:00:00.000Z
- **Evidence Rating:** Journal Article
## Executive GIST (TL;DR)
- Study compared diagnostic coding (ICD-10) with a fully automated **EHR**-based algorithm applying **Sepsis-3** criteria for in-hospital sepsis surveillance using data from Karolinska University Hospital and three hospitals in Region Västerbotten, Sweden. - The observational cohort covered admissions from 2016–2024 at Karolinska and 2022–2024 in Västerbotten; a common analysis period included 174,343 admissions (2022–2024). - The automated algorithm defined sepsis as suspected infection plus an increase in **SOFA** score ≥2 points; sepsis diagnostic codes were categorised as explicit or specific (explicit: R65.1; specific: R57.2). - Algorithm-classified sepsis incidence was higher than code-based incidence: at Karolinska 11.4% (algorithm) vs 2.5% (explicit codes) and 1.4% (specific codes); in Västerbotten 6.2% vs 1.2% and 0.6%, respectively. - Concordance between algorithm and ICD-10 coding was low: kappa values at Karolinska were 0.21 (explicit) and 0.14 (specific); in Västerbotten 0.15 and 0.09, respectively. - Introduction of an automated **SOFA** calculator in the EHR at Karolinska was associated with increased use of specific sepsis codes over 2016–2024 but coding rates remained far below algorithm-derived classification. - Authors conclude that automated **EHR**-based algorithms enable data-driven **sepsis** surveillance and may support more reliable diagnostic coding practices. - Keywords listed in the source: algorithm; diagnostics; incidence; **sepsis**; surveillance.
## Clinical Analysis & Structured Key Points
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Online ahead of print. # Improved sepsis surveillance using a fully automated electronic health record-based algorithm compared to diagnostic coding [Pontus Naucler](https://pubmed.ncbi.nlm.nih.gov/?term=Naucler+P&cauthor_id=42775946)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42775946/#full-view-affiliation-1 "Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden.")[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42775946/#full-view-affiliation-2 "Department of Medicine, Solna, Division of Infectious Diseases, Karolinska Institutet, Stockholm, Sweden."), [Suzanne Desirée van der Werff](https://pubmed.ncbi.nlm.nih.gov/?term=van+der+Werff+SD&cauthor_id=42775946)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42775946/#full-view-affiliation-1 "Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden.")[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42775946/#full-view-affiliation-2 "Department of Medicine, Solna, Division of Infectious Diseases, Karolinska Institutet, Stockholm, Sweden."), [Andreas Winroth](https://pubmed.ncbi.nlm.nih.gov/?term=Winroth+A&cauthor_id=42775946)[ 3 ](https://pubmed.ncbi.nlm.nih.gov/42775946/#full-view-affiliation-3 "Department of Clinical Microbiology, Umeå University, Umeå, Sweden.")[ 4 ](https://pubmed.ncbi.nlm.nih.gov/42775946/#full-view-affiliation-4 "Department of Infection Control and Prevention, Umeå, Sweden."), [Anders Ternhag](https://pubmed.ncbi.nlm.nih.gov/?term=Ternhag+A&cauthor_id=42775946)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42775946/#full-view-affiliation-1 "Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden.")[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42775946/#full-view-affiliation-2 "Department of Medicine, Solna, Division of Infectious Diseases, Karolinska Institutet, Stockholm, Sweden."), [Anders F Johansson](https://pubmed.ncbi.nlm.nih.gov/?term=Johansson+AF&cauthor_id=42775946)[ 3 ](https://pubmed.ncbi.nlm.nih.gov/42775946/#full-view-affiliation-3 "Department of Clinical Microbiology, Umeå University, Umeå, Sweden.")[ 4 ](https://pubmed.ncbi.nlm.nih.gov/42775946/#full-view-affiliation-4 "Department of Infection Control and Prevention, Umeå, Sweden.") Affiliations Expand ### Affiliations * 1 Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden. * 2 Department of Medicine, Solna, Division of Infectious Diseases, Karolinska Institutet, Stockholm, Sweden. * 3 Department of Clinical Microbiology, Umeå University, Umeå, Sweden. * 4 Department of Infection Control and Prevention, Umeå, Sweden. * PMID: **42775946** * DOI: [ 10.1080/23744235.2026.2731937 ](https://doi.org/10.1080/23744235.2026.2731937) Item in Clipboard # Improved sepsis surveillance using a fully automated electronic health record-based algorithm compared to diagnostic coding Pontus Naucler et al. Infect Dis (Lond). 2026. Show details Display options Display options Format Abstract PubMed PMID Infect Dis (Lond) Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Infect+Dis+%28Lond%29%22%5Bjour%5D&sort=date&sort_order=desc) * [ Search in NLM Catalog ](https://www.ncbi.nlm.nih.gov/nlmcatalog?term=%22Infect+Dis+%28Lond%29%22%5BTitle+Abbreviation%5D) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42775946/) . 2026 Sep 23:1-6. doi: 10.1080/23744235.2026.2731937. Online ahead of print. ### Authors [Pontus Naucler](https://pubmed.ncbi.nlm.nih.gov/?term=Naucler+P&cauthor_id=42775946)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42775946/#short-view-affiliation-1 "Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden.")[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42775946/#short-view-affiliation-2 "Department of Medicine, Solna, Division of Infectious Diseases, Karolinska Institutet, Stockholm, Sweden."), [Suzanne Desirée van der Werff](https://pubmed.ncbi.nlm.nih.gov/?term=van+der+Werff+SD&cauthor_id=42775946)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42775946/#short-view-affiliation-1 "Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden.")[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42775946/#short-view-affiliation-2 "Department of Medicine, Solna, Division of Infectious Diseases, Karolinska Institutet, Stockholm, Sweden."), [Andreas Winroth](https://pubmed.ncbi.nlm.nih.gov/?term=Winroth+A&cauthor_id=42775946)[ 3 ](https://pubmed.ncbi.nlm.nih.gov/42775946/#short-view-affiliation-3 "Department of Clinical Microbiology, Umeå University, Umeå, Sweden.")[ 4 ](https://pubmed.ncbi.nlm.nih.gov/42775946/#short-view-affiliation-4 "Department of Infection Control and Prevention, Umeå, Sweden."), [Anders Ternhag](https://pubmed.ncbi.nlm.nih.gov/?term=Ternhag+A&cauthor_id=42775946)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42775946/#short-view-affiliation-1 "Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden.")[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42775946/#short-view-affiliation-2 "Department of Medicine, Solna, Division of Infectious Diseases, Karolinska Institutet, Stockholm, Sweden."), [Anders F Johansson](https://pubmed.ncbi.nlm.nih.gov/?term=Johansson+AF&cauthor_id=42775946)[ 3 ](https://pubmed.ncbi.nlm.nih.gov/42775946/#short-view-affiliation-3 "Department of Clinical Microbiology, Umeå University, Umeå, Sweden.")[ 4 ](https://pubmed.ncbi.nlm.nih.gov/42775946/#short-view-affiliation-4 "Department of Infection Control and Prevention, Umeå, Sweden.") ### Affiliations * 1 Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden. * 2 Department of Medicine, Solna, Division of Infectious Diseases, Karolinska Institutet, Stockholm, Sweden. * 3 Department of Clinical Microbiology, Umeå University, Umeå, Sweden. * 4 Department of Infection Control and Prevention, Umeå, Sweden. * PMID: **42775946** * DOI: [ 10.1080/23744235.2026.2731937 ](https://doi.org/10.1080/23744235.2026.2731937) Item in Clipboard Cite Display options Display options Format Abstract PubMed PMID ## Abstract **Background:** Accurate diagnostic coding of sepsis is essential for surveillance, resource allocation, and health policy planning. Studies assessing the usability of claims-based data (ICD-10 codes) compared to clinical criteria for sepsis surveillance are needed. **Objectives:** To assess the concordance between ICD-10 diagnostic coding of sepsis and classification using a previously validated fully automated electronic health record (EHR)-based algorithm applying Sepsis-3 criteria. **Methods:** We conducted an observational study, including adult in-hospital admissions during 2016-2024 at Karolinska University Hospital and 2022-2024 at three hospitals in Region Västerbotten, Sweden. The Sepsis-3 algorithm identified suspected infection combined with an increase in Sequential Organ Failure Assessment (SOFA) score ≥2 points. Sepsis ICD-10 codes were categorised as explicit or specific (R65.1, R57.2). Concordance was evaluated using descriptive and kappa statistics, and time trends were analysed. **Results:** Among 174,343 admissions during the common study period (2022-2024), the Sepsis-3 algorithm classified sepsis in 11.4% of admissions at Karolinska and 6.2% in Västerbotten, compared to 2.5% and 1.2% with explicit codes and 1.4% and 0.6% with specific codes, respectively. Concordance of diagnostic codes with the Sepsis-3 algorithm was low (kappa 0.21 and 0.14 for explicit and specific codes at Karolinska; 0.15 and 0.09 in Västerbotten). During the study period 2016-24 at Karolinska, introduction of an automated SOFA calculator in the EHR system was associated with increased usage of specific sepsis codes but remained far below algorithm-based classification. **Conclusion:** Automated EHR-based algorithms allow for data-driven sepsis surveillance and may support more reliable diagnostic coding practices. **Keywords:** algorithm; diagnostics; incidence; sepsis; surveillance. [PubMed Disclaimer](https://pubmed.ncbi.nlm.nih.gov/disclaimer/) ## Similar articles * [ A Novel Digital Phenotype for Burn Sepsis: Leveraging Electronic Health Record Data and Natural Language Processing to Improve Case Definition. ](https://pubmed.ncbi.nlm.nih.gov/42242239/) Soulakis ND, Li L, Peters AA, Kubasiak JC.Soulakis ND, et al.Appl Clin Inform. 2026 May;17(3):466-475. doi: 10.1055/a-2885-7810. 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