Deep-Learning Model for Screening Sepsis Using Electrocardiography
Open accessAbstract
Background: Sepsis is a life-threatening organ dysfunction and a major healthcare burden worldwide. Although sepsis is a medical emergency that requires immediate management, screening for the occurrence of sepsis is difficult. Herein, we propose a deep learning-based model (DLM) for screening sepsis using electrocardiography (ECG). Methods: This retrospective cohort study included 46,017 patients who were admitted to two hospitals. A total of 1,548 and 639 patients had sepsis and septic shock, respectively. The DLM was developed using 73,727 ECGs from 18,142 patients, and internal validation was conducted using 7774 ECGs from 7,774 patients. Furthermore, we conducted an external validation with 20,101 ECGs from 20,101 patients from another hospital to verify the applicability of the DLM across centers. Results: During the internal and external validations, the area under the receiver operating characteristic curve (AUC) of the DLM using 12-lead ECG was 0.901 (95% confidence interval, 0.882–0.920) and 0.863 (0.846–0.879), respectively, for screening sepsis and 0.906 (95% confidence interval (CI), 0.877–0.936) and 0.899 (95% CI, 0.872–0.925), respectively, for detecting septic shock. The AUC of the DLM for detecting sepsis using 6-lead and single-lead ECGs was 0.845–0.882. A sensitivity map revealed that the QRS complex and T waves were associated with sepsis. Subgroup analysis was conducted using ECGs from 4,609 patients who were admitted with an infectious disease, and the AUC of the DLM for predicting in-hospital mortality was 0.817 (0.793–0.840). There was a significant difference in the prediction score of DLM using ECG according to the presence of infection in the validation dataset (0.277 vs. 0.574, p < 0.001), including severe acute respiratory syndrome coronavirus 2 (0.260 vs. 0.725, p = 0.018). Conclusions: The DLM delivered reasonable performance for sepsis screening using 12-, 6-, and single-lead ECGs. The results suggest that sepsis can be screened using not only conventional ECG devices but also diverse life-type ECG machines employing the DLM, thereby preventing irreversible disease progression and mortality.
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AI-ECG sepsis screening reported AUCs of 0.901 internally and 0.863 externally using 12-lead ECGs.
“for sepsis screening, with reported AUCs of 0.901 internally and 0.863 externally using 12-lead ECGs [41].”
From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [41]
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Self-citation
- Authors
- Kwon, J.-M., Lee, Y. R., Jung, M.-S., Lee, Y.-J., Jo, Y.-Y., Kang, D.-Y., Lee, S. Y., Cho, Y.-H., Shin, J.-H., Ban, J.-H., & Kim, K.-H.
- Affiliations
- Sejong Institute (KR)
- Corresponding author
- Kwon, Joon-myoung
- Venue
- Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine
- Published
- 2021-10-03
- Volume
- 29
- Issue
- 1
- Pages
- 145-145
- ISSN
- 1757-7241 (ISSN-L)
- PMC
- PMC8487616
- OpenAlex
- W3127943997
- Cited by
- 28
- References
- 33
- Citations by year
- 2026: 6 · 2025: 5 · 2024: 4 · 2023: 7 · 2022: 6
- FWCI
- 3.06
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scandinavian Journal of Trauma Resuscitation and Emergency Medicine (publishedVersion, cc-by)
- Research Square (acceptedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Research Foundation of Korea (2020R1F1A1073791)
- Topics
- Health Sciences › Medicine › Epidemiology › Sepsis Diagnosis and Treatment
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- sepsis screening, septic shock, electrocardiogram, deep learning, electrocardiographic diagnosis, QRS complex, T wave, in-hospital mortality prediction, COVID-19 pandemic, external validation, sensitivity maps, 12-lead ECG, single-lead ECG
- MeSH terms
- Deep Learning, COVID-19, SARS-CoV-2, Electrocardiography, Humans, Retrospective Studies, Sepsis
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