Evaluating sepsis watch generalizability through multisite external validation of a sepsis machine learning model

Sepsis accounts for a substantial portion of global deaths and healthcare costs. The objective of this reproducibility study is to validate Duke Health's Sepsis Watch ML model, in a new community healthcare setting and assess its performance and clinical utility in early sepsis detection at Summa Health's emergency departments. The study analyzed the model's ability to predict sepsis using a combination of static and dynamic patient data using 205,005 encounters between 2020 and 2021 from 101,584 unique patients. 54.7% (n = 112,223) patients were female and the average age was 50 (IQR [38,71]). The AUROC ranged from 0.906 to 0.960, and the AUPRC ranged from 0.177 to 0.252 across the four sites. Ultimately, the reproducibility of the Sepsis Watch model in a community health system setting confirmed its strong and robust performance and portability across different geographical and demographic contexts with little variation.

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Publication Details

Journal
npj Digital Medicine
Published
2025-06-11
DOI
https://doi.org/10.1038/s41746-025-01664-5
Citations
6
Primary Topic
Sepsis Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
8.13

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article

Evaluating sepsis watch generalizability through multisite external validation of a sepsis machine learning model

Marshall Nichols, Suresh Balu, William Ratliff, Manesh R. Patel et al.
6 citations
npj Digital Medicine
Sepsis Diagnosis and Treatment
8.13
article

Evaluating sepsis watch generalizability through multisite external validation of a sepsis machine learning model

Marshall Nichols, Suresh Balu, William Ratliff, Manesh R. Patel, Jennifer L. Eaton, Bruno Valan, Michael Gao, Matt Gardner, Srikanth Muthya, Mike Revoir, Cara O’Brien, Mark Sendak, Anusha Prakash, Dustin Tart, Ajit J. Thomas
article en
6 citations

Abstract

Sepsis accounts for a substantial portion of global deaths and healthcare costs. The objective of this reproducibility study is to validate Duke Health's Sepsis Watch ML model, in a new community healthcare setting and assess its performance and clinical utility in early sepsis detection at Summa Health's emergency departments. The study analyzed the model's ability to predict sepsis using a combination of static and dynamic patient data using 205,005 encounters between 2020 and 2021 from 101,584 unique patients. 54.7% (n = 112,223) patients were female and the average age was 50 (IQR [38,71]). The AUROC ranged from 0.906 to 0.960, and the AUPRC ranged from 0.177 to 0.252 across the four sites. Ultimately, the reproducibility of the Sepsis Watch model in a community health system setting confirmed its strong and robust performance and portability across different geographical and demographic contexts with little variation.

npj Digital MedicineVol. 8(1)
Summa Health System (US), Duke University (US), Duke Institute for Health Innovation (US), Duke University Hospital (US), Core Laboratories (United States) (US)
National Institutes of Health
Openalex Percentile: Top 2%
Sepsis Diagnosis and Treatment
8.13
DOIOpen Access PDFOpenAlex
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