Currently submitted to: JMIR Research Protocols
Date Submitted: Nov 11, 2025
Open Peer Review Period: Nov 11, 2025 - Jan 6, 2026
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Deciphering biomarker signatures for the early diagnosis and prediction of sepsis among adult patients in electronic health records: a scoping review protocol
ABSTRACT
Background:
Introduction: Sepsis is defined as a life-threatening condition caused by a dysregulated host immune response to infection, often resulting in organ dysfunction. The heterogeneous nature and clinical presentation, overlapping with other acute conditions, often leads to diagnostic delays of sepsis, and consequently, to unacceptably high morbidity and mortality levels.
Objective:
This scoping review aims to identify and synthesise studies that use electronic medical record (EMR) data to develop or validate artificial intelligence (AI)-based models for the early detection or prediction of sepsis. Inclusion criteria: Peer-reviewed studies published between March 2016 and February 2024 that use EMR data to develop, test, or evaluate AI or machine learning models for predicting sepsis or septic shock. Adult participants (>18) will be included with no restriction on geographical location in this study. Methods and analysis: This scoping review will be guided by the updated JBI (formerly Joanna Briggs Institute) methodology. The search strategy will include relevant keywords and MeSH terms related to sepsis, electronic health records, and machine learning. Major electronic databases, including MEDLINE, PUBMED, EMBASE, CINAHL, and Cochrane Database of Systematic Reviews/Central Register of Controlled Trials, will be searched. Titles, abstracts, and full texts will be screened by two reviewers independently, with discrepancies resolved by consensus. Ethics and dissemination: We will use publicly available data. No primary data will be collected. Ethical approval will not be required. Results will be extracted into a full report to be submitted to a peer-reviewed scientific journal and disseminated to stakeholders and partners in appropriate formats.
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