A Novel Digital Phenotype for Burn Sepsis: Leveraging Electronic Health Record Data and Natural Language Processing to Improve Case Definition

Background Sepsis remains a leading cause of death for burn patients, yet the condition is hard to spot early. Hospitals generally rely on International Classification of Diseases (ICD) codes for surveillance, but these codes are assigned late and often miss active cases. Objectives We developed and validated a scalable, electronic health record (EHR)-based digital phenotype that improves identification of burn-related sepsis compared with ICD codes alone. Methods We performed a retrospective cohort study of adult burn inpatients (n = 1,371) admitted to Loyola University Medical Center (2007 – 2021). Structured EHR data and clinical notes were extracted from a research database. Sequential Organ Failure Assessment (SOFA) scores were calculated every 4 hours; natural-language processing parsed vasopressor doses and culture alerts. Four algorithms were evaluated: (1) ICD codes; (2) SOFA increase ≥ 2 + broad-spectrum antibiotics; (3) SOFA increase ≥ 2 + positive blood culture; (4) SOFA increase ≥ 2 + any positive culture. Results ICD coding alone classified 79 encounters (5.8%) as sepsis. EHR-enhanced algorithms identified more cases: 123 via SOFA + antibiotics (9.0%), 21 via SOFA + blood culture (1.5%), and 53 via SOFA + any culture (3.9%). The rule count score (0-4) achieved highest performance (AUC 0.92), outperforming ICD codes alone (AUC 0.77). Conclusions Our multi-modal digital phenotype doubled sepsis detection compared to ICD-based surveillance. The tiered risk assessment approach showed excellent discrimination with increasing positive predictive value as more criteria were met. This phenotype can be implemented using routine EHR data, supporting early-warning tools for this high-risk population