  Sepsis is a life-threatening condition with organ dysfunction and is a
leading cause of death and critical illness worldwide. Even a few hours of
delay in the treatment of sepsis results in increased mortality. Early
detection of sepsis during emergency department triage would allow early
initiation of lab analysis, antibiotic administration, and other sepsis
treatment protocols. The purpose of this study was to compare sepsis detection
performance at ED triage (prior to the use of laboratory diagnostics) of the
standard sepsis screening algorithm (SIRS with source of infection) and a
machine learning algorithm trained on EHR triage data. A machine learning model
(KATE Sepsis) was developed using patient encounters with triage data from
16participating hospitals. KATE Sepsis and standard screening were
retrospectively evaluated on the adult population of 512,949 medical records.
KATE Sepsis demonstrates an AUC of 0.9423 (0.9401 - 0.9441) with sensitivity of
71.09% (70.12% - 71.98%) and specificity of 94.81% (94.75% - 94.87%). Standard
screening demonstrates an AUC of 0.6826 (0.6774 - 0.6878) with sensitivity of
40.8% (39.71% - 41.86%) and specificity of 95.72% (95.68% - 95.78%). The KATE
Sepsis model trained to detect sepsis demonstrates 77.67% (75.78% -79.42%)
sensitivity in detecting severe sepsis and 86.95% (84.2% - 88.81%) sensitivity
in detecting septic shock. The standard screening protocol demonstrates 43.06%
(41% - 45.87%) sensitivity in detecting severe sepsis and40% (36.55% - 43.26%)
sensitivity in detecting septic shock. Future research should focus on the
prospective impact of KATE Sepsis on administration of antibiotics, readmission
rate, morbidity and mortality.
