Evaluation of a real time machine learning sepsis risk algorithm for Emergency Department waiting rooms (SAFE-WAIT)

To evaluate and compare the real-time Sepsis risk Artificial intelligence algorithm For Emergency department WAITing room (SAFE-WAIT) model with the standard SIRS-based sepsis alert in recognizing and managing sepsis.

Study Design

A retrospective analysis of the AI algorithm predicting sepsis risk using an ongoing emergency department sepsis archive.

Setting and Participants

Adults presenting to a metropolitan emergency department in Western Sydney between July 2022 and June 2024 who received either a SAFE-WAIT risk category and/or the standard sepsis alert.

Main Outcome Measures

The primary outcomes: recognition of the development of sepsis and septic shock. Secondary outcomes: the time to physician review and initial antibiotic administration.

Results

Among 108,401 patients analysed, 104,904 received a SAFE-WAIT risk category. Of these, 5,149 (4.9%) had a confirmed sepsis diagnosis, and 312 (3%) developed septic shock. SAFE-WAIT categorized 270 (86.5%) of septic shock patients as high risk at triage, with event onset at 92 minutes (Inter Quartile Range: 17.25–233.00). Adjusted β-coefficients showed significantly faster antibiotic administration in moderate and high-risk SAFE-WAIT groups (Moderate: –58.81 minutes, 95% Confidence Interval (CI): –72.26 to –35.80, p < ; High: –116.99 minutes, 95% CI: –135.34 to –98.65, p < 0.001). High-risk patients had a slightly shorter time to first physician review (β = –3.5 minutes, 95% CI: –5.41 to –1.58, p < 0.001). These effects of SAFE WAIT grouping on the time to antibiotics and clinician review were both modified by triage category ( p for interaction < 0.001).

Conclusions

SAFE-WAIT effectively predicts sepsis-related adverse events at triage, positively impacting sepsis management. These findings underscore the potential role of AI-augmented clinical practice.