Mortality and Antibiotic Timing in Deep Learning-Derived Surviving Sepsis Campaign Risk Groups: A Multicenter Study 

TL;DR

A study used deep learning to classify sepsis patients into risk groups and found that antibiotic timing had less impact on mortality for low-risk patients.

Contribution

A novel deep learning approach was used to objectively stratify sepsis patients into risk groups and evaluate antibiotic timing outcomes.

Findings

Low-risk patients had similar mortality regardless of antibiotic timing within 1 or 3 hours.

Mortality rates varied significantly across the four risk groups, with the highest in the shock-likely groups.

Results were consistent across both development and validation sites.

Abstract

Background:
The current Surviving Sepsis Campaign (SSC) guidelines provide recommendations on timing of administering antibiotics in sepsis patients based on probability of sepsis and presence of shock. However, there have been minimal efforts to stratify patients objectively into these groups and describe patient outcomes as a function of antibiotic timing recommendations based on risk stratification using this approach.
Methods:
We conducted an observational cohort study using prospectively applied patient data from two large health systems using patient encounters between 2016 and 2024. At the time of clinical suspicion of sepsis, two deep learning (DL) models were used to stratify patients objectively into groups analogous to the SSC risk groups, based on a patient’s likelihood of having sepsis and likelihood of developing shock. These risk groups were: 1) shock likely to develop and sepsis probable, 2) shock likely to develop and sepsis possible, 3) shock unlikely to develop and sepsis probable, and 4) shock unlikely to develop and sepsis possible. The primary outcome was short-term mortality, a composite of in-hospital mortality and transition to hospice care, across each risk group.
Results:
We identified 34,163 adult patients with potential sepsis. At the development site, risk group mortality rates (%) and median time to antibiotics [IQR] were as follows: 1) 23.1%, 1.7 [1.0 - 3.1] hours; 17.7%, 3.0 [1.7 - 6.2] hours; 5.0%, 2.8 [1.5 - 5.1] hours; and 1.9%, 4.6 [2.7 - 8.0] hours. Results from the validation site were similar. Mortality rates were similar for patients with probable sepsis unlikely to develop shock regardless of antibiotic administration within 1 or 3 hours from triage.
Conclusions
:
Our results suggest that patients who are at low risk of developing shock, regardless of their likelihood of having sepsis, had similar rates of mortality in the 1-hour vs 3-hour time to antibiotic administration groups. Thus, a more lenient time to antibiotic administration could allow for more detailed evaluations and judicious administration of antibiotics, without impacting patient mortality, although we did not assess for causation. Additional prospective studies are required to validate these findings.