Prospective, multi-site study of patient outcomes after implementation of the TREWS machine learning-based early warning system for sepsis
R Adams et al, Nature Medicine, July 21, 2022
Early recognition and treatment of sepsis are linked to improved patient outcomes. Machine learning-based early warning systems may reduce the time to recognition, but few systems have undergone clinical evaluation. In this prospective, multi-site cohort study, we examined the association between patient outcomes and provider interaction with a deployed sepsis alert system called the Targeted Real-time Early Warning System (TREWS). Our findings indicate that early warning systems have the potential to identify sepsis patients early and improve patient outcomes and that sepsis patients who would benefit the most from early treatment can be identified and prioritized at the time of the alert.
Factors driving provider adoption of the TREWS machine learning-based early warning system and its effects on sepsis treatment timing
KE Henry et al, Nature Medicine, July 21, 2022
We analyzed provider interactions with a sepsis early detection tool (Targeted Real-time Early Warning System), which was deployed at five hospitals over a 2-year period. Among 9,805 retrospectively identified sepsis cases, the early detection tool achieved high sensitivity (82% of sepsis cases were identified) and a high rate of adoption: 89% of all alerts by the system were evaluated by a physician or advanced practice provider and 38% of evaluated alerts were confirmed by a provider. Patients with sepsis whose alert was confirmed by a provider within 3?h had a 1.85-h (95% CI 1.66–2.00) reduction in median time to first antibiotic order compared to patients with sepsis whose alert was either dismissed, confirmed more than 3?h after the alert or never addressed in the system.
Human–machine teaming is key to AI adoption: clinicians’ experiences with a deployed machine learning system
KE Henry et al, NPJ Digital Medicine, July 21, 2022
Based on a qualitative analysis of coded interviews with clinicians who use an ML-based system for sepsis, we found that, rather than viewing the system as a surrogate for their clinical judgment, clinicians perceived themselves as partnering with the technology. Our findings suggest that, even without a deep understanding of machine learning, clinicians can build trust with an ML system through experience, expert endorsement and validation, and systems designed to accommodate clinicians’ autonomy and support them across their entire workflow.
Evaluation of a Multivalent Transcriptomic Metric for Diagnosing Surgical Sepsis and Estimating Mortality Among Critically Ill Patients
SC Brakenridge et al, JAMA Network Open, July 12, 2022
Can a whole-blood RNA transcriptomic metric (IMX) obtained in the first 12 hours after intensive care unit (ICU) admission accurately measure the presence of bacterial infection and risk for sepsis mortality? In this diagnostic and prognostic study including 200 patients with critical illness enrolled from a surgical ICU, the IMX transcriptomic metric was equivalent to or significantly better than the sequential organ failure assessment score and existing biomarkers (procalcitonin and interleukin 6 levels) for the diagnosis of acute infections and estimation of 30-day mortality.