Early Warning System for Sepsis Improves Survival Rates, Reduces Hospital Stays
Patients who presented to the emergency departments and were flagged by an artificial intelligence (AI) algorithm for possibly having sepsis received antibiotics sooner and had better outcomes, according to a study published in Critical Care Medicine.
“We showed that when providers had access to the early warning system, patients had better sepsis-related outcomes,” said Yasir Tarabichi, Case Western Reserve School of Medicine, Cleveland, Ohio. “These patients got their antibiotics faster and had, on average, more days ‘alive and out of hospital’ than the group that had usual care. Taken together, the increase in survival rates and reduction in hospital stay improved with the implementation of the early warning system.”
Over 5 months in 2019, the researchers tracked patients aged 18 years and older who came into the emergency department. MetroHealth implemented an electronic health record-embedded early warning system for sepsis. Patients were randomised to standard sepsis care (n = 313) or standard care augmented by the display of a sepsis early warning system-triggered flag in the electronic health record combined with electronic health record-based emergency department pharmacist notification (n = 285).
Time to antibiotic administration from emergency department arrival -- the primary outcome -- was shorter in the augmented care group than that in the standard care group (median, 2.3 hours [interquartile range (IQR), 1.4-4.7 hours] vs 3.0 hours [IQR, 1.6-5.5 hours; P = .039). The hierarchical composite clinical outcome measure of days alive and out of hospital at 28 days was greater in the augmented care group than that in the standard care group (median, 24.1 days vs 22.5 days; P = .011). Rates of fluid resuscitation and antibiotic utilisation did not differ.
“This study adds to the recent national discourse about sepsis early warning systems,” said Dr. Tarabichi. “Recent studies assessed how that score worked in isolation, which is not reflective of how it would actually be used in the real world. We envisioned the early warning system’s role as supportive to our healthcare team’s response to sepsis. Most importantly, we assessed the utility of the tool with the highest quality approach -- a randomised controlled study. In fact, our work stands out as the first published randomised controlled evaluation of a model-based early warning system in the emergency room setting.”
SOURCE: Case Western Reserve University