The Persistent Sepsis Crisis
For years, the approach to sepsis in acute care settings has followed the same playbook: early warning alerts and standardized care protocols. Healthcare facilities worldwide have invested heavily in these systems, training staff and implementing complex monitoring protocols.
Unfortunately, it hasn’t moved the needle. Sepsis still affects 2.5 million U.S. hospital patients every year, kills over 300,000, and costs the healthcare system $52 billion annually (AHRQ, 2021). These staggering numbers represent not just financial burden, but countless families affected by preventable deaths and prolonged hospital stays.
The question healthcare leaders are now asking isn’t whether we need better tools—it’s whether we’ve been looking at the problem from the wrong angle entirely.
Why Traditional Approaches Fall Short
But what if the problem isn’t that we’re identifying sepsis too late? What if the problem lies in the inherent heterogeneity of sepsis itself? What if we looked further upstream at the underlying biology that drives each patient’s unique risk of developing sepsis?
Traditional scoring systems like SOFA, NEWS, and qSOFA rely on clinical thresholds that can indicate many conditions, not just sepsis. Single biomarkers offer only a narrow snapshot of what’s happening inside a patient’s body. Early warning systems issue alerts for anyone who could potentially have sepsis, creating alert fatigue among clinicians. The result is noise, uncertainty, and difficulty determining the best course of treatment for each individual patient.
This fundamental limitation means clinicians are often making critical decisions with incomplete information, leading to both overtreatment of low-risk patients and potential delays for those at genuine risk.
Understanding Why Biology Matters
That shift in thinking led to something unprecedented: the first ever FDA-authorized AI diagnostic tool specifically designed for sepsis detection.
Sepsis isn’t one disease. Rather, it’s a dysregulated immune response that looks completely different from patient to patient. Some patients become hyperinflamed, with their immune systems attacking their own tissues. Others slip into immune suppression, becoming vulnerable to secondary infections. Some patients cycle through both states in unpredictable ways that defy traditional treatment protocols.
“In the past, there have been a lot of failed attempts with therapies that have gone broadly after patients with sepsis who have different kinds of underlying biology,” says Nathan Shapiro, professor of Emergency Medicine at Harvard Medical School. “The idea going forward is to uncover the underlying biology and then add a therapeutic specifically targeted to the problem for an individual patient.”
The Breakthrough: Sepsis ImmunoScore
A New Solution Rooted in Extensive Research
Over the course of a decade, researchers at Prenosis built a comprehensive biobank containing over 130,000 blood samples from more than 30,000 hospitalized patients across multiple medical centers. They studied dozens of biomarkers not commonly associated with traditional sepsis diagnosis, looking for patterns that revealed true biological risk factors.
The result was Sepsis ImmunoScore: a sophisticated algorithm analyzing 22 parameters including procalcitonin, C-reactive protein, white blood cell counts, and vital signs. This multi-parameter approach provides a more complete picture of patient risk than any single biomarker or scoring system could achieve.
Rigorous Validation Process
The team pursued FDA authorization from the start, conducting multicenter validation studies across six hospitals to ensure the tool would work across diverse patient populations and clinical settings. Their results, published in NEJM AI, showed impressive diagnostic accuracy with AUCs ranging from 0.80 to 0.85 across different clinical sites.
More importantly, the tool accurately predicts which patients will progress to severe sepsis, require ICU admission, need mechanical ventilation, or face mortality risk within 24 hours of assessment.
“It’s hard for the human brain to integrate 22 separate factors in tandem, but machine learning helps give [clinicians] that 3-D clinical picture, combining parameters of patient biology to reveal the risk level of a patient so the clinician can enhance their decision-making,” says Akhil Bhargava, lead author on the NEJM AI paper. “It’s a tool to help providers who want a diagnostic test to know the relative risk of sepsis for a patient.”
Real-World Clinical Applications
Practical Impact for Acute Care Teams
The Sepsis ImmunoScore AI diagnostic tool can work in tandem with existing early warning alert systems, integrating seamlessly within already established clinical workflows. This compatibility means hospitals don’t need to completely overhaul their systems to benefit from this advanced technology.
The applications extend far beyond simply augmenting clinical decision-making. Early data suggests the tool could significantly reduce antibiotic overprescribing in non-infected patients, addressing the growing concern about antimicrobial resistance. Additionally, it can help hospitals improve SEP-1 bundle compliance rates and assist clinical teams with appropriate allocation of limited ICU resources during high-demand periods.
Future of Personalized Sepsis Treatment
The biobank research is now focused on patient stratification based on underlying biological profiles—work that could finally enable the targeted sepsis therapies that have eluded researchers for decades.
“The major limitation in the field is that sepsis is a human construct that actually encompasses a wide variety of pathophysiological processes happening in patients,” says Greg Watson, PhD, senior machine learning scientist at Prenosis and statistical advisor for the NEJM AI study. “The underlying biological heterogeneity has thwarted attempts to develop effective treatments for patients because we’re filing them all under the umbrella of sepsis. [We are] working really hard to amass and harness deeply informative biological and clinical data so we can learn from it and push the acute care field forward.”
The journey from concept to FDA authorization involved more than just algorithm development. It required fundamentally rethinking what sepsis diagnosis should look like in the modern healthcare landscape, shifting from reactive protocols to proactive, biology-based risk assessment.
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