Predicting Sepsis-Related Shock Earlier: How AI Could Transform Critical Care
By the bioMérieux Editors | Reading time: 2 min
PUBLICATION DATE: April 01, 2026
In the ICU, minutes count — particularly for patients with sepsis, where a sudden drop in blood pressure can signal a dangerous turn. If care teams can see that change coming, even by a couple of hours, they can prepare, personalize treatment, and potentially prevent a crisis. That’s where interpretable, clinically grounded AI can help — turning routine hospital data into early warning signs.
A recently published study, Early Prediction of Vasopressor Initiation in ICU Sepsis Patients Using an Interpretable EHR-based ML model, explores how machine learning (ML) using routine electronic health record (EHR) data can help doctors identify prospective emergencies several hours before current bedside cues allow.
This research shows how data science can support clinicians caring for critically ill patients. It was conducted in the scientific environment of the bioMérieux-Lyon University Hospital (HCL) Joint Research Unit and underscores a shared commitment to innovation in diagnostics that makes a real-world clinical impact.
Sepsis Study Highlights:
Using routine ICU EHR data, the team validated an interpretable ML model to predict near‑term vasopressor initiation in adult patients with sepsis to determine if healthcare professionals can anticipate when a sepsis patient is likely to need vasopressors hours ahead of time.
Key Findings:
- Lead time matters: alerts would fire roughly 2–4 hours before vasopressors typically started — time clinicians can use to reassess, prepare, and act.
- The data outperformed basic indicators like mean blood pressure and the modified shock index.
Why This Matters:
Early, interpretable predictions help clinical teams move from reacting to anticipating potential deterioration. While this first publication centers on sepsis, the Joint Research Unit’s approach is broader — using AI to discover early, clinically meaningful patterns in routine data so teams can act sooner, tailor care, and improve outcomes.
Joint Research Unit Focused on the Future of Clinical Innovation
Launched in 2002, the bioMérieux–Lyon University Hospital (HCL) Joint Research Unit is a long-standing public–private partnership designed to keep innovation close to patients. Located within Lyon University Hospital, it brings together clinicians, researchers, and students to tackle pressing infectious disease challenges with direct access to real-world clinical questions and pathways of care.
The unit’s focus has evolved toward sepsis and severe infections, with the goal of advancing diagnostic and prognostic approaches that help clinicians act earlier and with more precision. The value of this “embedded” model was clearly demonstrated during the COVID-19 pandemic by rapidly supporting hospital needs for diagnostics and continued development of solutions — illustrating how close hospital–industry collaboration can accelerate impact when urgency is high.
Today, alongside core themes such as sepsis[1], organ failure, antimicrobial resistance[2], fungal and respiratory infections[3], point-of-care needs in the emergency department, and vulnerable populations (pediatric[4], immunocompromised, transplant), the unit is expanding into a new frontier: AI for EHR mining. The idea is straightforward but powerful: EHRs contain millions of small signals — vitals, labs, treatments, trajectories — and AI can help connect the dots earlier than bedside cues alone.
Looking ahead, the unit is advancing approaches that draw on broader data types and generalize across a wider range of infectious disease and critical care scenarios. The goal remains the same: providing care givers data to anticipate risk earlier, so they can more effectively intervene, provide targeted treatment and, ultimately, save lives.
1) Peronnet et al., “Immune Profiling Panel Gene Set Identifies Critically Ill Patients with Low Monocyte Human Leukocyte Antigen-DR Expression”; Bodinier et al., “Integrated Clustering of Multiple Immune Marker Trajectories Reveals Different Immunotypes in Severely Injured Patients.”
2) Portefaix et al., “Performance Evaluation of Host Biomarker Combinations for the Diagnosis of Serious Bacterial Infection in Young Febrile Children.”
3) Mommert-Tripon et al., “Advancing Respiratory Virus Diagnostics.”
4) Pons et al., “Performance of 11 Host Biomarkers Alone or in Combination in the Diagnosis of Late-Onset Sepsis in Hospitalized Neonates.”
