SWITZERLAND: Volv Global Model Flags Early ARDS Risk in Pneumonia

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Volv Global model flags early ARDS risk in pneumonia: developed on US claims, validated on external ICU data and against independent clinical review, results will be presented at ERS Congress 2026.

Volv Global model flags early ARDS risk in pneumonia: developed on US claims, validated on external ICU data and against independent clinical review, results will be presented at ERS Congress 2026.

Developed on US claims, validated on external ICU data and against independent clinical review, results will be presented at ERS Congress 2026.

Our methodology is built to recognise disease-specific patterns in RWD regardless of disease. ARDS is one proof point; the same approach applies wherever a disease has a distinct signature in the data”

— Vahid Esmaeili, Data Science and Digital Health Director at Volv Global

ÉPALINGES, SWITZERLAND, August 28, 2026 /EINPresswire.com/ -- In brief
● In retrospective analysis, the model flagged patients likely to progress from community-acquired pneumonia (CAP) to ARDS up to five days before diagnosis.

● The model achieved strong discriminative power (ROC-AUC 0.89 on US claims data) and was validated on independent ICU data (ROC-AUC 0.88).
● Model predictions were in high agreement with 3 external KOLs performing chart review.
● Following prospective validation, findings could support patient enrichment in ARDS trials and earlier clinical decisions.

A machine learning model can flag community-acquired pneumonia (CAP) patients likely to progress to acute respiratory distress syndrome (ARDS) up to five days before diagnosis. Developed by Volv Global and tailored to CSL Behring's research question, the results will be presented at ERS Congress 2026 in Barcelona.
ARDS is a life-threatening lung injury: an estimated 3 million people are affected worldwide each year, around 10% of ICU admissions, with hospital mortality of 35 to 46 percent. CAP is a common trigger, and half of those who develop ARDS do so within two days of diagnosis. Clinicians often have just two to six hours to identify those at risk.
The model was trained on de-identified US claims data covering 341,697 patient records (2016–2023). In retrospective testing, it remained predictive up to five days before the ARDS code appeared or at the time of diagnosis with CAP, and three independent specialists in the US, UK and Germany confirmed the flagged phenotypes matched ARDS pathophysiology and had high concordance with patients flagged by the model as high-risk (95% for model’s top 20 high-risk cases).
Behind the result is inFlow, one of Volv Global's solutions for prognostic modelling and outcome prediction. By learning disease-specific biomarkers directly from population-scale real-world data, it surfaces at-risk patients earlier than routine coding allows – work that could, subject to prospective validation, give clinical teams valuable extra time to act.
CSL Behring, a global biotechnology company, worked with Volv Global to shape the clinical questions behind the model and how its results could inform trial design and patient care. Academic collaborators in the US, UK and Germany contributed clinical expertise throughout.
"ARDS progresses fast, and CAP patients don't have days to spare," said Christopher Rudolf, CEO and Founder of Volv Global. "We see this as a lighthouse project: a template for how we can partner with any pharma team facing a disease that is just as hard to catch in time."
"Our methodology is built to recognise disease-specific patterns in real-world data, regardless of the disease," said Vahid Esmaeili, Data Science and Digital Health Director at Volv Global. "ARDS is one proof point; the same approach can apply wherever a disease leaves a distinct signature in the data."