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.
