In the largest machine learning study of Pompe disease to date, Volv Global demonstrates that clinician-defined endpoints can be tracked and novel disease features discovered in US claims data across 3,549 patients.
New research presented at ISPOR Global 2026 in Philadelphia demonstrates that machine learning can map clinician-defined endpoints to real-world claims data in Pompe disease and surface disease manifestations beyond pre-specified frameworks. The study, conducted by Volv Global in collaboration with Sanofi, was conducted in a US administrative claims database.
Pompe disease is a rare, chronically debilitating metabolic disorder in which enzyme replacement therapy has now extended patient survival, bringing new long-term manifestations not captured by endpoints established earlier in its treatment history. Many clinically meaningful endpoints do not map to routine healthcare codes, leaving a gap between what patients experience and what the evidence base reflects – with consequences for disease monitoring, HTA submissions, and trial design.
The research addresses that gap through three sequenced methodological contributions:
● The prevalence of literature-based disease symptoms was compared between the Pompe patient cohort and a matched control population without Pompe disease, confirming that the correct patients are represented in the claims data and that these symptoms are reliably measurable within it – a foundational validation step underpinning all subsequent analyses.
● Machine learning models mapped 46 of 67 pre-specified clinical endpoints to diagnosis, procedure, and treatment codes in claims data, demonstrating that endpoints designed for clinical trials can be reliably tracked in routine healthcare data.
● An unsupervised discovery analysis identified novel cardiovascular, respiratory, and systemic features highly prevalent in the Pompe cohort but absent from any pre-specified framework, confirmed against the same control population and offering candidates for endpoint design in future natural history studies and trials.
Volv Global’s proprietary machine learning methodology was applied across all three components, providing a reproducible framework applicable across rare diseases where treatment advances have outpaced existing evidence frameworks.
