Early diagnosis of persons with von Willebrand disease using a machine learning algorithm and real-world data.

Background: Von Willebrand disease (VWD) is underdiagnosed, often delaying treatment. VWD claims coding is limited and includes no severity qualifiers; improved identification methods for VWD are needed. The aim of this study is to identify and characterize undiagnosed symptomatic persons with VWD in the US from medical insurance claims using predictive machine learning (ML) models.

Research design and methods: Diagnosed and potentially undiagnosed VWD cohorts were defined using Komodo longitudinal US claims data (January 2015-March 2020). ML models were built using key characteristics predictive of VWD diagnosis from the diagnosed cohort. Two ML models predicted VWD diagnosis with the highest accuracy in females (random forest; 84%) and males (gradient boosting machine; 85%). Undiagnosed persons suspected to have VWD were identified using an 80% cutoff probability; profiles of key characteristics were constructed.

Results: The trained ML models were applied to the undiagnosed cohort (28,463 females; 20,439 males) with suspected VWD. Fifty-two percent of undiagnosed females had heavy menstrual bleeding, a key pre-diagnosis symptom. Undiagnosed males tended to have more frequent medical procedures, hospitalizations, and emergency room visits compared with undiagnosed females.

Conclusions: ML algorithms successfully identified potentially undiagnosed symptomatic people with VWD, although many may remain undiagnosed and undertreated. External validation of the algorithms is recommended.