Large scale causal modeling to identify adults at risk for combined and common variable immunodeficiencies.

Combined immunodeficiencies (CID) and common variable immunodeficiencies (CVID), prevalent yet substantially underdiagnosed primary immunodeficiencies, necessitate improved early detection. Leveraging large-scale electronic health records (EHR) from four nationwide US cohorts, we developed a novel causal Bayesian Network (BN) model to identify antecedent clinical phenotypes associated with CID/CVID. Consensus directed acyclic graphs (DAGs) demonstrated robust predictive performance within each cohort (ROC AUC: 0.61-0.77) and generalizability across unseen cohorts (ROC AUC: 0.56-0.72) in identifying CID/CVID, despite varying inclusion criteria across cohorts. The consensus DAGs reveal causal relationships between comorbidities preceding CID/CVID diagnosis, including autoimmune and blood disorders, lymphomas, organ damage or inflammation, respiratory conditions, genetic anomalies, recurrent infections, and allergies. Further evaluation through causal inference and by expert clinical immunologists substantiates the clinical relevance of the identified phenotypic trajectories. These findings hold promise for translation into improved clinical practice, potentially leading to earlier identification and intervention of adults at risk for CID/CVID.