A Predictive Model for Identification of Pediatric Individuals With Common Variable Immunodeficiency through Electronic Medical Records.

Introduction: Common variable immunodeficiency (CVID) is characterized by recurrent sinopulmonary infections. However, in the pediatric population, recurrent sinopulmonary infections early in life are common, which can render key clinical features of CVID less distinctive. Accordingly, the diagnosis of CVID is often delayed due to the heterogeneous nature of the presentation and the broad age of onset. A 10-year lag in diagnosis has been found for CVID, and a critical need is improved time-to-diagnosis.

Objective: To utilize machine-learning techniques to identify a clinical signature of CVID in a pediatric population.

Methods: Our selected cohort included 112 individuals with CVID and 627 controls. Controls were restricted from having other medical conditions associated with infection. A machine learning dataset was constructed by summing patient-level counts of clinical metrics. Three supervised machine learning classifiers were trained, tuned, and performance-tested. We validated our findings using a distinct control cohort with high medical complexity and tested a logistic regression approach.

Results: Key features associated with CVID were chest X-ray count, antibiotic prescriptions, and number of common infections. Our Extreme Gradient Boosting model best predicted eventual CVID diagnosis with an F1 score of 0.77, with 21 of 29 CVID diagnoses classified correctly (false negative: 8), and 179 of 183 non-CVID patients correctly classified (false positive: 4), up to 10 years prior to the eventual clinical diagnosis. Key features with a robust association with pediatric CVID were the frequency of common infections and antibiotic prescriptions.

Conclusion: In spite of a high frequency of infections in the comparator population, the clinical signature of pediatric CVID was sufficiently distinctive to enable early identification.