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In The Journal of allergy and clinical immunology

BACKGROUND : Identification of patients with underlying inborn errors of immunity and inherent susceptibility to infection remains challenging. The ensuing protracted diagnostic odyssey for such patients often results in greater morbidity and suboptimal outcomes underscoring a need to develop systematic methods for improving diagnostic rates.

OBJECTIVE : The principal aim of this study is to build and validate a generalizable analytical pipeline for population-wide detection of infection susceptibility and risk of primary immunodeficiency.

METHODS : This prospective, longitudinal cohort study coupled weighted rules with a machine learning classifier for risk stratification. Claims data were analyzed from a diverse population (n = 427,110) iteratively over 30 months. Cohort outcomes were enumerated for new diagnoses, hospitalizations and acute care visits. We followed Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis standards.

RESULTS : Cohort members initially identified as high-risk were proportionally more likely to receive a diagnosis of PI compared to low-medium risk or those without claims of interest respectively (9% vs. 1.5% vs. 0.2%; p < 0.001 Chi-Square). Subsequent machine learning stratification enabled an annualized individual snapshot of complexity for triaging referrals. Our top-performing machine learning model for visit-level prediction utilized a single dense layer neural network architecture (AUROC = 0.98, F1 Score = 0.98).

CONCLUSIONS : Here we show that a 2-step analytical pipeline can facilitate identification of individuals with primary immunodeficiency and accurately quantify clinical risk.

CLINICAL IMPLICATIONS : This transparent, generalizable methodology links predictions with clinical outcomes which is expected to facilitate early diagnosis of patients with primary immunodeficiency.

Rider Nicholas L, Coffey Michael, Kurian Ashok, Quinn Jessica, Orange Jordan S, Modell Vicki, Modell Fred


Augmented/Artificial Intelligence, Clinical Data Science, Inborn Error of Immunity, Learning Health System, Machine Learning, Primary Immunodeficiency, Public Health