Application of an Artificial Intelligence/Machine Learning Model for Estimating Potential US Prevalence of WHIM Syndrome, a Rare Immunodeficiency, from Insurance Claims Data

Introduction: WHIM (Warts, Hypogammaglobulinemia, Infections, and Myelokathexis) syndrome is an inborn error of immunity characterized as a primary immunodeficiency with neutropenia-but the acronym does not reflect the broad spectrum of disease manifestations that patients may experience. A WHIM syndrome diagnosis may be confirmed clinically by the presence of myelokathexis, the retention of white blood cells in the bone marrow, or by identification of a known pathogenic gain-of-function mutation in the CXCR4 gene coding for the CXCR4 receptor. Diagnosis of WHIM syndrome is thought to be frequently missed because of low disease awareness, missed identification of myelokathexis, and lack of routine genetic testing (Al Ustwani O, et al. Br J Haematol. 2014:164;15-23; Dotta L, et al. Curr Mol Med. 2011;11:317-325; Heusinkveld L, et al. Exp Opin Orphan Drugs. 2017;5(10):813-825). The prevalence of WHIM syndrome has never been systematically studied and is unknown. Determination of prevalence via insurance claims data is hindered by the absence of an International Classification of Diseases (ICD)-10 code for WHIM syndrome as well as inconsistent coding for key symptoms of WHIM syndrome, which are variably penetrant. This study applied an artificial intelligence (AI)/machine learning (ML) model to estimate the potential prevalence of WHIM syndrome using a large US insurance claims database.