ABSTRACT
Human inborn errors of immunity include rare disorders entailing functional and quantitative antibody deficiencies due to impaired B cells called the common variable immunodeficiency (CVID) phenotype. Patients with CVID face delayed diagnoses and treatments for 5 to 15 years after symptom onset because the disorders are rare (prevalence of ~1/25,000), and there is extensive heterogeneity in CVID phenotypes, ranging from infections to autoimmunity to inflammatory conditions, overlapping with other more common disorders. The prolonged diagnostic odyssey drives excessive system-wide costs before diagnosis. Because there is no single causal mechanism, there are no genetic tests to definitively diagnose CVID. Here, we present PheNet, a machine learning algorithm that identifies patients with CVID from their electronic health records (EHRs). PheNet learns phenotypic patterns from verified CVID cases and uses this knowledge to rank patients by likelihood of having CVID. PheNet could have diagnosed more than half of our patients with CVID 1 or more years earlier than they had been diagnosed. When applied to a large EHR dataset, followed by blinded chart review of the top 100 patients ranked by PheNet, we found that 74% were highly probable to have CVID. We externally validated PheNet using >6 million records from disparate medical systems in California and Tennessee. As artificial intelligence and machine learning make their way into health care, we show that algorithms such as PheNet can offer clinical benefits by expediting the diagnosis of rare diseases.
Subject(s)
Common Variable Immunodeficiency , Electronic Health Records , Humans , Common Variable Immunodeficiency/diagnosis , Machine Learning , Algorithms , Male , Female , Phenotype , Adult , Undiagnosed Diseases/diagnosisABSTRACT
Activated phosphoinositide 3-kinase delta syndrome (APDS) is an inborn error of immunity with heterogeneous clinical manifestations of infections, immune dysregulation, autoimmunity; lymphoproliferation; and malignancy. Immune complex-mediated vasculitides have not yet been described in APDS patients. Here we offer a case series of three patients with APDS who have refractory IgA vasculitis (also called Henoch-Schönlein purpura), a form of immune complex-mediated vasculitis that activates complement and attracts neutrophils, macrophages and eosinophils to cause local tissue injury. Leniolisib is an inhibitor of PI3K p110δ and an FDA-approved treatment for APDS. IgA vasculitis resolved upon treatment with leniolisib. Patients with immune dysregulation including IgA vasculitis should be screened for APDS.
Subject(s)
Giant Cell Arteritis , Granulomatosis with Polyangiitis , IgA Vasculitis , Mucocutaneous Lymph Node Syndrome , Polyarteritis Nodosa , Pyridines , Pyrimidines , Humans , Antigen-Antibody Complex , Phosphatidylinositol 3-Kinase/therapeutic use , Phosphatidylinositol 3-KinasesABSTRACT
A wide array of clinical manifestations follow infection with Coccidioides immitis or Coccidioides posadasii, ranging from asymptomatic infection to life-threatening pulmonary disease or extrapulmonary dissemination and meningitis. Epidemiological studies require consistent definitions of cases and their comparative clinical features. Understanding host and pathogen determinants of the severity of coccidioidomycosis also requires that specific clinical features (such as coccidioidal meningitis) and their overlap be precisely defined and quantified. Here we propose a system for categorization of outcomes of coccidioidomycosis in individuals who are not overtly immunocompromised that harmonizes clinical assessments during translational research of this increasingly common disease.
