Common Variable Immunodeficiency Disorder: A Decade of Insights from a Cohort of 150 Patients in India and the Use of Machine Learning Algorithms to Predict Severity.

Abstract

Common Variable Immunodeficiency (CVID) is a heterogeneous disorder characterized by impaired antibody production and recurrent infections. In this study we investigated the clinical and immunological features of CVID in Indian patients and develops a machine learning model for predicting disease severity. We retrospectively analyzed 150 patients diagnosed with CVID over a decade at a tertiary care center in India. The median age of diagnosis was 18 years, with a male predominance (62%). The majority of patients (66.6%) had a severe phenotype, with recurrent respiratory tract infections being the most common clinical manifestation (84.2%). Gastrointestinal complications were observed in 45% of patients, while autoimmune manifestations were seen in 21%. All patients exhibited hypogammaglobulinemia. IgA levels varied, with 7.8% normal and 14.5% undetectable. IgM levels were decreased in 85.5% of patients. B-cell analysis revealed 64.4% had reduced class-switched memory B cells, with 21.7% showing very low levels. Nine adult patients presented with late-onset combined immunodeficiency. Genetic testing, performed on 52 patients, identified underlying monogenic causes in 29 pediatric and 15 adult patients. LRBA deficiency was the most common genetic defect, found in seven pediatric and three adult patients. We developed a novel machine learning-based severity prediction model for CVID patients, utilizing readily available lymphocyte subsets, class-switched memory B cell counts, and serum immunoglobulin levels to provide an accessible and robust tool for predicting disease severity using Ameratunga's clinical severity score. Random Forest outperformed other models across all metrics, achieving an accuracy of 0.853 (95% CI: 0.840-0.866). Feature importance analysis across all models identified Th-Tc ratio, CD19, and IgM levels as the most influential predictors for severity prediction. Our study highlights the diverse clinical and immunological features of CVID in Indian patients, emphasizing the need for early diagnosis and individualized management strategies. The machine learning model developed using commonly available immune parameters provide a robust tool for predicting disease severity, potentially guiding treatment strategies to improve patient outcomes.