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

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.

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Publication Details

Journal
Journal of Clinical Immunology
Published
2025-08-26
DOI
https://doi.org/10.1007/s10875-025-01897-9
Citations
2
Primary Topic
Immunodeficiency and Autoimmune Disorders
Type
article
Field-Weighted Citation Impact
1.59

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article

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

Venkatesh S Pai, Manas Kalra, Harikrishnan Gangadharan, Sarath Balaji et al.
2 citations
Journal of Clinical Immunology
Immunodeficiency and Autoimmune Disorders
1.59
article

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

Venkatesh S Pai, Manas Kalra, Harikrishnan Gangadharan, Sarath Balaji, Aparna Dhondi Dalvi, Vijaya Gowri, Revathi Raj, Priyanka Setia, Gaurav Narula, Chandrakala Shanmukhaiah, Manisha Rajan Madkaikar, Manish Soneja, Shweta Shinde, Mayuri Goriwale, Akash Shukla, Nitin Shah, Snehal Shabrish, Ashish Bavdekar, Amrutha Jose, Ishan Talukdar, Prawin Kumar, Gouri Hule, Pranoti Kini, Umair Ahmed Bargir, Abhijeet Ganapule, Reetika Malik Yadav, Meena Sivasankaran, Maya Gupta, Ujjawal Khurana, Shobna Bhatia, Punit Jain, Disha Vedpathak, Neha Jodhawat, Farah Jijina, Narendra Kumar Chaudhary, Himanshi Chaudhary, Abhinav Sengupta, Sujata Sharma, Mukesh Desai, Prasad Taur, Abhishek Zanwar, Girish Subramaniam, Ratna Sharma, Amita Athavale, Sagar Bhattad, Lavina Temkar, Mamta Mangalani, Subhaprakash Saniyal, Tehsin Petiwala, Dayanand Upase
article en
2 citations

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.

Journal of Clinical ImmunologyVol. 45(1)
Apollo Hospitals (IN), Madras Medical College (IN), Government Medical College (IN), King Edward Memorial Hospital and Seth G.S. Medical College (IN), B. J. Medical College & Sassoon Hospital (IN), All India Institute of Medical Sciences Jodhpur (IN), Tata Memorial Hospital (IN), Lokmanya Tilak Municipal General Hospital and Lokmanya Tilak Municipal Medical College (IN), P. D. Hinduja Hospital and Medical Research Centre (IN), Ruby Hall Clinic (IN), Rajarshee Chhatrapati Shahu Maharaj Government Medical College and CPR Hospital Kolhapur (IN), Indira Gandhi Government Medical College & Hospital (IN), All India Institute of Medical Sciences Bhopal (IN), ICMR - National Institute of Immunohaemotology (IN), Bai Jerbai Wadia Hospital for Children (IN), Sir Ganga Ram Hospital (IN), Fortis Hospital (IN), Apollo Hospitals (IN), Kanchi Kamakoti CHILDS Trust Hospital (IN), Bombay Hospital (IN), Birla Institute of Technology and Science, Pilani - Goa Campus (IN), King Edward Memorial Hospital Research Centre (IN), Apollo Proton Cancer Centre (IN), Themis (India) (IN), All India Institute of Medical Sciences Rishikesh (IN), All India Institute of Medical Sciences (IN)
Indian Council of Medical Research
Openalex Percentile: Top 15%
Immunodeficiency and Autoimmune Disorders
1.59
DOIOpen Access PDFOpenAlex
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