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<p>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&#xa0;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.</p>

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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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月26日

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

摘要

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.