1. Dissecting Secondary Immunodeficiency: Identification of Primary Immunodeficiency within B-Cell Lymphoproliferative Disorders.
- Author
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Palacios-Ortega M, Guerra-Galán T, Jiménez-Huete A, García-Aznar JM, Pérez-Guzmán M, Mansilla-Ruiz MD, Mendiola ÁV, López CP, Hornero EM, Rodriguez AP, Cortijo AP, Polo Zarzuela M, Morales MM, Mandly EA, Cárdenas MC, Carrero A, García CJ, Bolaños E, Íñigo B, Medina F, de la Fuente E, Ochoa-Grullón J, García-Solís B, García-Carmona Y, Fernández-Arquero M, Benavente-Cuesta C, de Diego RP, Rider N, and Sánchez-Ramón S
- Subjects
- Humans, Male, Female, Child, Preschool, Child, Infant, Immunologic Deficiency Syndromes diagnosis, Immunologic Deficiency Syndromes immunology, Adolescent, Diagnosis, Differential, Adult, Primary Immunodeficiency Diseases diagnosis, Primary Immunodeficiency Diseases immunology, Primary Immunodeficiency Diseases etiology, Lymphoproliferative Disorders diagnosis, Lymphoproliferative Disorders etiology, Lymphoproliferative Disorders immunology, B-Lymphocytes immunology
- Abstract
Distinguishing between primary (PID) and secondary (SID) immunodeficiencies, particularly in relation to hematological B-cell lymphoproliferative disorders (B-CLPD), poses a major clinical challenge. We aimed to analyze and define the clinical and laboratory variables in SID patients associated with B-CLPD, identifying overlaps with late-onset PIDs, which could potentially improve diagnostic precision and prognostic assessment. We studied 37 clinical/laboratory variables in 151 SID patients with B-CLPD. Patients were classified as "Suspected PID Group" when having recurrent-severe infections prior to the B-CLPD and/or hypogammaglobulinemia according to key ESID criteria for PID. Bivariate association analyses showed significant statistical differences between "Suspected PID"- and "SID"-groups in 10 out of 37 variables analyzed, with "Suspected PID" showing higher frequencies of childhood recurrent-severe infections, family history of B-CLPD, significantly lower serum Free Light Chain (sFLC), immunoglobulin concentrations, lower total leukocyte, and switch-memory B-cell counts at baseline. Rpart machine learning algorithm was performed to potentially create a model to differentiate both groups. The model developed a decision tree with two major variables in order of relevance: sum κ + λ and history of severe-recurrent infections in childhood, with high sensitivity 89.5%, specificity 100%, and accuracy 91.8% for PID prediction. Identifying significant clinical and immunological variables can aid in the difficult task of recognizing late-onset PIDs among SID patients, emphasizing the value of a comprehensive immunological evaluation. The differences between "Suspected PID" and SID groups, highlight the need of early, tailored diagnostic and treatment strategies for personalized patient management and follow up., (© 2024. The Author(s).)
- Published
- 2024
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