Scientific publications
Dissecting Secondary Immunodeficiency: Identification of Primary Immunodeficiency within B-Cell Lymphoproliferative Disorders. Scientific Publication
María Palacios-Ortega 1 2 3 , Teresa Guerra-Galán 1 2 3 , Adolfo Jiménez-Huete 4 , José María García-Aznar 5 , Marc Pérez-Guzmán 1 , Maria Dolores Mansilla-Ruiz 1 3 , Ángela Villegas Mendiola 1 3 , Cristina Pérez López 5 , Elsa Mayol Hornero 1 2 3 , Alejandro Peixoto Rodriguez 1 2 3 , Ascensión Peña Cortijo 6 , Marta Polo Zarzuela 6 , Marta Mateo Morales 6 , Eduardo Anguita Mandly 6 , Maria Cruz Cárdenas 7 , Alejandra Carrero 2 , Carlos Jiménez García 1 , Estefanía Bolaños 6 , Belén Íñigo 6 , Fiorella Medina 6 , Eduardo de la Fuente 1 2 3 , Juliana Ochoa-Grullón 1 3 , Blanca García-Solís 2 3 8 , Yolanda García-Carmona 9 , Miguel Fernández-Arquero 1 2 3 , Celina Benavente-Cuesta 6 , Rebeca Pérez de Diego 3 8 , Nicholas Rider 10 , Silvia Sánchez-Ramón 11 12 13 14
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.
CITATION J Clin Immunol. 2024 Oct 23;45(1):32. doi: 10.1007/s10875-024-01818-2
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