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Dissecting Secondary Immunodeficiency: Identification of Primary Immunodeficiency within B-Cell Lymphoproliferative Disorders

  • María Palacios-Ortega,
  • Teresa Guerra-Galán,
  • Adolfo Jiménez-Huete,
  • José María García-Aznar,
  • Marc Pérez-Guzmán,
  • Maria Dolores Mansilla-Ruiz,
  • Ángela Villegas Mendiola,
  • Cristina Pérez López,
  • Elsa Mayol Hornero,
  • Alejandro Peixoto Rodriguez,
  • Ascensión Peña Cortijo,
  • Marta Polo Zarzuela,
  • Marta Mateo Morales,
  • Eduardo Anguita Mandly,
  • Maria Cruz Cárdenas,
  • Alejandra Carrero,
  • Carlos Jiménez García,
  • Estefanía Bolaños,
  • Belén Íñigo,
  • Fiorella Medina,
  • Eduardo de la Fuente,
  • Juliana Ochoa-Grullón,
  • Blanca García-Solís,
  • Yolanda García-Carmona,
  • Miguel Fernández-Arquero,
  • Celina Benavente-Cuesta,
  • Rebeca Pérez de Diego,
  • Nicholas Rider,
  • Silvia Sánchez-Ramón

摘要

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.