REVIEW article
Front. Immunol.
Sec. Primary Immunodeficiencies
BUILDING ALLIANCES FOR EARLY DETECTION OF IMMUNODEFICIENCIES: FROM PRIMARY CARE TO HEMATOLOGY
Provisionally accepted
- 1Universitat Autonoma de Barcelona, Barcelona, Spain
- 2Vall d'Hebron Institut de Recerca, Barcelona, Spain
- 3Hospital Infantil i l'Hospital de la Dona de Vall d'Hebron, Barcelona, Spain
- 4Jeffrey Modell Diagnostic and Research Center for Primary Immunodeficiencies, Barcelona, Spain
- 5Universite Toulouse Capitole, Toulouse, France
- 6University of Florence, Florence, Italy
- 7Meyer Children's Hospital, Florence, Italy
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Inborn errors of immunity (IEI), also known as primary immunodeficiencies, are a heterogeneous group of rare disorders characterized by increased susceptibility to infections, immune dysregulation, and malignancy. Early detection remains a major challenge due to the complexity of clinical presentations, limited awareness among non-specialists, and delayed diagnostic pathways. This review explores current strategies to enhance early detection of IEI, highlighting both technological innovations and clinical insights. Tools such as newborn screening, the Jeffrey Modell Foundation (JMF) warning signs, software like SPIRIT, and the PIDCAP project—a structured model designed for primary care implementation using ICD-coded clinical data— have shown promise in identifying at-risk patients. Artificial intelligence (AI) offers additional potential by detecting diagnostic patterns in electronic health records, although challenges related to data quality, heterogeneity, and system interoperability persist. Importantly, hematologic manifestations such as autoimmune cytopenias, lymphoproliferative disorders, and myelodysplastic syndromes often precede or accompany IEI and should prompt immunological evaluation. These conditions, frequently encountered in hematology, may serve as early clinical clues and justify genetic and immunophenotypic assessment. A multidisciplinary approach combining primary care, immunology, hematology, and AI technologies is essential to advance the early detection of IEI. Projects like PIDCAP, and their potential extension to secondary immunodeficiencies, exemplify scalable, patient-centered strategies that may significantly improve diagnostic timeliness and clinical outcomes.
Keywords: artificial intelligence, autoimmune cytopenias, early diagnosis, inborn errors of immunity, Lymphoproliferative Disorders, PIDCAP Project, Primary Care
Received: 08 Sep 2025; Accepted: 08 Dec 2025.
Copyright: © 2025 Rivière, Pasquet and Gambineri. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Jacques G. Rivière
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