Evidence map›Paper›PMID 36177024›Full record

ArticleFrontiers in immunology2022

The Euroflow PID Orientation Tube in the diagnostic workup of primary immunodeficiency: Daily practice performance in a tertiary university hospital.

Jana Neirinck, Annelies Emmaneel, Malicorne Buysse, Jan Philippé, Sofie Van Gassen, Yvan Saeys, Xavier Bossuyt, Stefanie De Buyser, Mirjam van der Burg, Martín Pérez-Andrés and 7 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in immunology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.0field-weighted citation impact, top 25% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 14 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors at 8 institutions in 3 countries.

Jana NeirinckDepartment of Diagnostic Sciences, Ghent University, Ghent, Belgium.
Annelies EmmaneelData Mining and Modelling for Biomedicine Group, Vlaams Instituut voor Biotechnologie (VIB) Center for Inflammation Research, Ghent, Belgium.
Malicorne BuysseDepartment of Laboratory Medicine, Ghent University Hospital, Ghent, Belgium.
Jan PhilippéDepartment of Diagnostic Sciences, Ghent University, Ghent, Belgium.
Sofie Van GassenData Mining and Modelling for Biomedicine Group, Vlaams Instituut voor Biotechnologie (VIB) Center for Inflammation Research, Ghent, Belgium.
Yvan SaeysData Mining and Modelling for Biomedicine Group, Vlaams Instituut voor Biotechnologie (VIB) Center for Inflammation Research, Ghent, Belgium.
Xavier BossuytDepartment of Microbiology, Immunology and Transplantation, KU Leuven, Leuven, Belgium.
Stefanie De BuyserDepartment of Public Health and Primary Care, Ghent University, Ghent, Belgium.
Mirjam van der BurgLaboratory for Pediatric Immunology, Department of Pediatrics, Leiden University Medical Center, Leiden, Netherlands.
Martín Pérez-AndrésCancer Research Centre (Instituto de Biología Molecular y Celular del Cáncer (IBMCC), USAL-CSIC; CIBERONC CB16/12/00400), Institute for Biomedical Research of Salamanca (IBSAL), Department of Medicine and Cytometry Service (NUCLEUS Research Support Platform), University of Salamanca (USAL), Salamanca, Spain.
Alberto OrfaoCancer Research Centre (Instituto de Biología Molecular y Celular del Cáncer (IBMCC), USAL-CSIC; CIBERONC CB16/12/00400), Institute for Biomedical Research of Salamanca (IBSAL), Department of Medicine and Cytometry Service (NUCLEUS Research Support Platform), University of Salamanca (USAL), Salamanca, Spain.
Jacques J M van DongenDepartment of Immunology, Leiden University Medical Cente, Leiden, Netherlands.
Bart N LambrechtLaboratory of Mucosal Immunology, VIB-UGhent Center for Inflammation Research, Ghent University, Ghent, Belgium.
Tessa KerreDepartment of Hematology, Ghent University Hospital, Ghent, Belgium.
Mattias HofmansDepartment of Laboratory Medicine, Ghent University Hospital, Ghent, Belgium.
Filomeen HaerynckDepartment of Pediatric Pulmonology and Immunology and Primary Immunodeficiency (PID) Research Lab, Ghent University Hospital, Ghent, Belgium.
Carolien BonroyDepartment of Diagnostic Sciences, Ghent University, Ghent, Belgium.
Ghent University · BEGhent University Hospital · BELeiden University Medical Center · NLCentro de Investigación Biomédica en Red de Cáncer · ESCentro de Investigación del Cáncer · ESKU Leuven · BEVIB-UGent Center for Inflammation Research · BEVlaams Instituut voor Biotechnologie · BE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Multiparameter flow cytometry (FCM) immunophenotyping is an important tool in the diagnostic screening and classification of primary immunodeficiencies (PIDs). The EuroFlow Consortium recently developed the PID Orientation Tube (PIDOT) as a universal screening tool to identify lymphoid-PID in suspicious patients. Although PIDOT can identify different lymphoid-PIDs with high sensitivity, clinical validation in a broad spectrum of patients with suspicion of PID is missing. In this study, we investigated the diagnostic performance of PIDOT, as part of the EuroFlow diagnostic screening algorithm for lymphoid-PID, in a daily practice at a tertiary reference center for PID. Methods: PIDOT was tested in 887 consecutive patients suspicious of PID at the Ghent University Hospital, Belgium. Patients were classified into distinct subgroups of lymphoid-PID vs. non-PID disease controls (non-PID DCs), according to the IUIS and ESID criteria. For the clinical validation of PIDOT, comprehensive characterization of the lymphoid defects was performed, together with the identification of the most discriminative cell subsets to distinguish lymphoid-PID from non-PID DCs. Next, a decision-tree algorithm was designed to guide subsequent FCM analyses. Results: The mean number of lymphoid defects detected by PIDOT in blood was 2.87 times higher in lymphoid-PID patients vs. non-PID DCs (p < 0.001), resulting in an overall sensitivity and specificity of 87% and 62% to detect severe combined immunodeficiency (SCID), combined immunodeficiency with associated or syndromic features (CID), immune dysregulation disorder (ID), and common variable immunodeficiency (CVID). The most discriminative populations were total memory and switched memory B cells, total T cells, TCD4+cells, and naive TCD4+cells, together with serum immunoglobulin levels. Based on these findings, a decision-tree algorithm was designed to guide further FCM analyses, which resulted in an overall sensitivity and specificity for all lymphoid-PIDs of 86% and 82%, respectively. Conclusion: Altogether, our findings confirm that PIDOT is a powerful tool for the diagnostic screening of lymphoid-PID, particularly to discriminate (S)CID, ID, and CVID patients from other patients suspicious of PID. The combination of PIDOT and serum immunoglobulin levels provides an efficient guide for further immunophenotypic FCM analyses, complementary to functional and genetic assays, for accurate PID diagnostics.

Indexed as

Common Variable ImmunodeficiencyPelvic Inflammatory DiseasePrimary Immunodeficiency DiseasesFemaleFlow CytometryHospitals, UniversityHumansImmunoglobulinsImmunophenotypingImmunoglobulinsclinical validationEuroFlow standardizationflow cytometryimmunophenotyping analysisprimary immunodeficiencies (PID)

Identifiers

PMID36177024
PMCPMC9513319
OpenAlexW4296016715

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.