BUSCADOR RECOLECTA

Digital.CSIC. Repositorio Institucional del CSIC

oai:digital.csic.es:10261/330894

Supplementary Table 1: Overview of the excluded patients (N=147. Supplementary Table 2: Study population demographics. Supplementary Table 3: Manual Gating strategy for the identification of lymphoid populations in blood according to the EuroFlow guidelines for analysis of blood samples stained with PIDOT. Supplementary Table 4: The clinical characteristics of the non-PID disease controls (DC) (n=116). Supplementary Table 5: Verification of the EuroFlow reference values using an independent healthy control group (N=68). Supplementary Figures Supplementary Figure 1: Box plots of serum immunoglobulin levels at time of PIDOT analysis. Supplementary Figure S2: Box plots of total memory and switched memory B-cells (% as expressed on the B-cells) measured by the PIDOT. Supplementary Figure 3: Box plots of frequency of total defective lymphoid populations (over the 22 FCM PIDOT variables). Supplementary Figure 4: Box plots of frequency of total increased cell counts (over the 22 FCM PIDOT variables). Supplementary Figure 5: Receiver Operating Characteristic (ROC) curve to assess the performance of the decision-tree algorithm in relation to the predicted probabilities for lymphoid-PID., [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., Peer reviewed