Cluster analysis of flowcytometric immunophenotyping with extended T cell subsets in suspected immunodeficiency
Abstract Background Patients with immunodeficiencies commonly experience diagnostic delays resulting in morbidity. There is an unmet need to identify patients earlier, especially those with high risk for complications. Compared to immunoglobulin quantification and flowcytometric B cell subset analys...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-12-01
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| Series: | Immunity, Inflammation and Disease |
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| Online Access: | https://doi.org/10.1002/iid3.1106 |
| _version_ | 1797372732372942848 |
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| author | Luca Seitz Daniel Gaitan Caroline M. Berkemeier Christoph T. Berger Mike Recher |
| author_facet | Luca Seitz Daniel Gaitan Caroline M. Berkemeier Christoph T. Berger Mike Recher |
| author_sort | Luca Seitz |
| collection | DOAJ |
| description | Abstract Background Patients with immunodeficiencies commonly experience diagnostic delays resulting in morbidity. There is an unmet need to identify patients earlier, especially those with high risk for complications. Compared to immunoglobulin quantification and flowcytometric B cell subset analysis, expanded T cell subset analysis is rarely performed in the initial evaluation of patients with suspected immunodeficiency. The simultaneous interpretation of multiple immune variables, including lymphocyte subsets, is challenging. Objective To evaluate the diagnostic value of cluster analyses of immune variables in patients with suspected immunodeficiency. Methods Retrospective analysis of 38 immune system variables, including seven B cell and sixteen T cell subpopulations, in 107 adult patients (73 with immunodeficiency, 34 without) evaluated at a tertiary outpatient immunology clinic. Correlation analyses of individual variables, k‐means cluster analysis with evaluation of the classification into “no immunodeficiency” versus “immunodeficiency” and visual analyses of hierarchical heatmaps were performed. Results Binary classification of patients into groups with and without immunodeficiency was correct in 54% of cases with the full data set and increased to 69% and 75% of cases, respectively, when only 16 variables with moderate (p < .05) or 7 variables with strong evidence (p < .01) for a difference between groups were included. In a cluster heatmap with all patients but only moderately differing variables and a heatmap with only immunodeficient patients restricted to T cell variables alone, segregation of most patients with common variable immunodeficiency and combined immunodeficiency was observed. Conclusion Cluster analyses of immune variables, including detailed lymphocyte flowcytometry with T cell subpopulations, may support clinical decision making for suspected immunodeficiency in daily practice. |
| first_indexed | 2024-03-08T18:40:03Z |
| format | Article |
| id | doaj.art-9269f0808e1f427b9ae4da569e9fe475 |
| institution | Directory Open Access Journal |
| issn | 2050-4527 |
| language | English |
| last_indexed | 2024-03-08T18:40:03Z |
| publishDate | 2023-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | Immunity, Inflammation and Disease |
| spelling | doaj.art-9269f0808e1f427b9ae4da569e9fe4752023-12-29T08:52:36ZengWileyImmunity, Inflammation and Disease2050-45272023-12-011112n/an/a10.1002/iid3.1106Cluster analysis of flowcytometric immunophenotyping with extended T cell subsets in suspected immunodeficiencyLuca Seitz0Daniel Gaitan1Caroline M. Berkemeier2Christoph T. Berger3Mike Recher4Immunodeficiency Laboratory, Department of Biomedicine University Hospital Basel and University of Basel Basel SwitzerlandImmunodeficiency Laboratory, Department of Biomedicine University Hospital Basel and University of Basel Basel SwitzerlandDivision of Medical Immunology, Laboratory Medicine University Hospital Basel Basel SwitzerlandUniversity Center for Immunology University Hospital Basel Basel SwitzerlandImmunodeficiency Laboratory, Department of Biomedicine University Hospital Basel and University of Basel Basel SwitzerlandAbstract Background Patients with immunodeficiencies commonly experience diagnostic delays resulting in morbidity. There is an unmet need to identify patients earlier, especially those with high risk for complications. Compared to immunoglobulin quantification and flowcytometric B cell subset analysis, expanded T cell subset analysis is rarely performed in the initial evaluation of patients with suspected immunodeficiency. The simultaneous interpretation of multiple immune variables, including lymphocyte subsets, is challenging. Objective To evaluate the diagnostic value of cluster analyses of immune variables in patients with suspected immunodeficiency. Methods Retrospective analysis of 38 immune system variables, including seven B cell and sixteen T cell subpopulations, in 107 adult patients (73 with immunodeficiency, 34 without) evaluated at a tertiary outpatient immunology clinic. Correlation analyses of individual variables, k‐means cluster analysis with evaluation of the classification into “no immunodeficiency” versus “immunodeficiency” and visual analyses of hierarchical heatmaps were performed. Results Binary classification of patients into groups with and without immunodeficiency was correct in 54% of cases with the full data set and increased to 69% and 75% of cases, respectively, when only 16 variables with moderate (p < .05) or 7 variables with strong evidence (p < .01) for a difference between groups were included. In a cluster heatmap with all patients but only moderately differing variables and a heatmap with only immunodeficient patients restricted to T cell variables alone, segregation of most patients with common variable immunodeficiency and combined immunodeficiency was observed. Conclusion Cluster analyses of immune variables, including detailed lymphocyte flowcytometry with T cell subpopulations, may support clinical decision making for suspected immunodeficiency in daily practice.https://doi.org/10.1002/iid3.1106cluster analysisflowcytometryinborn errors of immunityprimary immunodeficiencyT cell subsets |
| spellingShingle | Luca Seitz Daniel Gaitan Caroline M. Berkemeier Christoph T. Berger Mike Recher Cluster analysis of flowcytometric immunophenotyping with extended T cell subsets in suspected immunodeficiency Immunity, Inflammation and Disease cluster analysis flowcytometry inborn errors of immunity primary immunodeficiency T cell subsets |
| title | Cluster analysis of flowcytometric immunophenotyping with extended T cell subsets in suspected immunodeficiency |
| title_full | Cluster analysis of flowcytometric immunophenotyping with extended T cell subsets in suspected immunodeficiency |
| title_fullStr | Cluster analysis of flowcytometric immunophenotyping with extended T cell subsets in suspected immunodeficiency |
| title_full_unstemmed | Cluster analysis of flowcytometric immunophenotyping with extended T cell subsets in suspected immunodeficiency |
| title_short | Cluster analysis of flowcytometric immunophenotyping with extended T cell subsets in suspected immunodeficiency |
| title_sort | cluster analysis of flowcytometric immunophenotyping with extended t cell subsets in suspected immunodeficiency |
| topic | cluster analysis flowcytometry inborn errors of immunity primary immunodeficiency T cell subsets |
| url | https://doi.org/10.1002/iid3.1106 |
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