{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T14:42:57Z","timestamp":1786977777615,"version":"build-2736575974"},"reference-count":60,"publisher":"Wiley","issue":"12","license":[{"start":{"date-parts":[[2023,12,6]],"date-time":"2023-12-06T00:00:00Z","timestamp":1701820800000},"content-version":"vor","delay-in-days":5,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Immunity Inflam &amp;amp; Disease"],"published-print":{"date-parts":[[2023,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To evaluate the diagnostic value of cluster analyses of immune variables in patients with suspected immunodeficiency.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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\u2010means cluster analysis with evaluation of the classification into \u201cno immunodeficiency\u201d versus \u201cimmunodeficiency\u201d and visual analyses of hierarchical heatmaps were performed.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      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 (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u2009.05) or 7 variables with strong evidence (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u2009.01) 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.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Cluster analyses of immune variables, including detailed lymphocyte flowcytometry with T cell subpopulations, may support clinical decision making for suspected immunodeficiency in daily practice.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/iid3.1106","type":"journal-article","created":{"date-parts":[[2023,12,6]],"date-time":"2023-12-06T12:35:53Z","timestamp":1701866153000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Cluster analysis of 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