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Most existing predictive models, predominantly derived from HLA\u2010matched donor cohorts, have been utilized for nonrelapse mortality (NRM) prediction; however, their utility in predicting aGvHD risk specifically in haplo\u2010HSCT recipients receiving antithymocyte globulin (ATG)\u2013based prophylaxis warrants further validation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      A total of 280 patients undergoing ATG\u2013based haplo\u2010HSCT were retrospectively analyzed across two medical centers, split into training, internal test, and external validation cohorts. We first evaluated the predictive accuracy of the previously established Mount Sinai Acute GvHD International Consortium (MAGIC) algorithm for aGvHD, steroid\u2010refractory aGvHD (SR\u2010aGvHD). Subsequently, plasma concentrations of candidate cytokines (ST2, REG3\u03b1, Elafin, and TNFRI), selected a priori for their links to epithelial injury and inflammatory signaling in GvHD, were assessed for their predictive and causal relationships with aGvHD using logistic regression, weighted average area under the curve (wAUC), Mendelian randomization (MR), and restricted cubic spline (RCS) analyses. A new predictive model (the HAG model) was constructed based on identified key cytokines and validated across multicenter cohorts. MR analyses utilized external genome\u2010wide association (GWAS) datasets to validate the reliability of identified cytokines. A visual interface for the model was created using R\n                      <jats:italic>Shiny<\/jats:italic>\n                      .\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      MAGIC algorithm remains effective in the ATG\u2013based haplo\u2010HSCT setting for predicting aGvHD, achieving AUC values of 0.693 (training), 0.658 (internal test), and 0.622 (external validation). Among candidate cytokines, a combination of ST2, REG3\u03b1, and Elafin (the HAG model) demonstrated the highest predictive accuracy. MR analysis leveraging external GWAS data supported potential causal associations of ST2 (OR\u2009=\u20091.280,\n                      <jats:italic>p<\/jats:italic>\n                      = 0.004), REG3\u03b1 (OR\u2009=\u20091.300,\n                      <jats:italic>p<\/jats:italic>\n                      = 0.012), and Elafin (OR\u2009=\u20091.209,\n                      <jats:italic>p<\/jats:italic>\n                      = 0.039) with aGvHD risk, providing complementary biological support for their selection as candidate biomarkers. The HAG model displayed good discrimination for aGvHD (AUC\u2009=\u20090.636\u20130.701) and SR\u2010aGvHD (AUC\u2009=\u20090.666\u20130.779). Integration of clinical factors further enhanced prediction (HAG\u2010C model, wAUC from 0.682 to 0.701).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The HAG model, incorporating ST2, REG3\u03b1, and Elafin, provides clinically meaningful prediction of aGvHD and related clinical outcomes in ATG\u2013based haplo\u2010HSCT recipients, and may serve as a mechanistically informed tool for risk stratification and clinical management.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1155\/mi\/3165406","type":"journal-article","created":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T19:36:25Z","timestamp":1782848185000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Development and Validation of a Cytokine\u2010Based Predictive Model for Acute GvHD and Composite Outcomes in ATG\u2013Based Haploidentical Hematopoietic Stem Cell Transplantation"],"prefix":"10.1155","volume":"2026","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-5625-5910","authenticated-orcid":false,"given":"Yiyin","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0589-0386","authenticated-orcid":false,"given":"Xinghao","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhou","family":"Jin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuanhe","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2719-3805","authenticated-orcid":false,"given":"Xiaoxia","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4643-2098","authenticated-orcid":false,"given":"Yang","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,6,30]]},"reference":[{"key":"e_1_2_14_1_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41572-023-00438-1"},{"key":"e_1_2_14_2_2","doi-asserted-by":"publisher","DOI":"10.1186\/s40164-023-00425-y"},{"key":"e_1_2_14_3_2","doi-asserted-by":"publisher","DOI":"10.3324\/haematol.2024.286040"},{"key":"e_1_2_14_4_2","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMra1609337"},{"key":"e_1_2_14_5_2","doi-asserted-by":"publisher","DOI":"10.1182\/blood-2012-08-355990"},{"key":"e_1_2_14_6_2","doi-asserted-by":"publisher","DOI":"10.1186\/s40164-022-00278-x"},{"key":"e_1_2_14_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.blre.2022.101035"},{"key":"e_1_2_14_8_2","doi-asserted-by":"publisher","DOI":"10.1172\/jci.insight.89798"},{"key":"e_1_2_14_9_2","doi-asserted-by":"publisher","DOI":"10.1182\/bloodadvances.2022007296"},{"key":"e_1_2_14_10_2","doi-asserted-by":"publisher","DOI":"10.1182\/bloodadvances.2019000791"},{"key":"e_1_2_14_11_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41588-021-00978-w"},{"key":"e_1_2_14_12_2","doi-asserted-by":"publisher","DOI":"10.1182\/bloodadvances.2021005620"},{"key":"e_1_2_14_13_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41409-018-0204-7"},{"key":"e_1_2_14_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bbmt.2015.09.001"},{"key":"e_1_2_14_15_2","first-page":"825","article-title":"1994 Consensus Conference on Acute GVHD Grading","volume":"15","author":"Przepiorka D.","year":"1995","journal-title":"Bone Marrow Transplant"},{"key":"e_1_2_14_16_2","doi-asserted-by":"publisher","DOI":"10.1093\/braincomms\/fcae393"},{"key":"e_1_2_14_17_2","unstructured":"R Core Team R: A Language and Environment for Statistical Computing 2022 Published onlinehttps:\/\/www.R-project.org\/."},{"key":"e_1_2_14_18_2","doi-asserted-by":"publisher","DOI":"10.1093\/ije\/dyx034"},{"key":"e_1_2_14_19_2","unstructured":"BurgessS.andYavorskaO. 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