{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T18:19:50Z","timestamp":1787681990714,"version":"build-2784847793"},"reference-count":35,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T00:00:00Z","timestamp":1785888000000},"content-version":"vor","delay-in-days":4,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Clinical Transplantation"],"published-print":{"date-parts":[[2026,8]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Post\u2010transplant fractures are associated with increased mortality and reduced graft survival. Although computed tomography (CT)\u2010derived metrics, such as vertebral trabecular bone attenuation and abdominal aortic calcification have been reported as useful for assessing osteoporosis, their utility in predicting post\u2010transplant fractures in kidney transplant recipients (KTRs) has not been explored. This study examined pre\u2010transplant factors, including CT imaging parameters, associated with post\u2010transplant fractures using machine learning techniques.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This retrospective study included 111 primary living\u2010donor KTRs who underwent preoperative CT at our hospital between 2009 and 2023. Aorto\u2010iliac calcification was quantified using the Agatston score. Vertebral trabecular bone CT attenuation values (Hounsfield units [HU]) were recorded at T12 to L5 levels. Machine learning with SHapley Additive exPlanations (SHAP) was used to identify the top predictors of fracture.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The median follow\u2010up period was 69 months. Twenty\u2010four recipients experienced fractures: hip (\n                      <jats:italic>n<\/jats:italic>\n                      = 3), leg (\n                      <jats:italic>n<\/jats:italic>\n                      = 6), arm (\n                      <jats:italic>n<\/jats:italic>\n                      = 5), rib (\n                      <jats:italic>n<\/jats:italic>\n                      = 3), and vertebrae (\n                      <jats:italic>n<\/jats:italic>\n                      = 9). The L1 vertebral CT attenuation value and aortic segment calcification score emerged as the top predictors of fracture. Based on SHAP cutoffs (L1 CT = 104 HU; Agatston score = 504), recipients were categorized into Group 1: high L1\/low calcification; Group 2: high L1\/high calcification; and Group 3: low L1\/high calcification. Compared to Group 1, fracture risk was higher in Group 2 (Subdistribution hazard ratio [SHR] 3.06;\n                      <jats:italic>p<\/jats:italic>\n                      = 0.034) and markedly higher in Group 3 (SHR 11.66;\n                      <jats:italic>p<\/jats:italic>\n                      &lt; 0.0001).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Pre\u2010transplant lower vertebral trabecular bone attenuation and higher aortic calcification on CT are strongly associated with post\u2010transplant fractures.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/ctr.70635","type":"journal-article","created":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T15:06:56Z","timestamp":1785942416000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Preoperative Computed Tomography Aortic Calcification and Attenuation of the Vertebral Body Predict Fractures Post\u2010Kidney Transplantation Using Machine Learning"],"prefix":"10.1111","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9065-7828","authenticated-orcid":false,"given":"Mitsuru","family":"Tomizawa","sequence":"first","affiliation":[{"name":"Department of Urology Nara Medical University  Kashihara Nara Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9514-9964","authenticated-orcid":false,"given":"Shunta","family":"Hori","sequence":"additional","affiliation":[{"name":"Department of Urology Nara Medical University  Kashihara Nara Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kuniaki","family":"Inoue","sequence":"additional","affiliation":[{"name":"Department of Urology Nara Medical University  Kashihara Nara 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