Evidence map›Paper›PMID 42555277›Full record

ArticleClinical transplantation2026

Preoperative Computed Tomography Aortic Calcification and Attenuation of the Vertebral Body Predict Fractures Post-Kidney Transplantation Using Machine Learning.

Mitsuru Tomizawa, Shunta Hori, Kuniaki Inoue, Tatsuo Yoneda, Tetsuya Tachiiri, Takahiro Nakai, Kenta Onishi, Yosuke Morizawa, Daisuke Gotoh, Yasushi Nakai and 3 more

Abstract read
In one paragraph

Article in Clinical transplantation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Mitsuru TomizawaDepartment of Urology, Nara Medical University, Kashihara, Nara, Japan.ORCID https://orcid.org/0000-0002-9065-7828
Shunta HoriDepartment of Urology, Nara Medical University, Kashihara, Nara, Japan.ORCID https://orcid.org/0000-0001-9514-9964
Kuniaki InoueDepartment of Urology, Nara Medical University, Kashihara, Nara, Japan.
Tatsuo YonedaDepartment of Urology, Nara Medical University, Kashihara, Nara, Japan.ORCID https://orcid.org/0009-0005-8119-3784
Tetsuya TachiiriDepartment of Diagnostic and Interventional Radiology, Nara Medical University, Kashihara, Nara, Japan.ORCID https://orcid.org/0000-0002-4462-2464
Takahiro NakaiDepartment of Diagnostic and Interventional Radiology, Nara Medical University, Kashihara, Nara, Japan.ORCID https://orcid.org/0009-0007-7808-7272
Kenta OnishiDepartment of Urology, Nara Medical University, Kashihara, Nara, Japan.ORCID https://orcid.org/0000-0003-1767-9905
Yosuke MorizawaDepartment of Urology, Nara Medical University, Kashihara, Nara, Japan.
Daisuke GotohDepartment of Urology, Nara Medical University, Kashihara, Nara, Japan.
Yasushi NakaiDepartment of Urology, Nara Medical University, Kashihara, Nara, Japan.ORCID https://orcid.org/0000-0002-7579-0372
Makito MiyakeDepartment of Urology, Nara Medical University, Kashihara, Nara, Japan.
Nobumichi TanakaDepartment of Urology, Nara Medical University, Kashihara, Nara, Japan.
Kiyohide FujimotoDepartment of Urology, Nara Medical University, Kashihara, Nara, Japan.ORCID https://orcid.org/0000-0003-1507-2464

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionPost-transplant fractures are associated with increased mortality and reduced graft survival. Although computed tomography (CT)-derived metrics, such as vertebral trabecular bone attenuation and abdominal aortic calcification have been reported as useful for assessing osteoporosis, their utility in predicting post-transplant fractures in kidney transplant recipients (KTRs) has not been explored. This study examined pre-transplant factors, including CT imaging parameters, associated with post-transplant fractures using machine learning techniques.

methodsThis retrospective study included 111 primary living-donor KTRs who underwent preoperative CT at our hospital between 2009 and 2023. Aorto-iliac 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.

resultsThe median follow-up period was 69 months. Twenty-four recipients experienced fractures: hip (n = 3), leg (n = 6), arm (n = 5), rib (n = 3), and vertebrae (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; p = 0.034) and markedly higher in Group 3 (SHR 11.66; p < 0.0001).

conclusionPre-transplant lower vertebral trabecular bone attenuation and higher aortic calcification on CT are strongly associated with post-transplant fractures.

Indexed as

Fractures, BoneKidney Failure, ChronicKidney TransplantationMachine LearningPostoperative ComplicationsSpinal FracturesTomography, X-Ray ComputedVascular CalcificationVertebral BodyAdultFemaleFollow-Up StudiesGraft SurvivalHumansMaleMiddle Agedaortabone fracturecancellous boneend‐stage renal diseasekidney transplantationvascular calcificationvertebra

Identifiers

PMID42555277
PMCPMC13440457

What Socratic holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.