Beetz Nick Lasse, Geisel Dominik, Shnayien Seyd, Auer Timo Alexander, Globke Brigitta, Öllinger Robert, Trippel Tobias Daniel, Schachtner Thomas, Fehrenbach Uli
Department of Radiology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 13353 Berlin, Germany.
DZHK (German Center for Cardiovascular Research), 10785 Berlin, Germany.
Biomedicines. 2022 Feb 26;10(3):554. doi: 10.3390/biomedicines10030554.
The Eurotransplant Senior Program allocates kidneys to elderly transplant patients. The aim of this retrospective study is to investigate the use of computed tomography (CT) body composition using artificial intelligence (AI)-based tissue segmentation to predict patient and kidney transplant survival. Body composition at the third lumbar vertebra level was analyzed in 42 kidney transplant recipients. Cox regression analysis of 1-year, 3-year and 5-year patient survival, 1-year, 3-year and 5-year censored kidney transplant survival, and 1-year, 3-year and 5-year uncensored kidney transplant survival was performed. First, the body mass index (BMI), psoas muscle index (PMI), skeletal muscle index (SMI), visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT) served as independent variates. Second, the cut-off values for sarcopenia and obesity served as independent variates. The 1-year uncensored and censored kidney transplant survival was influenced by reduced PMI ( = 0.02 and = 0.03, respectively) and reduced SMI ( = 0.01 and = 0.03, respectively); 3-year uncensored kidney transplant survival was influenced by increased VAT ( = 0.04); and 3-year censored kidney transplant survival was influenced by reduced SMI ( = 0.05). Additionally, sarcopenia influenced 1-year uncensored kidney transplant survival ( = 0.05), whereas obesity influenced 3-year and 5-year uncensored kidney transplant survival. In summary, AI-based body composition analysis may aid in predicting short- and long-term kidney transplant survival.
欧洲器官移植高级项目为老年移植患者分配肾脏。这项回顾性研究的目的是利用基于人工智能（AI）的组织分割技术进行计算机断层扫描（CT）身体成分分析，以预测患者和肾移植的存活率。对42名肾移植受者第三腰椎水平的身体成分进行了分析。对1年、3年和5年的患者存活率、1年、3年和5年的截尾肾移植存活率以及1年、3年和5年的非截尾肾移植存活率进行了Cox回归分析。首先，体重指数（BMI）、腰大肌指数（PMI）、骨骼肌指数（SMI）、内脏脂肪组织（VAT）和皮下脂肪组织（SAT）作为独立变量。其次，少肌症和肥胖的临界值作为独立变量。1年非截尾和截尾肾移植存活率受PMI降低（分别为 = 0.02和 = 0.03）和SMI降低（分别为 = 0.01和 = 0.03）的影响；3年非截尾肾移植存活率受VAT增加（ = 0.04）的影响；3年截尾肾移植存活率受SMI降低（ = 0.05）的影响。此外，少肌症影响1年非截尾肾移植存活率（ = 0.05），而肥胖影响3年和5年非截尾肾移植存活率。总之，基于AI的身体成分分析可能有助于预测肾移植的短期和长期存活率。
