{"title":"预测活体肾移植术后血清肌酐水平:供受体肌肉质量差异的作用。","authors":"Ezgi Avanaz, Ali Avanaz","doi":"10.1016/j.transproceed.2025.05.001","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>In living donor kidney transplantation (LDKT), postoperative serum creatinine (SCr) levels in the recipients are influenced by muscle mass, which can be assessed via psoas muscle area (PMA) measurements. This study aimed to create a formula to predict the postoperative SCr levels by evaluating the difference in muscle mass between donors and recipients.</p><p><strong>Methods: </strong>We retrospectively analyzed data from patients aged 18 years and older who underwent LDKT between January 2020 and December 2022. Recipients and donors with preoperative magnetic resonance imaging (MRI) or computed tomography (CT) scans were included in the PMA measurements. A total of 67 patients were analyzed.</p><p><strong>Results: </strong>The recipients had a mean age of 42 ± 12.8 years and a mean postoperative SCr of 1.24 ± 0.33 mg/dl. Multivariate analysis revealed that donor age and the difference in the PMA between recipients and donors were significant predictors of postoperative SCr levels. The derived formula is as follows: Recipient postoperative SCr = 0.320 + (0.016 × recipient-donor PMA difference/100) + (0.006 × donor age).</p><p><strong>Conclusion: </strong>This study highlights that considering the difference in muscle mass between donors and recipients can enhance the prediction of postoperative SCr levels. Further multicenter studies are needed to validate and refine this predictive model.</p>","PeriodicalId":94258,"journal":{"name":"Transplantation proceedings","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Predicting Postoperative Serum Creatinine Levels in Living Donor Kidney Transplantation: The Role of Donor-Recipient Muscle Mass Differences.\",\"authors\":\"Ezgi Avanaz, Ali Avanaz\",\"doi\":\"10.1016/j.transproceed.2025.05.001\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>In living donor kidney transplantation (LDKT), postoperative serum creatinine (SCr) levels in the recipients are influenced by muscle mass, which can be assessed via psoas muscle area (PMA) measurements. This study aimed to create a formula to predict the postoperative SCr levels by evaluating the difference in muscle mass between donors and recipients.</p><p><strong>Methods: </strong>We retrospectively analyzed data from patients aged 18 years and older who underwent LDKT between January 2020 and December 2022. Recipients and donors with preoperative magnetic resonance imaging (MRI) or computed tomography (CT) scans were included in the PMA measurements. A total of 67 patients were analyzed.</p><p><strong>Results: </strong>The recipients had a mean age of 42 ± 12.8 years and a mean postoperative SCr of 1.24 ± 0.33 mg/dl. Multivariate analysis revealed that donor age and the difference in the PMA between recipients and donors were significant predictors of postoperative SCr levels. The derived formula is as follows: Recipient postoperative SCr = 0.320 + (0.016 × recipient-donor PMA difference/100) + (0.006 × donor age).</p><p><strong>Conclusion: </strong>This study highlights that considering the difference in muscle mass between donors and recipients can enhance the prediction of postoperative SCr levels. Further multicenter studies are needed to validate and refine this predictive model.</p>\",\"PeriodicalId\":94258,\"journal\":{\"name\":\"Transplantation proceedings\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-06-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Transplantation proceedings\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1016/j.transproceed.2025.05.001\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transplantation proceedings","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/j.transproceed.2025.05.001","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Predicting Postoperative Serum Creatinine Levels in Living Donor Kidney Transplantation: The Role of Donor-Recipient Muscle Mass Differences.
Purpose: In living donor kidney transplantation (LDKT), postoperative serum creatinine (SCr) levels in the recipients are influenced by muscle mass, which can be assessed via psoas muscle area (PMA) measurements. This study aimed to create a formula to predict the postoperative SCr levels by evaluating the difference in muscle mass between donors and recipients.
Methods: We retrospectively analyzed data from patients aged 18 years and older who underwent LDKT between January 2020 and December 2022. Recipients and donors with preoperative magnetic resonance imaging (MRI) or computed tomography (CT) scans were included in the PMA measurements. A total of 67 patients were analyzed.
Results: The recipients had a mean age of 42 ± 12.8 years and a mean postoperative SCr of 1.24 ± 0.33 mg/dl. Multivariate analysis revealed that donor age and the difference in the PMA between recipients and donors were significant predictors of postoperative SCr levels. The derived formula is as follows: Recipient postoperative SCr = 0.320 + (0.016 × recipient-donor PMA difference/100) + (0.006 × donor age).
Conclusion: This study highlights that considering the difference in muscle mass between donors and recipients can enhance the prediction of postoperative SCr levels. Further multicenter studies are needed to validate and refine this predictive model.