Literature DB >> 23052636

Pretransplantation soluble CD30 level as a predictor of acute rejection in kidney transplantation: a meta-analysis.

Yile Chen1, Qiang Tai, Shaodong Hong, Yuan Kong, Yushu Shang, Wenhua Liang, Zhiyong Guo, Xiaoshun He.   

Abstract

BACKGROUND: The question of whether high pretransplantation soluble CD30 (sCD30) level can be a predictor of kidney transplant acute rejection (AR) is under debate. Herein, we performed a meta-analysis on the predictive efficacy of sCD30 for AR in renal transplantation.
METHODS: PubMed (1966-2012), EMBASE (1988-2012), and Web of Science (1986-2012) databases were searched for studies concerning the predictive efficacy of sCD30 for AR after kidney transplantation. After a careful review of eligible studies, sensitivity, specificity, and other measures of the accuracy of sCD30 were pooled. A summary receiver operating characteristic curve was used to represent the overall test performance.
RESULTS: Twelve studies enrolling 2507 patients met the inclusion criteria. The pooled estimates for pretransplantation sCD30 in prediction of allograft rejection risk were poor, with a sensitivity of 0.70 (95% confidence interval (CI), 0.66-0.74), a specificity of 0.48 (95% CI, 0.46-0.50), a positive likelihood ratio of 1.35 (95% CI, 1.20-1.53), a negative likelihood ratio of 0.68 (95% CI, 0.55-0.84), and a diagnostic odds ratio of 2.07 (95% CI, 1.54-2.80). The area under curve of the summary receiver operating characteristic curve was 0.60, indicating poor overall accuracy of the serum sCD30 level in the prediction of patients at risk for AR.
CONCLUSIONS: The results of the meta-analysis show that the accuracy of pretransplantation sCD30 for predicting posttransplantation AR was poor. Prospective studies are needed to clarify the usefulness of this test for identifying risks of AR in transplant recipients.

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Year:  2012        PMID: 23052636     DOI: 10.1097/TP.0b013e31826784ad

Source DB:  PubMed          Journal:  Transplantation        ISSN: 0041-1337            Impact factor:   4.939


  9 in total

Review 1.  Biomarkers to detect rejection after kidney transplantation.

Authors:  Vikas R Dharnidharka; Andrew Malone
Journal:  Pediatr Nephrol       Date:  2017-06-19       Impact factor: 3.714

Review 2.  Moving Biomarkers toward Clinical Implementation in Kidney Transplantation.

Authors:  Madhav C Menon; Barbara Murphy; Peter S Heeger
Journal:  J Am Soc Nephrol       Date:  2017-01-06       Impact factor: 10.121

Review 3.  A Rationale for Age-Adapted Immunosuppression in Organ Transplantation.

Authors:  Felix Krenzien; Abdallah ElKhal; Markus Quante; Hector Rodriguez Cetina Biefer; Uehara Hirofumi; Steven Gabardi; Stefan G Tullius
Journal:  Transplantation       Date:  2015-11       Impact factor: 4.939

4.  Five-year clinical effects of donor bone marrow cells infusions in kidney allograft recipients: improved graft function and higher graft survival.

Authors:  Ghasem Solgi; Vijayakrishna Gadi; Biswajit Paul; Joannis Mytilineos; Gholamreza Pourmand; Abdolrasoul Mehrsai; Moslem Ranjbar; Mousa Mohammadnia; Behrouz Nikbin; Ali Akbar Amirzargar
Journal:  Chimerism       Date:  2013-05-31

5.  Soluble co-signaling molecules predict long-term graft outcome in kidney-transplanted patients.

Authors:  Susana G Melendreras; Pablo Martínez-Camblor; Aurora Menéndez; Cristina Bravo-Mendoza; Ana González-Vidal; Eliecer Coto; Carmen Díaz-Corte; Marta Ruiz-Ortega; Carlos López-Larrea; Beatriz Suárez-Álvarez
Journal:  PLoS One       Date:  2014-12-05       Impact factor: 3.240

Review 6.  Clinical immune-monitoring strategies for predicting infection risk in solid organ transplantation.

Authors:  Mario Fernández-Ruiz; Deepali Kumar; Atul Humar
Journal:  Clin Transl Immunology       Date:  2014-02-28

7.  Peritransplant Soluble CD30 as a Risk Factor for Slow Kidney Allograft Function, Early Acute Rejection, Worse Long-Term Allograft Function, and Patients' Survival.

Authors:  Andriy V Trailin; Tetyana I Ostapenko; Tamara N Nykonenko; Svitlana N Nesterenko; Olexandr S Nykonenko
Journal:  Dis Markers       Date:  2017-06-11       Impact factor: 3.434

Review 8.  Pretransplant characteristics of kidney transplant recipients that predict posttransplant outcome.

Authors:  Martin Tepel; Subagini Nagarajah; Qais Saleh; Olivier Thaunat; Stephan J L Bakker; Jacob van den Born; Morten A Karsdal; Federica Genovese; Daniel G K Rasmussen
Journal:  Front Immunol       Date:  2022-07-25       Impact factor: 8.786

9.  PD1-Expressing T Cell Subsets Modify the Rejection Risk in Renal Transplant Patients.

Authors:  Rebecca Pike; Niclas Thomas; Sarita Workman; Lyn Ambrose; David Guzman; Shivajanani Sivakumaran; Margaret Johnson; Douglas Thorburn; Mark Harber; Benny Chain; Hans J Stauss
Journal:  Front Immunol       Date:  2016-04-11       Impact factor: 7.561

  9 in total

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