Predictive value of procalcitonin in chronic allograft dysfunction in kidney transplant recipients

Yao,Jing; Jiang,Lijuan; Xue,Dong; Sun,Yanbei

doi:10.3892/etm.2019.8113

Predictive value of procalcitonin in chronic allograft dysfunction in kidney transplant recipients

Authors:
- Jing Yao
- Lijuan Jiang
- Dong Xue
- Yanbei Sun
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Affiliations: Blood Purification Centre, The Affiliated Changzhou No. 2 People's Hospital of Nanjing Medical University, Changzhou, Jiangsu 213003, P.R. China, Department of Clinical Laboratory, The Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu 213003, P.R. China, Department of Urology, The Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu 213003, P.R. China, Department of Nephrology, The Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu 213003, P.R. China
Published online on: October 21, 2019 https://doi.org/10.3892/etm.2019.8113
Pages: 4603-4608
Copyright: © Yao et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

The present study was designed to determine the potential role of circulating procalcitonin (PCT) in predicting chronic allograft dysfunction (CAD) in kidney transplant recipients (KTRs). A total of 87 KTRs were retrospectively analyzed and divided into a CAD and a non‑CAD (normal renal function) group. Clinical features and inflammatory markers were compared between the groups, including PCT, white blood cell count, C‑reactive protein, neutrophil percentage (N%) and lipoprotein(a) [Lp(a)], and the receiver operating characteristic (ROC) curve for CAD prediction was plotted. Univariate and multivariate logistic regression analyses were used to analyze the relevant risk factors for CAD. The results indicated that i) the values of these indicators in the CAD group, including the male ratio, years after transplantation, PCT, N% and Lp(a), were significantly higher than those in the non‑CAD group, while the body mass index, aspartate aminotransferase, high‑density lipoprotein and low‑density lipoprotein were significantly lower; ii) PCT and Lp(a) were able to predict CAD with an area under the ROC curve of 0.893 and 0.770, respectively; iii) multivariate logistic regression analysis of factors influencing CAD in KTRs suggested that elevated PCT was an independent risk factor. In KTRs, PCT was identified as a potential biomarker for predicting CAD.

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