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Prediction of chronic kidney disease after orthotopic liver transplantation: development and validation of a nomogram model

BACKGROUND: We aimed to develop and validate a nomogram model for predicting CKD after orthotopic liver transplantation (OLT). METHODS: The retrospective data of 399 patients who underwent transplantation and were followed in our centre were collected. They were randomly assigned to the training set...

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Autores principales: Guo, Dandan, Wang, Huifang, Liu, Jun, Liu, Hang, Zhang, Ming, Fu, Zixuan, Liu, Xuemei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8761273/
https://www.ncbi.nlm.nih.gov/pubmed/35034618
http://dx.doi.org/10.1186/s12882-021-02650-1
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author Guo, Dandan
Wang, Huifang
Liu, Jun
Liu, Hang
Zhang, Ming
Fu, Zixuan
Liu, Xuemei
author_facet Guo, Dandan
Wang, Huifang
Liu, Jun
Liu, Hang
Zhang, Ming
Fu, Zixuan
Liu, Xuemei
author_sort Guo, Dandan
collection PubMed
description BACKGROUND: We aimed to develop and validate a nomogram model for predicting CKD after orthotopic liver transplantation (OLT). METHODS: The retrospective data of 399 patients who underwent transplantation and were followed in our centre were collected. They were randomly assigned to the training set (n = 293) and validation set (n = 106). Multivariable Cox regression analysis was performed in the training set to identify predictors of CKD. According to the Cox regression analysis results, a nomogram model was developed and validated. The renal function of recipients was monitored, and the long-term survival prognosis was assessed. RESULTS: The incidence of CKD at 5 years after OLT was 25.6%. Cox regression analysis identified several predictors of post-OLT CKD, including recipient age at surgery (HR 1.036, 95% CI 1.006-1.068; p = 0.018), female sex (HR 2.867, 95% CI 1.709-4.810; p < 0.001), preoperative hypertension (HR 1.670, 95% CI 0.962-2.898; p = 0.068), preoperative eGFR (HR 0.996, 95% CI 0.991-1.001; p = 0.143), uric acid at 3 months (HR 1.002, 95% CI 1.001-1.004; p = 0.028), haemoglobin at 3 months (HR 0.970, 95% CI 0.956-0.983; p < 0.001), and average concentration of cyclosporine A at 3 months (HR 1.002, 95% CI 1.001-1.003; p < 0.001). According to these parameters, a nomogram model for predicting CKD after OLT was constructed and validated. The C-indices were 0.75 and 0.80 in the training and validation sets. The calibration curve of the nomogram showed that the CKD probabilities predicted by the nomogram agreed with the observed probabilities at 1, 3, and 5 years after OLT (p > 0.05). Renal function declined slowly year by year, and there were significant differences between patients divided by these predictors. Kaplan-Meier survival analysis showed that the survival prognosis of recipients decreased significantly with the progression of renal function. CONCLUSIONS: With excellent predictive abilities, the nomogram may be a simple and reliable tool to identify patients at high risk for CKD and poor long-term prognosis after OLT.
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spelling pubmed-87612732022-01-18 Prediction of chronic kidney disease after orthotopic liver transplantation: development and validation of a nomogram model Guo, Dandan Wang, Huifang Liu, Jun Liu, Hang Zhang, Ming Fu, Zixuan Liu, Xuemei BMC Nephrol Research BACKGROUND: We aimed to develop and validate a nomogram model for predicting CKD after orthotopic liver transplantation (OLT). METHODS: The retrospective data of 399 patients who underwent transplantation and were followed in our centre were collected. They were randomly assigned to the training set (n = 293) and validation set (n = 106). Multivariable Cox regression analysis was performed in the training set to identify predictors of CKD. According to the Cox regression analysis results, a nomogram model was developed and validated. The renal function of recipients was monitored, and the long-term survival prognosis was assessed. RESULTS: The incidence of CKD at 5 years after OLT was 25.6%. Cox regression analysis identified several predictors of post-OLT CKD, including recipient age at surgery (HR 1.036, 95% CI 1.006-1.068; p = 0.018), female sex (HR 2.867, 95% CI 1.709-4.810; p < 0.001), preoperative hypertension (HR 1.670, 95% CI 0.962-2.898; p = 0.068), preoperative eGFR (HR 0.996, 95% CI 0.991-1.001; p = 0.143), uric acid at 3 months (HR 1.002, 95% CI 1.001-1.004; p = 0.028), haemoglobin at 3 months (HR 0.970, 95% CI 0.956-0.983; p < 0.001), and average concentration of cyclosporine A at 3 months (HR 1.002, 95% CI 1.001-1.003; p < 0.001). According to these parameters, a nomogram model for predicting CKD after OLT was constructed and validated. The C-indices were 0.75 and 0.80 in the training and validation sets. The calibration curve of the nomogram showed that the CKD probabilities predicted by the nomogram agreed with the observed probabilities at 1, 3, and 5 years after OLT (p > 0.05). Renal function declined slowly year by year, and there were significant differences between patients divided by these predictors. Kaplan-Meier survival analysis showed that the survival prognosis of recipients decreased significantly with the progression of renal function. CONCLUSIONS: With excellent predictive abilities, the nomogram may be a simple and reliable tool to identify patients at high risk for CKD and poor long-term prognosis after OLT. BioMed Central 2022-01-16 /pmc/articles/PMC8761273/ /pubmed/35034618 http://dx.doi.org/10.1186/s12882-021-02650-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Guo, Dandan
Wang, Huifang
Liu, Jun
Liu, Hang
Zhang, Ming
Fu, Zixuan
Liu, Xuemei
Prediction of chronic kidney disease after orthotopic liver transplantation: development and validation of a nomogram model
title Prediction of chronic kidney disease after orthotopic liver transplantation: development and validation of a nomogram model
title_full Prediction of chronic kidney disease after orthotopic liver transplantation: development and validation of a nomogram model
title_fullStr Prediction of chronic kidney disease after orthotopic liver transplantation: development and validation of a nomogram model
title_full_unstemmed Prediction of chronic kidney disease after orthotopic liver transplantation: development and validation of a nomogram model
title_short Prediction of chronic kidney disease after orthotopic liver transplantation: development and validation of a nomogram model
title_sort prediction of chronic kidney disease after orthotopic liver transplantation: development and validation of a nomogram model
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8761273/
https://www.ncbi.nlm.nih.gov/pubmed/35034618
http://dx.doi.org/10.1186/s12882-021-02650-1
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