Prediction model of delayed graft function based on clinical characteristics combined with serum IL-2 levels
Abstract Background Kidney transplantation is an effective treatment for end-stage renal disease (ESRD). Delayed graft function (DGF) is a common complication after kidney transplantation and exerts substantial effects on graft function and long-term graft survival. Therefore, the construction of an...
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| Format: | Article |
| Language: | English |
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BMC
2022-08-01
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| Series: | BMC Nephrology |
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| Online Access: | https://doi.org/10.1186/s12882-022-02908-2 |
| _version_ | 1811340367983804416 |
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| author | Shitao Zhao Yuan Liu Chen Zhou Zide Chen Zeyu Cai JiaLiang Han Jiansheng Xiao Qi Xiao |
| author_facet | Shitao Zhao Yuan Liu Chen Zhou Zide Chen Zeyu Cai JiaLiang Han Jiansheng Xiao Qi Xiao |
| author_sort | Shitao Zhao |
| collection | DOAJ |
| description | Abstract Background Kidney transplantation is an effective treatment for end-stage renal disease (ESRD). Delayed graft function (DGF) is a common complication after kidney transplantation and exerts substantial effects on graft function and long-term graft survival. Therefore, the construction of an effective model to predict the occurrence of DGF is particularly important. Methods Seventy-one patients receiving their first kidney transplant at the First Affiliated Hospital of Nanchang University from October 2020 to October 2021 were enrolled in the discovery cohort. Based on clinical characteristics and serum markers, a logistic regression model was used to simulate the risk of DGF in the discovery cohort. The DGF prediction model was named the prediction system and was composed of risk factors related to DGF. Thirty-two patients receiving a kidney transplant at the First Affiliated Hospital of Nanchang University from October 2021 to February 2022 were enrolled in the validation cohort. The validation cohort was used to verify the accuracy and reliability of the prediction model. Results Cold ischemia time (CIT), donor history of diabetes mellitus, donor interleukin-2 (IL-2) level and donor terminal creatinine level constitute the prediction system. In the validation test, the area under the receiver operating characteristic curve (AUC) was 0.867 for the prediction system, and good calibration of the model was confirmed in the validation cohort. Conclusions This study constructed a reliable and highly accurate prediction model that provides a practical tool for predicting DGF. Additionally, IL-2 participates in the kidney injury process and may be a potential marker of kidney injury. |
| first_indexed | 2024-04-13T18:41:00Z |
| format | Article |
| id | doaj.art-9ddf1a42db8c4f1c96333f8b3dfaa39f |
| institution | Directory Open Access Journal |
| issn | 1471-2369 |
| language | English |
| last_indexed | 2024-04-13T18:41:00Z |
| publishDate | 2022-08-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Nephrology |
| spelling | doaj.art-9ddf1a42db8c4f1c96333f8b3dfaa39f2022-12-22T02:34:44ZengBMCBMC Nephrology1471-23692022-08-0123111310.1186/s12882-022-02908-2Prediction model of delayed graft function based on clinical characteristics combined with serum IL-2 levelsShitao Zhao0Yuan Liu1Chen Zhou2Zide Chen3Zeyu Cai4JiaLiang Han5Jiansheng Xiao6Qi Xiao7Department of Transplantation, The First Affiliated Hospital of Nanchang UniversityDepartment of Transplantation, The First Affiliated Hospital of Nanchang UniversityDepartment of Transplantation, The First Affiliated Hospital of Nanchang UniversityDepartment of Transplantation, The First Affiliated Hospital of Nanchang UniversityDepartment of Transplantation, The First Affiliated Hospital of Nanchang UniversityDepartment of Transplantation, The First Affiliated Hospital of Nanchang UniversityDepartment of Transplantation, The First Affiliated Hospital of Nanchang UniversityDepartment of Transplantation, The First Affiliated Hospital of Nanchang UniversityAbstract Background Kidney transplantation is an effective treatment for end-stage renal disease (ESRD). Delayed graft function (DGF) is a common complication after kidney transplantation and exerts substantial effects on graft function and long-term graft survival. Therefore, the construction of an effective model to predict the occurrence of DGF is particularly important. Methods Seventy-one patients receiving their first kidney transplant at the First Affiliated Hospital of Nanchang University from October 2020 to October 2021 were enrolled in the discovery cohort. Based on clinical characteristics and serum markers, a logistic regression model was used to simulate the risk of DGF in the discovery cohort. The DGF prediction model was named the prediction system and was composed of risk factors related to DGF. Thirty-two patients receiving a kidney transplant at the First Affiliated Hospital of Nanchang University from October 2021 to February 2022 were enrolled in the validation cohort. The validation cohort was used to verify the accuracy and reliability of the prediction model. Results Cold ischemia time (CIT), donor history of diabetes mellitus, donor interleukin-2 (IL-2) level and donor terminal creatinine level constitute the prediction system. In the validation test, the area under the receiver operating characteristic curve (AUC) was 0.867 for the prediction system, and good calibration of the model was confirmed in the validation cohort. Conclusions This study constructed a reliable and highly accurate prediction model that provides a practical tool for predicting DGF. Additionally, IL-2 participates in the kidney injury process and may be a potential marker of kidney injury.https://doi.org/10.1186/s12882-022-02908-2Kidney transplantationDelayed graft functionInterleukin-2Cold ischemia timeDiabetesCreatinine |
| spellingShingle | Shitao Zhao Yuan Liu Chen Zhou Zide Chen Zeyu Cai JiaLiang Han Jiansheng Xiao Qi Xiao Prediction model of delayed graft function based on clinical characteristics combined with serum IL-2 levels BMC Nephrology Kidney transplantation Delayed graft function Interleukin-2 Cold ischemia time Diabetes Creatinine |
| title | Prediction model of delayed graft function based on clinical characteristics combined with serum IL-2 levels |
| title_full | Prediction model of delayed graft function based on clinical characteristics combined with serum IL-2 levels |
| title_fullStr | Prediction model of delayed graft function based on clinical characteristics combined with serum IL-2 levels |
| title_full_unstemmed | Prediction model of delayed graft function based on clinical characteristics combined with serum IL-2 levels |
| title_short | Prediction model of delayed graft function based on clinical characteristics combined with serum IL-2 levels |
| title_sort | prediction model of delayed graft function based on clinical characteristics combined with serum il 2 levels |
| topic | Kidney transplantation Delayed graft function Interleukin-2 Cold ischemia time Diabetes Creatinine |
| url | https://doi.org/10.1186/s12882-022-02908-2 |
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