Predicting kidney graft function and failure among kidney transplant recipients
Abstract Background Graft loss is a major health concern for kidney transplant (KTx) recipients. It is of clinical interest to develop a prognostic model for both graft function, quantified by estimated glomerular filtration rate (eGFR), and the risk of graft failure. Additionally, the model should...
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2024-12-01
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| Series: | BMC Medical Research Methodology |
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| Online Access: | https://doi.org/10.1186/s12874-024-02445-6 |
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| author | Yi Yao Brad C. Astor Wei Yang Tom Greene Liang Li |
| author_facet | Yi Yao Brad C. Astor Wei Yang Tom Greene Liang Li |
| author_sort | Yi Yao |
| collection | DOAJ |
| description | Abstract Background Graft loss is a major health concern for kidney transplant (KTx) recipients. It is of clinical interest to develop a prognostic model for both graft function, quantified by estimated glomerular filtration rate (eGFR), and the risk of graft failure. Additionally, the model should be dynamic in the sense that it adapts to accumulating longitudinal information, including time-varying at-risk population, predictor-outcome association, and clinical history. Finally, the model should also properly account for the competing risk by death with a functioning graft. A model with the features above is not yet available in the literature and is the focus of this research. Methods We built and internally validated a prediction model on 3,893 patients from the Wisconsin Allograft Recipient Database (WisARD) who had a functioning graft 6 months after kidney transplantation. The landmark analysis approach was used to build a proof-of-concept dynamic prediction model to address the aforementioned methodological issues: the prediction of graft failure, accounted for competing risk of death, as well as the future eGFR value, are updated at each post-transplant time. We used 21 predictors including recipient characteristics, donor characteristics, transplant-related and post-transplant factors, longitudinal eGFR, hospitalization, and rejection history. A sensitivity analysis explored a less conservative variable selection rule that resulted in a more parsimonious model with reduced predictors. Results For prediction up to the next 1 to 5 years, the model achieved high accuracy in predicting graft failure, with the AUC between 0.80 and 0.95, and moderately high accuracy in predicting eGFR, with the root mean squared error between 10 and 18 mL/min/1.73m2 and 70%-90% of predicted eGFR falling within 30% of the observed eGFR. The model demonstrated substantial accuracy improvement compared to a conventional prediction model that used only baseline predictors. Conclusion The model outperformed conventional prediction model that used only baseline predictors. It is a useful tool for patient counseling and clinical management of KTx and is currently available as a web app. |
| format | Article |
| id | doaj-art-71f917b835674bfb8e1374e0eef448f5 |
| institution | Kabale University |
| issn | 1471-2288 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Research Methodology |
| spelling | doaj-art-71f917b835674bfb8e1374e0eef448f52025-01-05T12:34:15ZengBMCBMC Medical Research Methodology1471-22882024-12-012411910.1186/s12874-024-02445-6Predicting kidney graft function and failure among kidney transplant recipientsYi Yao0Brad C. Astor1Wei Yang2Tom Greene3Liang Li4Department of Biostatistics, University of Texas MD Anderson Cancer CenterSchool of Medicine and Public Health, University of Wisconsin-MadisonPerelman School of Medicine, University of PennsylvaniaSchool of Medicine, University of UtahDepartment of Biostatistics, University of Texas MD Anderson Cancer CenterAbstract Background Graft loss is a major health concern for kidney transplant (KTx) recipients. It is of clinical interest to develop a prognostic model for both graft function, quantified by estimated glomerular filtration rate (eGFR), and the risk of graft failure. Additionally, the model should be dynamic in the sense that it adapts to accumulating longitudinal information, including time-varying at-risk population, predictor-outcome association, and clinical history. Finally, the model should also properly account for the competing risk by death with a functioning graft. A model with the features above is not yet available in the literature and is the focus of this research. Methods We built and internally validated a prediction model on 3,893 patients from the Wisconsin Allograft Recipient Database (WisARD) who had a functioning graft 6 months after kidney transplantation. The landmark analysis approach was used to build a proof-of-concept dynamic prediction model to address the aforementioned methodological issues: the prediction of graft failure, accounted for competing risk of death, as well as the future eGFR value, are updated at each post-transplant time. We used 21 predictors including recipient characteristics, donor characteristics, transplant-related and post-transplant factors, longitudinal eGFR, hospitalization, and rejection history. A sensitivity analysis explored a less conservative variable selection rule that resulted in a more parsimonious model with reduced predictors. Results For prediction up to the next 1 to 5 years, the model achieved high accuracy in predicting graft failure, with the AUC between 0.80 and 0.95, and moderately high accuracy in predicting eGFR, with the root mean squared error between 10 and 18 mL/min/1.73m2 and 70%-90% of predicted eGFR falling within 30% of the observed eGFR. The model demonstrated substantial accuracy improvement compared to a conventional prediction model that used only baseline predictors. Conclusion The model outperformed conventional prediction model that used only baseline predictors. It is a useful tool for patient counseling and clinical management of KTx and is currently available as a web app.https://doi.org/10.1186/s12874-024-02445-6Competing riskDynamic predictionGraft failureKidney transplantationRenal function |
| spellingShingle | Yi Yao Brad C. Astor Wei Yang Tom Greene Liang Li Predicting kidney graft function and failure among kidney transplant recipients BMC Medical Research Methodology Competing risk Dynamic prediction Graft failure Kidney transplantation Renal function |
| title | Predicting kidney graft function and failure among kidney transplant recipients |
| title_full | Predicting kidney graft function and failure among kidney transplant recipients |
| title_fullStr | Predicting kidney graft function and failure among kidney transplant recipients |
| title_full_unstemmed | Predicting kidney graft function and failure among kidney transplant recipients |
| title_short | Predicting kidney graft function and failure among kidney transplant recipients |
| title_sort | predicting kidney graft function and failure among kidney transplant recipients |
| topic | Competing risk Dynamic prediction Graft failure Kidney transplantation Renal function |
| url | https://doi.org/10.1186/s12874-024-02445-6 |
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