Patient Survival After Kidney Transplantation: Important Role of Graft-sustaining Factors as Determined by Predictive Modeling Using Random Survival Forest Analysis
| Autor: | Wilfried Gwinner, Michael Marschollek, Tanja Abeling, Matthias Gietzelt, Irina Scheffner |
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| Rok vydání: | 2020 |
| Předmět: |
Graft Rejection
Male medicine.medical_specialty Biopsy Urinary system Disease Type 2 diabetes 030230 surgery Kidney Malignancy Risk Assessment 03 medical and health sciences 0302 clinical medicine Risk Factors Germany Internal medicine Humans Medicine Registries Kidney transplantation Retrospective Studies Transplantation business.industry Incidence Graft Survival Retrospective cohort study Middle Aged medicine.disease Kidney Transplantation Transplant Recipients Survival Rate Cohort Female 030211 gastroenterology & hepatology business Risk assessment Follow-Up Studies Forecasting |
| Zdroj: | Transplantation. 104:1095-1107 |
| ISSN: | 0041-1337 |
| DOI: | 10.1097/tp.0000000000002922 |
| Popis: | Background Identification of the relevant factors for death can improve patient's individual risk assessment and decision making. A well-documented patient cohort (n = 892) in a renal transplant program with protocol biopsies was used to establish multivariable models for risk assessment at 3 and 12 months posttransplantation by random survival forest analysis. Methods Patients transplanted between 2000 and 2007 were observed for up to 11 years. Loss to follow-up was negligible (n = 15). A total of 2251 protocol biopsies and 1214 biopsies for cause were performed. All rejections and clinical borderline rejections in protocol biopsies were treated. Results Ten-year patient survival was 78%, with inferior survival of patients with graft loss. Using all pre- and posttransplant variables until 3 and 12 months (n = 65), the obtained models showed good performance to predict death (concordance index: 0.77-0.78). Validation with a separate cohort of patients (n = 349) showed a concordance index of 0.76 and good discrimination of risks by the models, despite substantial differences in clinical variables. Random survival forest analysis produced robust models over a wide range of parameter settings. Besides well-established risk factors like age, cardiovascular disease, type 2 diabetes, and graft function, posttransplant urinary tract infection and rejection treatment were important factors. Urinary tract infection and rejection treatment were not specifically associated with death due to infection or malignancy but correlated strongly with inferior graft function and graft loss. Conclusions The established models indicate the important areas that need special attention in the care of renal transplant patients, particularly modifiable factors like graft rejection and urinary tract infection. |
| Databáze: | OpenAIRE |
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