External Validation of the DynPG for Kidney Transplant Recipients

被引:5
|
作者
Lenain, Remi [1 ]
Dantan, Etienne [2 ]
Giral, Magali [3 ,4 ]
Foucher, Yohann [2 ,5 ]
Asar, Ozgur [6 ]
Naesens, Maarten [7 ,8 ]
Hazzan, Marc [1 ]
Fournier, Marie-Cecile [2 ]
机构
[1] Lille Univ Hosp, Dept Nephrol, Huriez Hosp, Lille, France
[2] Tours Univ, Nantes Univ, UMR 1246, INSERM, Nantes, France
[3] Univ Nantes, RTRS Centaure, CHU Nantes, CRTI UMR 1064,Inserm,ITUN, Nantes, France
[4] Ctr Invest Clin Biotherapie, Nantes, France
[5] CHU Nantes, Nantes, France
[6] Acibadem Mehmet Ali Aydinlar Univ, Dept Biostat & Med Informat, Istanbul, Turkey
[7] Katholieke Univ Leuven, Dept Microbiol Immunol & Transplantat, Leuven, Belgium
[8] Univ Hosp Leuven, Dept Nephrol & Renal Transplantat, Leuven, Belgium
关键词
TIME-TO-EVENT; MULTIVARIABLE PREDICTION MODEL; JOINT MODEL; INDIVIDUAL PROGNOSIS; DIAGNOSIS TRIPOD; ADHERENCE; ACCURACY; PACKAGE;
D O I
10.1097/TP.0000000000003209
中图分类号
R392 [医学免疫学]; Q939.91 [免疫学];
学科分类号
100102 ;
摘要
Background. In kidney transplantation, dynamic prediction of patient and kidney graft survival (DynPG) may help to promote therapeutic alliance by delivering personalized evidence-based information about long-term graft survival for kidney transplant recipients. The objective of the current study is to externally validate the DynPG. Methods. Based on 6 baseline variables, the DynPG can be updated with any new serum creatinine measure available during the follow-up. From an external validation sample of 1637 kidney recipients with a functioning graft at 1-year posttransplantation from 2 European transplantation centers, we assessed the prognostic performance of the DynPG. Results. As one can expect from an external validation sample, differences in several recipient, donor, and transplantation characteristics compared with the learning sample were observed. Patients were mainly transplanted from deceased donors (91.6% versus 84.8%; P < 0.01), were less immunized against HLA class I (18.4% versus 32.7%; P < 0.01) and presented less comorbidities (62.2% for hypertension versus 82.7%, P < 0.01; 25.1% for cardiovascular disease versus 33.9%, P < 0.01). Despite these noteworthy differences, the area under the ROC curve varied from 0.70 (95% confidence interval [CI], 0.64-0.76) to 0.76 (95% CI, 0.64-0.88) for prediction times at 1 and 6 years posttransplantation respectively, and calibration plots revealed reasonably accurate predictions. Conclusions. We validated the prognostic capacities of the DynPG in terms of both discrimination and calibration. Our study showed the robustness of the DynPG for informing both the patient and the physician, and its transportability for a cohort presenting different features than the one used for the DynPG development.
引用
收藏
页码:396 / 403
页数:8
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