External Validation of the DynPG for Kidney Transplant Recipients
External Validation of the DynPG for Kidney Transplant Recipients
Tarih
2021-01-01
Yazarlar
Lenain, Remi
Dantan, Etienne
Giral, Magali
Foucher, Yohann
Asar, Ozgur
Naesens, Maarten
Hazzan, Marc
Fournier, Marie-Cecile
Süreli Yayın başlığı
Süreli Yayın ISSN
Cilt Başlığı
Yayınevi
LIPPINCOTT WILLIAMS \& WILKINS
Dergi Adı
TRANSPLANTATION
Özet
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.
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.