Clinical journal of the American Society of Nephrology : CJASN · 2025
Dynamic Risk Prediction of Graft Failure after Deceased Donor Kidney Transplant.
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Thiessen Philbrook Heather, Hu David G, Mohan Sumit, Reese Peter P, Doshi Mona, Malhotra Divyanshu, Mansour Sherry G, Hall Isaac E, Kerr Kathleen F, Parikh Chirag R
Аннотация
KEY POINTS: This risk prediction model can enhance post-transplant care by identifying high-risk recipients for personalized interventions. The predicted risk of 3-year graft failure is dynamically updated with each follow-up eGFR measurement.
BACKGROUND: The identification of kidney transplant recipients at high risk of graft failure enables timely interventions to improve outcomes. We developed and validated a risk prediction model that uses changes in eGFR to dynamically predict 3-year graft failure.
METHODS: The risk prediction model was developed in a prospective multicenter cohort of deceased donor kidney transplant recipients (2010–2013) and validated in three independent cohorts (a registry cohort and two electronic medical record cohorts). The two-stage approach first estimated eGFR trends using a linear mixed-effects model and then used these trends in a logistic regression model to predict 3-year graft failure. eGFR was calculated using race-free equation, and graft failure was defined as the return to dialysis or retransplantation, censored for death. The model can be used at any time within the first 3 years after transplant, and the predicted risk is dynamically updated with each additional eGFR measurement.
RESULTS: In the development cohort (N=1114), 94 (8%) experienced graft failure within 3 years of transplant. The model's predictive accuracy improved over time with the increase in available eGFR measurements. The optimism-corrected area under the ROC curve (AUC) was 0.70 (95% confidence interval, 0.63 to 0.77) at 3 months post-transplant and reached an AUC of 0.90 (95% confidence interval, 0.85 to 0.95) at 30 months post-transplant. Performance was attenuated in the validation cohorts (AUC range 0.60–0.64 at 3 months to 0.72–0.78 at 30 months), likely due to differences in data collection approaches in ascertaining eGFR.
CONCLUSIONS: This risk prediction model has the potential to enhance post-transplant care by identifying high-risk recipients who may benefit from closer monitoring and personalized interventions.
BACKGROUND: The identification of kidney transplant recipients at high risk of graft failure enables timely interventions to improve outcomes. We developed and validated a risk prediction model that uses changes in eGFR to dynamically predict 3-year graft failure.
METHODS: The risk prediction model was developed in a prospective multicenter cohort of deceased donor kidney transplant recipients (2010–2013) and validated in three independent cohorts (a registry cohort and two electronic medical record cohorts). The two-stage approach first estimated eGFR trends using a linear mixed-effects model and then used these trends in a logistic regression model to predict 3-year graft failure. eGFR was calculated using race-free equation, and graft failure was defined as the return to dialysis or retransplantation, censored for death. The model can be used at any time within the first 3 years after transplant, and the predicted risk is dynamically updated with each additional eGFR measurement.
RESULTS: In the development cohort (N=1114), 94 (8%) experienced graft failure within 3 years of transplant. The model's predictive accuracy improved over time with the increase in available eGFR measurements. The optimism-corrected area under the ROC curve (AUC) was 0.70 (95% confidence interval, 0.63 to 0.77) at 3 months post-transplant and reached an AUC of 0.90 (95% confidence interval, 0.85 to 0.95) at 30 months post-transplant. Performance was attenuated in the validation cohorts (AUC range 0.60–0.64 at 3 months to 0.72–0.78 at 30 months), likely due to differences in data collection approaches in ascertaining eGFR.
CONCLUSIONS: This risk prediction model has the potential to enhance post-transplant care by identifying high-risk recipients who may benefit from closer monitoring and personalized interventions.