Mortality Prediction after the First Year of Kidney Transplantation: An Observational Study on Two European Cohorts

Abstract

After the first year post transplantation, prognostic mortality scores in kidney transplant recipients can be useful for personalizing medical management. We developed a new prognostic score based on 5 parameters and computable at 1-year post transplantation. The outcome was the time between the first anniversary of the transplantation and the patient’s death with a functioning graft. Afterwards, we appraised the prognostic capacities of this score by estimating time-dependent Receiver Operating Characteristic (ROC) curves from two prospective and multicentric European cohorts: the DIVAT (Données Informatisées et VAlidées en Transplantation) cohort composed of patients transplanted between 2000 and 2012 in 6 French centers; and the STCS (Swiss Transplant Cohort Study) cohort composed of patients transplanted between 2008 and 2012 in 6 Swiss centers. We also compared the results with those of two existing scoring systems: one from Spain (Hernandez et al.) and one from the United States (the Recipient Risk Score, RRS, Baskin-Bey et al.). From the DIVAT validation cohort and for a prognostic time at 10 years, the new prognostic score (AUC = 0.78, 95%CI = [0.69, 0.85]) seemed to present significantly higher prognostic capacities than the scoring system proposed by Hernandez et al. (p = 0.04) and tended to perform better than the initial RRS (p = 0.10). By using the Swiss cohort, the RRS and the the new prognostic score had comparable prognostic capacities at 4 years (AUC = 0.77 and 0.76 respectively, p = 0.31). In addition to the current available scores related to the risk to return in dialysis, we recommend to further study the use of the score we propose or the RRS for a more efficient personalized follow-up of kidney transplant recipients.

Cardiac Troponin T Risk Stratification Model Predicts All-Cause Mortality Following Kidney Transplant

“…Finally, time on dialysis prior to transplant was not a risk factor for death in our cohort. Time on dialysis has been shown to be a significant risk factor for mortality in some studies [8], but not in others [4,14]. Inclusion of ABI results to define PAD in our study and cardiac diseases in the model may account for our findings, given the strong associations between dialysis duration and CV disease.…”

“…Several mortality risk prediction models for kidney transplant recipients exist, which rely on a variety and often large number of clinical and laboratory variables both pre and post transplant and have not incorporated cTnT [5,8,10,14,15,[22][23][24]. The purpose of this study is to examine the role of pre-transplant clinical variables and cTnT to predict 5-and 10-year mortality in a cohort of kidney transplant recipients at a tertiary center.…”

Quantifying and estimating the predictive accuracy for censored time‐to‐event data with competing risks

“…In modern evidence‐based medicine, decisions on a diagnosis or personalized treatment plan are often guided by risk scores generated from prognostic models . Such prognostic risk scores can be either a single risk factor, such as a biomarker, or a risk probability calculated from multiple risk factors.…”

“…In modern evidence-based medicine, decisions on a diagnosis or personalized treatment plan are often guided by risk scores generated from prognostic models. [1][2][3] Such prognostic risk scores can be either a single risk factor, such as a biomarker, or a risk probability calculated from multiple risk factors. For a risk score to be used in clinical practice, its predictive accuracy is often assessed through 2 types of metrics: (1) the discrimination metric, which measures how well the risk score can distinguish subjects with and without the disease condition, and (2) the calibration metric, which measures how well the predicted risk matches the observed risk in the target population.…”

Burden of end‐stage renal disease and evolving challenges in kidney transplantation

“…Importantly, the prospective data can be used to determine and validate risk and prognostic scores. This could potentially be a great asset in organ transplantation where such scores may guide donor–recipient matching at allocation in order to improve patient and graft outcomes . Similarly, survival scores could help decision‐making when confronted with elderly and comorbid patients with ESRD.…”