Waiting Time for Second Kidney Transplantation and Mortality

Background and objectivesThe median kidney transplant half-life is 10–15 years. Because of the scarcity of donor organs and immunologic sensitization of candidates for retransplantation, there is a need for quantitative information on if and when a second transplantation is no longer associated with a lower risk of mortality compared with waitlisted patients treated by dialysis. Therefore, we investigated the association of time on waiting list with patient survival in patients who received a second transplantation versus remaining on the waiting list.Design, setting, participants, & measurementsIn this retrospective study using target trial emulation, we analyzed data of 2346 patients from the Austrian Dialysis and Transplant Registry and Eurotransplant with a failed first graft, aged over 18 years, and waitlisted for a second kidney transplantation in Austria during the years 1980–2019. The differences in restricted mean survival time and hazard ratios for all-cause mortality comparing the treatment strategies “retransplant” versus “remain waitlisted with maintenance dialysis” are reported for different waiting times after first graft loss.ResultsSecond kidney transplantation showed a longer restricted mean survival time at 10 years of follow-up compared with remaining on the waiting list (5.8 life months gained; 95% confidence interval, 0.9 to 11.1). This survival difference was diminished in patients with longer waiting time after loss of the first allograft; restricted mean survival time differences at 10 years were 8.0 (95% confidence interval, 1.9 to 14.0) and 0.1 life months gained (95% confidence interval, −14.3 to 15.2) for patients with waiting time for retransplantation of <1 and 8 years, respectively.ConclusionsSecond kidney transplant is associated with patient survival compared with remaining waitlisted and treatment by dialysis, but the survival difference diminishes with longer waiting time.

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FC 119SURVIVAL BENEFIT OF KIDNEY TRANSPLANTATION COMPARED TO REMAINING ON WAITLIST ACROSS DIFFERENT AGES OF TRANSPLANT CANDIDATES: A RETROSPECTIVE COHORT STUDY USING TARGET TRIAL EMULATION

Nephrology Dialysis Transplantation ◽

10.1093/ndt/gfab146.001 ◽

2021 ◽

Vol 36 (Supplement_1) ◽

Author(s):

Christine Wallisch ◽

Susanne Strohmaier ◽

Michael Kammer ◽

Georg Heinze ◽

Rainer Oberbauer ◽

...

Keyword(s):

Kidney Transplantation ◽

Survival Time ◽

Kidney Transplant ◽

Survival Benefit ◽

Probability Weighting ◽

Target Trial ◽

Inverse Probability ◽

Mean Survival Time ◽

Restricted Mean Survival Time ◽

Transplant Candidate

Abstract Background and Aims Kidney transplantation is considered to be the optimal treatment strategy for eligible end stage renal disease patients. However, the body of evidence to underpin the anticipated survival advantage for kidney transplant recipients is weak, as random treatment allocation to either kidney transplantation or remaining on dialysis is not feasible and previously reported results obtained from observational studies did not allow for causal interpretation. The aim of this study is to investigate survival differences of kidney transplantation compared to remaining waitlisted on dialysis across different transplant candidate ages applying causal inference methodology. Method We conducted a retrospective cohort study using the Austrian Dialysis and Transplant Registry. We included all maintenance dialysis patients who were waitlisted for their first kidney transplant between January 2000 and December 2018 and utilized repeated updates on waitlisting status and relevant covariates. To estimate the causal effect of kidney transplantation compared to remaining waitlisted on all-cause survival we applied a sequential Cox approach mimicking a series of target trials, where each trial started at the time of a transplantation. In each of these emulated trials transplanted patients were classified as treated and patients with current active waitlisting status as controls, and the groups were balanced for covariates by inverse probability weighting. Controls who were transplanted at later times were censored but assigned to the treated group in a later target trial of the series. All trials were combined into a single data set and analyzed by a Cox proportional hazards model using inverse probability weighting also to adjust for artificial censoring. Additionally, we evaluated potential effect modifications by age at trial initiation (continuous) and stratified our analyses by time on waitlist before trial initiation (up to 1 year, between 1 and 2 years, and more than 2 years). Results are reported as hazard ratios (HRs), 5-year survival probabilities and restricted mean survival time together with respective bootstrap confidence intervals (CIs). Results The study cohort consisted of 4206 patients, of whom one third were women and the mean age was 52 years. In total, 3399 patients (81%) received a transplant and 1256 patients died. The median time from waitlisting to transplantation was 1.8 years. Overall, patients who received a kidney transplant had a significant survival benefit compared to patients who remained waitlisted (HR 0.36, 95% CI 0.29 to 0.43). Assessing survival across different ages showed a significant benefit for kidney transplantation for patients between 32 and 77 years of age at time of transplantation (e.g. HR at age of 70: 0.43, 95% CI 0.33 to 0.54). For older and younger patients our analysis did not provide definitive conclusions due to limited sample sizes. Transplanted patients had higher predicted survival and longer restricted mean survival time compared to patients remaining waitlisted. For example, within 5 years after engraftment, a transplanted patient 70 years of age at trial initiation had a 0.28 higher survival probability (95% CI 0.20-0.37) and was expected to gain 0.75 years of survival time. Our stratified analyses showed a survival benefit for kidney transplantation regardless of time on waitlist before trial initiation across all ages. Conclusion Our study provides robust evidence based on state-of-the-art causal inference methodology for increased survival after kidney transplantation across different transplant candidate ages and irrespective of time on waiting list.

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Estimation on conditional restricted mean survival time with counting process

Journal of Biopharmaceutical Statistics ◽

10.1080/10543406.2020.1814799 ◽

2020 ◽

pp. 1-15

Author(s):

Junshan Qiu ◽

Dali Zhou ◽

H.M. Jim Hung ◽

John Lawrence ◽

Steven Bai

Keyword(s):

Survival Time ◽

Counting Process ◽

Mean Survival Time ◽

Restricted Mean Survival Time

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A Model-Independent Method to Determine Restricted Mean Survival Time in the Analysis of Survival Curves

SN Comprehensive Clinical Medicine ◽

10.1007/s42399-019-00199-7 ◽

2019 ◽

Vol 2 (1) ◽

pp. 66-68 ◽

Cited By ~ 7

Author(s):

Andrea Messori ◽

Vera Damuzzo ◽

Laura Agnoletto ◽

Luca Leonardi ◽

Marco Chiumente ◽

...

Keyword(s):

Survival Time ◽

Independent Method ◽

Survival Curves ◽

Mean Survival Time ◽

Restricted Mean Survival Time ◽

Model Independent

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Extrapolating Parametric Survival Models in Health Technology Assessment Using Model Averaging: A Simulation Study

Medical Decision Making ◽

10.1177/0272989x21992297 ◽

2021 ◽

Vol 41 (4) ◽

pp. 476-484

Author(s):

Daniel Gallacher ◽

Peter Kimani ◽

Nigel Stallard

Keyword(s):

Survival Time ◽

Survival Data ◽

Mean Squared Error ◽

Model Averaging ◽

Information Criterion ◽

Health Technology ◽

Weighted Averaging ◽

Health Technologies ◽

Mean Survival Time ◽

Restricted Mean Survival Time

Previous work examined the suitability of relying on routine methods of model selection when extrapolating survival data in a health technology appraisal setting. Here we explore solutions to improve reliability of restricted mean survival time (RMST) estimates from trial data by assessing model plausibility and implementing model averaging. We compare our previous methods of selecting a model for extrapolation using the Akaike information criterion (AIC) and Bayesian information criterion (BIC). Our methods of model averaging include using equal weighting across models falling within established threshold ranges for AIC and BIC and using BIC-based weighted averages. We apply our plausibility assessment and implement model averaging to the output of our previous simulations, where 10,000 runs of 12 trial-based scenarios were examined. We demonstrate that removing implausible models from consideration reduces the mean squared error associated with the restricted mean survival time (RMST) estimate from each selection method and increases the percentage of RMST estimates that were within 10% of the RMST from the parameters of the sampling distribution. The methods of averaging were superior to selecting a single optimal extrapolation, aside from some of the exponential scenarios where BIC already selected the exponential model. The averaging methods with wide criterion-based thresholds outperformed BIC-weighted averaging in the majority of scenarios. We conclude that model averaging approaches should feature more widely in the appraisal of health technologies where extrapolation is influential and considerable uncertainty is present. Where data demonstrate complicated underlying hazard rates, funders should account for the additional uncertainty associated with these extrapolations in their decision making. Extended follow-up from trials should be encouraged and used to review prices of therapies to ensure a fair price is paid.

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