Competing Risk Analysis in Renal Allograft Survival: A New Perspective to an Old Problem

"Long-term effects of center volume on transplant outcomes in adult kidney transplant recipients"

BACKGROUND

The influence of center volume on kidney transplant outcomes is a topic of ongoing debate. In this study, we employed competing risk analyses to accurately estimate the marginal probability of graft failure in the presence of competing events, such as mortality from other causes with long-term outcomes. The incorporation of immunosuppression protocols and extended follow-up offers additional insights. Our emphasis on long-term follow-up aligns with biological considerations where competing risks play a significant role.

METHODS

We examined data from 219,878 adult kidney-only transplantations across 256 U.S. transplant centers (January 2001-December 2015) sourced from the Organ Procurement and Transplantation Network registry. Centers were classified into quartiles by annual volume: low (Q1 = 28), medium (Q2 = 75), medium-high (Q3 = 121), and high (Q4 = 195). Our study investigated the relationship between center volume and 5-year outcomes, focusing on graft failure and mortality. Sub-population analyses included deceased donors, living donors, diabetic recipients, those with kidney donor profile index >85%, and re-transplants from deceased donors.

RESULTS

Adjusted cause-specific hazard ratios (aCHR) for Five-Year Graft Failure and Patient Death were examined by center volume, with low-volume centers as the reference standard (aCHR: 1.0). In deceased donors, medium-high and high-volume centers showed significantly lower cause-specific hazard ratios for graft failure (medium-high aCHR = 0.892, p<0.001; high aCHR = 0.953, p = 0.149) and patient death (medium-high aCHR = 0.828, p<0.001; high aCHR = 0.898, p = 0.003). Among living donors, no significant differences were found for graft failure, while a trend towards lower cause-specific hazard ratios for patient death was observed in medium-high (aCHR = 0.895, p = 0.107) and high-volume centers (aCHR = 0.88, p = 0.061).

CONCLUSION

Higher center volume is associated with significantly lower cause-specific hazard ratios for graft failure and patient death in deceased donors, while a trend towards reduced cause-specific hazard ratios for patient death is observed in living donors.

Assessing the influence of graft loss on 4-year patient survival after simultaneous pancreas-kidney transplantation: Kaplan-Meier versus Competing Risk Analysis model

BACKGROUND

Graft loss increases the risk of patient death after simultaneous pancreas-kidney (SPK) transplantation. The relative risk of each graft failure is complex due to the influence of several competing events.

METHODS

This retrospective, single-center study compared 4-year patient survival according to the graft status using Kaplan-Meier (KM) and Competing Risk Analysis (CRA). Patient survival was also assessed according to five eras (Era 1: 2001-2003; Era 2: 2004-2006; Era 3: 2007-2009; Era 4: 2010-2012; Era 5: 2012-2015).

RESULTS

Between 2000 and 2015, 432 SPK transplants were performed. Using KM, patient survival was 86.5% for patients without graft loss (n = 333), 93.4% for patients with pancreas graft loss (n = 46), 43.7% for patients with kidney graft loss (n = 16), and 25.4% for patients with pancreas and kidney graft loss (n = 37). Patient survival was underestimated using KM versus CRA methods in patients with pancreas and kidney graft losses (25.4% vs. 36.2%), respectively. Induction with lymphocyte depleting antibodies was associated with 81% reduced risk (HR.19, 95% CI.38-.98, p = .0048), while delayed kidney function (HR 2.94, 95% CI 1.09-7.95, p = .033) and surgical complications (HR 2.94, 95% CI 1.22-7.08, p = .016) were associated with higher risk of death. Four-year patient survival increased from Era 1 to Era 5 (79% vs. 87.9%, p = .047).

CONCLUSION

In this cohort of patients, kidney graft loss, with or without pancreas graft loss, was associated with higher mortality after SPK transplantation. Compared to CRA, the KM model underestimated survival only among patients with pancreas and kidney graft losses. Patient survival increased over time.

A Comparative Study on Graft and Overall Survival Rates Between Diabetic and Nondiabetic Kidney Transplant Patients Through Survival Analysis

Background

Patients with diabetes mellitus (DM) have worse graft and overall survival, but recent evidence suggests that the difference is no longer significant.

Objective

To compare the outcomes between patients with end-stage kidney disease due to DM (ESKD-DM) and ESKD due to nondiabetic etiology (ESKD-non-DM) who underwent kidney transplantation (KT) up to 10 years of follow-up.

Design

Survival analysis of a retrospective cohort.

Setting and Patients

All patients who underwent KT at the Hospital Universitario San Ignacio, Colombia, between 2004 and 2022.

Measurements

Overall and graft survival in ESKD-DM and ESKD-non-DM who received KT. Patients who died with functional graft were censored for the calculation of kidney graft survival.

Methods

Log-rank test, Cox proportional hazards model, and competing risk analysis were used to compare overall and graft survival in patients with ESKD-DM and ESKD-non-DM who underwent KT.

Results

A total of 375 patients were included: 60 (16%) with ESKD-DM and 315 (84%) with ESKD-non-DM. Median follow-up was 83.3 months. Overall survival was lower in patients with ESKD-DM at 5 (75.0% vs 90.8%, P < .001) and 10 years (55.0% vs 86.7%, P < .001). Cardiovascular death was higher in patients with diabetes (27.3% vs 8.2%, P = .021). Death-censored graft survival was similar in both groups (96.7% vs 93.3% at 5 years, P = .324). On multivariate analysis, the factors associated with global survival were DM (hazard ratio [HR] = 2.11, 95% confidence interval [CI] = 1.23-3.60, P = .006), recipient age (HR = 1.05, 95% CI = 1.02-1.08, P < .001), delayed graft function (HR = 2.07, 95% CI = 1.24-3.46, P = .005), and donor age (HR = 1.03, 95% CI = 1.01-1.05, P = .002). In the competing risk analysis, DM was associated with mortality only in the cardiovascular death group (sub-hazard ratio [SHR] = 6.06, 95% CI = 1.01-36.4, P = .049).

Limitations

Change in diabetes treatment received over time and adherence to glycemic targets were not considered. The sample size is relatively small, which limits the precision of our estimates. The Kidney Donor Profile Index and the occurrence of treated acute rejection were not included in the regression models.

Conclusion

Overall survival is lower in patients with diabetes, possibly due to older age and cardiovascular comorbidities. Therefore, patients with diabetes should be followed more closely to control cardiovascular risk factors. However, there is no difference in graft survival.

Effect of pretransplant dialysis vintage on clinical outcomes in deceased donor kidney transplant

The waiting time for deceased donor kidney transplants (DDKT) is increasing. We evaluated DDKT prognosis according to the pretransplant dialysis vintage. A total of 4117 first-time kidney transplant recipients were enrolled from a prospective nationwide cohort in Korea. DDKT recipients were divided into tertiles according to pretransplant dialysis duration. Graft failure, mortality, and composite were compared between DDKT and living donor kidney transplant (LDKT) recipients. Pretransplant dialysis vintage was longer annually in DDKT recipients. In the subdistribution of the hazard model for the competing risk, the first tertile did not show an increased risk of graft failure compared with LDKT recipients; however, the second and third tertile groups had an increased risk of graft failure compared to LDKT recipients (adjusted hazard ratio [aHR] 3.59; 95% confidence interval [CI] 1.69-7.63; P < 0.001; aHR 2.37; 95% CI 1.06-5.33; P = 0.037). All DDKT groups showed a significantly higher risk of patient death than LDKT, with the highest risk in the third tertile group (aHR 11.12; 95% CI 4.94-25.00; P < 0.001). A longer pretransplant dialysis period was associated with a higher risk of the composite of patient death and graft failure in DDKT recipients. DDKT after a short period of dialysis had non-inferior results on graft survival compared with LDKT.

Transplant outcomes of 100 cases of living-donor ABO-incompatible kidney transplantation

BACKGROUND

Although ABO-incompatible (ABOi) kidney transplantation (KT) has been performed successfully, a standard preconditioning regimen has not been established. Based on the initial antidonor ABO antibody titers, an individualized preconditioning regimen is developed, and this study explored the efficacy and safety of the regimen.

METHODS

From September 1, 2014, to September 1, 2020, we performed 1668 consecutive living-donor KTs, including 100 ABOi and 1568 ABO-compatible (ABOc) KTs. ABOi KT recipients (KTRs) with a lower antibody titer (≤1:8) were administered oral immunosuppressive drugs (OIs) before KT, while patients with a medium titer (1:16) received OIs plus antibody-removal therapy (plasma exchange/double-filtration plasmapheresis), patients with a higher titer (≥1:32) were in addition received rituximab (Rit). Competing risk analyses were conducted to estimate the cumulative incidence of infection, acute rejection (AR), graft loss, and patient death.

RESULTS

After propensity score analyses, 100 ABOi KTRs and 200 matched ABOc KTRs were selected. There were no significant differences in graft and patient survival between the ABOi and ABOc groups (P  = 0.787, P  = 0.386, respectively). After using the individualized preconditioning regimen, ABOi KTRs showed a similar cumulative incidence of AR (10.0% υs . 10.5%, P  = 0.346). Among the ABOi KTRs, the Rit-free group had a similar cumulative incidence of AR ( P  = 0.714) compared to that of the Rit-treated group. Multivariate competing risk analyses revealed that a Rit-free regimen reduced the risk of infection (HR: 0.31; 95% CI: 0.12-0.78, P  = 0.013). Notably, antibody titer rebound was more common in ABOi KTRs receiving a Rit-free preconditioning regimen ( P  = 0.013) than those receiving Rit. ABOi KTRs with antibody titer rebound had a 2.72-fold risk of AR (HR: 2.72, 95% CI: 1.01-7.31, P  = 0.048). ABOi KTRs had similar serum creatinine and estimated glomerular filtration rate compared to those of ABOc KTRs after the first year.

CONCLUSIONS

An individualized preconditioning regimen can achieve comparable graft and patient survival rates in ABOi KT with ABOc KT. Rit-free preconditioning effectively prevented AR without increasing the risk of infectious events in those with lower initial titers; however, antibody titer rebound should be monitored.

Using Information Available at the Time of Donor Offer to Predict Kidney Transplant Survival Outcomes: A Systematic Review of Prediction Models

Statistical models that can predict graft and patient survival outcomes following kidney transplantation could be of great clinical utility. We sought to appraise existing clinical prediction models for kidney transplant survival outcomes that could guide kidney donor acceptance decision-making. We searched for clinical prediction models for survival outcomes in adult recipients with single kidney-only transplants. Models that require information anticipated to become available only after the time of transplantation were excluded as, by that time, the kidney donor acceptance decision would have already been made. The outcomes of interest were all-cause and death-censored graft failure, and death. We summarised the methodological characteristics of the prediction models, predictive performance and risk of bias. We retrieved 4,026 citations from which 23 articles describing 74 models met the inclusion criteria. Discrimination was moderate for all-cause graft failure (C-statistic: 0.570–0.652; Harrell’s C: 0.580–0.660; AUC: 0.530–0.742), death-censored graft failure (C-statistic: 0.540–0.660; Harrell’s C: 0.590–0.700; AUC: 0.450–0.810) and death (C-statistic: 0.637–0.770; Harrell’s C: 0.570–0.735). Calibration was seldom reported. Risk of bias was high in 49 of the 74 models, primarily due to methods for handling missing data. The currently available prediction models using pre-transplantation information show moderate discrimination and varied calibration. Further model development is needed to improve predictions for the purpose of clinical decision-making.

Systematic Review Registration:https://osf.io/c3ehp/l.

Death With Function and Graft Failure After Kidney Transplantation: Risk Factors at Baseline Suggest New Approaches to Management

Background

Improving both patient and graft survival after kidney transplantation are major unmet needs. The goal of this study was to assess risk factors for specific causes of graft loss to determine to what extent patients who develop either death with a functioning graft (DWFG) or graft failure (GF) have similar baseline risk factors for graft loss.

Methods

We retrospectively studied all solitary renal transplants performed between January 1, 2006, and December 31, 2018, at 3 centers and determined the specific causes of DWFG and GF. We examined outcomes in different subgroups using competing risk estimates and cause-specific Cox models.

Results

Of the 5752 kidney transplants, graft loss occurred in 21.6% (1244) patients, including 12.0% (691) DWFG and 9.6% (553) GF. DWFG was most commonly due to malignancy (20.0%), infection (19.7%), cardiac disease (12.6%) with risk factors of older age and pretransplant dialysis, and diabetes as the cause of renal failure. For GF, alloimmunity (38.7%), glomerular diseases (18.6%), and tubular injury (13.9%) were the major causes. Competing risk incidence models identified diabetes and older recipients with higher rates of both DWFG and nonalloimmune GF.

Conclusions

These data suggest that at baseline, 2 distinct populations can be identified who are at high risk for renal allograft loss: a younger, nondiabetic patient group who develops GF due to alloimmunity and an older, more commonly diabetic population who develops DWFG and GF due to a mixture of causes-many nonalloimmune. Individualized management is needed to improve long-term renal allograft survival in the latter group.

Association of balanced abdominal organ transplant center volumes with patient outcomes

BACKGROUND

The volume-outcome relationship for organ-specific transplantation is well-described; it is unknown if the relative balance of kidney compared with liver volumes within an institution relates to organ-specific outcomes. We assessed the association between relative balance within a transplant center and outcomes.

METHODS

National retrospective analysis of isolated kidney and liver transplants in United States 2005-2014 followed through 2019. Latent class analysis defined transplant center phenotypes. Multivariate Cox models estimated death-censored graft loss and mortality.

RESULTS

Latent class analysis identified four phenotypes: kidney only (n = 117), kidney dominant (n = 36), mixed/balanced (n = 90), and liver dominant (n = 13). Compared to mixed centers, the risk of kidney graft loss was higher at kidney-dominant (HR 1.07, p < .001) and liver-dominant (HR 1.10, p < .001) centers, while kidney-only (HR 1.06, p = .01) centers had higher mortality. Liver graft loss was not associated with phenotype, but risk of patient death was lower (HR 0.93, p = .02) at liver dominant and higher (HR 1.06, p = .02) at kidney-dominant centers.

CONCLUSIONS

A mixed phenotype was associated with improved kidney transplant outcomes, whereas liver transplant outcomes were best at liver-dominant centers. While these findings need to be verified with center-level resources, optimization of shared resources could improve patient and organ outcomes.