Horizontal mixture model for competing risks: a method used in waitlisted renal transplant candidates

Regional Disparities in Kidney Transplant Allocation in Brazil: A Retrospective Cohort Study

BACKGROUND

Brazil has a large public transplant program, but it remains unclear if the kidney waitlist criteria effectively allocate organs. This study aimed to investigate whether gender, ethnicity, clinical characteristics, and Brazilian regions affect the chance of deceased donor kidney transplant (DDKT).

METHODS

We conducted a retrospective cohort study using the National Transplant System/Brazil database, which included all patients on the kidney transplant waitlist from January 2012 to December 2022, followed until May 2023. The primary outcome assessed was the chance of DDKT, measured using subdistribution hazard and cause-specific hazard models (subdistribution hazard ratio [sHR]).

RESULTS

We analyzed 118 617 waitlisted patients over a 10-year study period. Male patients had an sHR of 1.07 ([95% CI: 1.05-1.10], p < 0.001), indicating a higher chance of DDTK. Patients of mixed race and Yellow/Indigenous ethnicity had lower rates of receiving a transplant compared to Caucasian patients, with sHR of 0.97 (95% CI: 0.95-1) and 0.89 (95% CI: 0.95-1), respectively. Patients from the South region had the highest chance of DDKT, followed by those from the Midwest and Northeast, compared to patients from the Southeast, with sHR of 2.53 (95% CI: 2.47-2.61), 1.21 (95% CI: 1.16-1.27), and 1.10 (95% CI: 1.07-1.13), respectively. The North region had the lowest chance of DDTK, sHR of 0.29 (95% CI: 0.27-0.31).

CONCLUSION

We found that women and racial minorities faced disadvantages in kidney transplantation. Additionally, we observed regional disparities, with the North region having the lowest chance of DDKT and longer times on dialysis before being waitlisted. In contrast, patients in the South regions had a chance of DDKT and shorter times on dialysis before being waitlisted. It is urgent to implement approaches to enhance transplant capacity in the North region and address race and gender disparities in transplantation.

Outcomes of Declined Deceased Donor Kidney Offers That Are Subsequently Implanted: A UK Registry Study

BACKGROUND

Deceased donor kidneys are often declined for ≥1 patients but then implanted into another. Studies are needed to guide transplant clinicians and patients, especially given the increasing age and comorbidity of donors. This study compares outcomes of recipients of transplanted kidneys that were initially declined with outcomes of patients who remained on the waiting list.

METHODS

This UK Transplant Registry study examined named-patient, adult donation after brain death donor single kidney-only offers that were declined for donor- or organ-related reasons (DORRs), in which the kidney was subsequently transplanted from January 1, 2010, to December 31, 2018. Outcomes included graft function and survival of kidneys transplanted following DORR decline, survival and transplant status of patients who had a kidney declined, and intercenter decline rates.

RESULTS

A total of 4722 kidneys declined for DORRs, which eventually resulted in single kidney-only transplants, were examined. One year after the offer decline, 35% of patients for whom the organ was declined remained on the list, 55% received a deceased donor transplant at a median of 174 d after the initial offer decline, and 4% had been removed or died. For patients transplanted following offer decline, there was no significant difference in 5-y graft survival when comparing the outcomes to those recipients who received the declined kidney. There was significant variation in DORR decline rates between UK transplant units (17%-54%).

CONCLUSIONS

This study shows reasonable outcomes of kidneys previously declined for DORRs and supports the utilization of those considered to be of higher risk for carefully selected recipients.

A machine learning prediction model for waiting time to kidney transplant

Background

Predicting waiting time for a deceased donor kidney transplant can help patients and clinicians to discuss management and contribute to a more efficient use of resources. This study aimed at developing a predictor model to estimate time on a kidney transplant waiting list using a machine learning approach.

Methods

A retrospective cohort study including data of patients registered, between January 1, 2000 and December 31, 2017, in the waiting list of São Paulo State Organ Allocation System (SP-OAS) /Brazil. Data were randomly divided into two groups: 75% for training and 25% for testing. A Cox regression model was fitted with deceased donor transplant as the outcome. Sensitivity analyses were performed using different Cox models. Cox hazard ratios were used to develop the risk-prediction equations.

Results

Of 54,055 records retrieved, 48,153 registries were included in the final analysis. During the study period, approximately 1/3 of the patients were transplanted with a deceased donor. The major characteristics associated with changes in the likelihood of transplantation were age, subregion, cPRA, and frequency of HLA-DR, -B and -A. The model developed was able to predict waiting time with good agreement in internal validation (c-index = 0.70).

Conclusion

The kidney transplant waiting time calculator developed shows good predictive performance and provides information that may be valuable in assisting candidates and their providers. Moreover, it can significantly improve the use of economic resources and the management of patient care before transplant.

Renal transplantation outcomes in obese patients: a French cohort-based study

Background

Whilst there are a number of publications comparing the relationship between body mass index (BMI) of kidney transplant recipients and graft/patient survival, no study has assessed this for a French patient cohort.

Methods

In this study, cause-specific Cox models were used to study patient and graft survival and several other time-to-event measures. Logistic regressions were performed to study surgical complications at 30 days post-transplantation as well as delayed graft function.

Results

Among the 4691 included patients, 747 patients were considered obese with a BMI level greater than 30 kg/m². We observed a higher mortality for obese recipients (HR = 1.37, p = 0.0086) and higher risks of serious bacterial infections (HR = 1.24, p = 0.0006) and cardiac complications (HR = 1.45, p < 0.0001). We observed a trend towards death censored graft survival (HR = 1.22, p = 0.0666) and no significant increased risk of early surgical complications.

Conclusions

We showed that obesity increased the risk of death and serious bacterial infections and cardiac complications in obese French kidney transplant recipients. Further epidemiologic studies aiming to compare obese recipients versus obese candidates remaining on dialysis are needed to improve the guidelines for obese patient transplant allocation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12882-021-02278-1.