Immunosuppression and cancer risk in kidney transplant recipients: A retrospective cohort study.

We assessed whether contemporary immunosuppression agents were associated with cancer among kidney transplant recipients (KTR), and if this association varied by age and sex. We studied a retrospective province-wide cohort of primary KTR (1997-2016). Employing multivariable Cox models, we estimated associations of cumulative doses of prednisone, mycophenolate and tacrolimus administered over the past 10 years, lagged by 2 years, with the incidence of primary malignant neoplasms (PMN). We assessed interactions with age and sex. To assess the impact of exposure recency, we used weighted cumulative exposure (WCE) modeling. Among 1064 KTR, 108 (10.2%) developed PMN over median follow-up of 73 months (interquartile range: 32-120). Adjusted hazard ratios (aHRs) and 95% confidence intervals (CIs) of 0.96 (0.64-1.43), 1.34 (0.96-1.86), and 1.06 (0.88-1.29) were estimated for cumulative daily doses of prednisone (5 mg), mycophenolate (1000 mg), and tacrolimus (2 mg) administered continuously over the past 10 years, respectively. PMN risk associated with cumulative tacrolimus exposure was modified by age (interaction p = .035) and was more pronounced in 15-year and 30-year-old KTR (aHRs of 1.57 [1.08-2.28] and 1.31 [1.03-1.66], respectively) in comparison to older KTR. PMN risk increase associated with higher cumulative mycophenolate dose was more pronounced in females (aHR = 1.86 [1.15-3.00]) than in males (aHR = 1.16 [0.74-1.81]; interaction p = .131). WCE analyses suggested increased PMN risk the higher the mycophenolate doses taken 5-10 years ago. A trend toward increased PMN risk with long-term mycophenolate exposure, particularly in females, and more pronounced risk with long-term tacrolimus exposure in younger KTR, identify opportunities for tailored immunosuppression to mitigate cancer risk.

Machine Learning Support for Decision-Making in Kidney Transplantation: Step-by-step Development of a Technological Solution.

BACKGROUND: Kidney transplantation is the preferred treatment option for patients with end-stage renal disease. To maximize patient and graft survival, the allocation of donor organs to potential recipients requires careful consideration. OBJECTIVE: This study aimed to develop an innovative technological solution to enable better prediction of kidney transplant survival for each potential donor-recipient pair. METHODS: We used deidentified data on past organ donors, recipients, and transplant outcomes in the United States from the Scientific Registry of Transplant Recipients. To predict transplant outcomes for potential donor-recipient pairs, we used several survival analysis models, including regression analysis (Cox proportional hazards), random survival forests, and several artificial neural networks (DeepSurv, DeepHit, and recurrent neural network [RNN]). We evaluated the performance of each model in terms of its ability to predict the probability of graft survival after kidney transplantation from deceased donors. Three metrics were used: the C-index, integrated Brier score, and integrated calibration index, along with calibration plots. RESULTS: On the basis of the C-index metrics, the neural network-based models (DeepSurv, DeepHit, and RNN) had better discriminative ability than the Cox model and random survival forest model (0.650, 0.661, and 0.659 vs 0.646 and 0.644, respectively). The proposed RNN model offered a compromise between the good discriminative ability and calibration and was implemented in a technological solution of technology readiness level 4. CONCLUSIONS: Our technological solution based on the RNN model can effectively predict kidney transplant survival and provide support for medical professionals and candidate recipients in determining the most optimal donor-recipient pair.