Kidney transplant recipients (KTRs) are at increased risk of developing de novo post-transplant malignancies (PTMs), with regional differences in types with excess risk compared to the general population. A single-center, population-controlled, retrospective cohort study was conducted at a tertiary care center in Thailand among all adults who underwent their first kidney transplant from 1986 to 2018. Standardized incidence ratios (SIRs) of malignancy by age, sex, and place of residence were obtained using data from the National Cancer Registry of Thailand as population control. There were 2,024 KTRs [mean age, 42.4 years (SD 11.4); female patients, 38.6%] during 16,495 person-years at risk. Of these, 125 patients (6.2%) developed 133 de novo PTMs. The SIR for all PTMs was 3.85 (95% CI 3.22, 4.56), and for pooled solid and hematologic PTMs, it was 3.32 (95% CI 2.73, 3.99). Urothelial malignancies had the largest excess risk, especially in women [female SIR 114.7 (95% CI 66.8, 183.6); male SIR 17.5 (95% CI 8.72, 31.2)]. The next two most common cancers were non-Hodgkin’s lymphoma and skin cancer [SIR 20.3 (95% CI 13.6, 29.1) and 24.7 (95% CI 15.3-37.8), respectively]. Future studies are needed to identify the risk factors and assess the need for systematic screening among PTMs with excess risk in KTRs.

The mortality rate after novel coronavirus infection, which causes severe acute respiratory distress syndrome (SARS-CoV-2), is much higher in kidney transplant recipients (KTRs) compared to the general population. Seroconversion after vaccination is also lower, and breakthrough infection is much higher. Many studies reported seroconversion rate after a booster (third) dose of vaccine but clinical outcomes received less attention. Here, we reported the impact of an mRNA vaccine booster dose on clinical outcomes of KTRs with SARS-CoV-2 infection. A total of 183 KTRs with SARS-CoV-2 infection were identified. Of 183 KTRs, 146 KTRs had sufficient data for analysis and were included in this study. Forty-eight patients (32.9%) received zero to 1 doses of vaccine (Group 1), thirty-one (21.2%) received two doses (Group 2), and sixty-seven (45.9%) received a booster dose (Group 3). Pneumonia developed in 50%, 23%, and 10% in Group 1, 2, and 3 (p < 0.001). Hospital admission requirement was 81%, 48%, and 12% (p < 0.001). Mortality rate was 26%, 3%, and 3% (p = 0.001). A multivariate analysis showed that only diabetes adversely affects mortality while the booster dose of the vaccine significantly reduced mortality. The booster dose of the vaccine is strongly recommended in all KTRs especially those with diabetes. Our study also suggested the timing of the booster dose vaccine to be administered within 4 months after the second dose.

The coronavirus virus disease 2019 (COVID-19) pandemic has impacted the global healthcare system. In Thailand, the first and most available vaccines were inactivated and viral vector vaccines. We reported the impact of those vaccines in preventing severe disease and death in kidney transplant recipients. This retrospective study comprised 45 kidney transplant recipients with COVID-19 infection, classified by vaccination status. Outcomes of interest were death, pneumonia, and allograft dysfunction. There were 23 patients in vaccinated group and 22 patients in unvaccinated group. All baseline characteristics were similar except mean age was older in vaccinated group, 55 vs. 48 years. Total 11 patients (24%) died (13% vaccinated vs. 36% unvaccinated RR, 0.56; 95% CI, 0.29–0.83; p = 0.03). Multivariate analysis showed that vaccination significantly decrease mortality (odds ratio, 0.54; 95% CI, 0.10–0.94; p = 0.03). Pneumonia developed equally in both groups (70%). There was a trend toward less oxygen requirement as well as ventilator requirement in vaccinated group. The rate of allograft dysfunction was similar (47%). Inactivated and viral vector COVID-19 vaccines have beneficial effect on mortality reduction in kidney transplant recipients. Even partial vaccination can exert some protection against death. However, full vaccination should be encouraged to achieve better prevention.

Background: Given the significant impact of delayed graft function (DGF) on transplant outcomes, the aim of this study was to develop and validate machine learning (ML) models capable of predicting the risk of DGF in deceased-donor kidney transplantation (DDKT). Methods: This retrospective cohort study was conducted using clinical and histopathological data collected between 2018 and 2022 at Ramathibodi Hospital from DDKT donors, recipients, and post-implantation time-zero kidney biopsy samples to develop predictive models. The performance of three ML models (neural network, random forest, and extreme gradient boosting [XGBoost]) and traditional logistic regression on an independent test data set was evaluated using the area under the receiver operating characteristic curve (AUROC) and Brier score calibration. Results: Among 354 DDKT recipients, 64 (18.1%) experienced DGF. The key contributing factors included a donor body mass index > 23 kg/m², donor diabetes mellitus, a prolonged cold ischemia time, a male recipient, and an interstitial fibrosis/tubular atrophy score of 2–3 in the time-zero kidney biopsy sample. The random forest model had a specificity of 99.96% and an AUROC of 0.9323, the neural network model had a specificity of 97.43% and an AUROC of 0.844, and the XGBoost model had a specificity of 99.81% and an AUROC of 0.989. A traditional statistical model had a specificity of 84.4% and an AUROC of 0.769. Conclusions: Predictive models, especially XGBoost models, have potential as tools for assessing DGF risk post-DDKT, guiding acceptance decisions, and avoiding risky biopsy, and they may be crucial in resource-limited settings.

The rapid increase in end-stage kidney disease (ESKD) patients compared to available organ donors remains a significant challenge. This study aimed to externally validate and update the Scientific Registry of Transplant Recipients (SRTR) prediction model for receiving deceased donor kidney transplantation (KT) among Thai patients on the waiting list. This retrospective study included 3,923 patients registered for deceased donor KT. The original SRTR score was calculated. The primary outcome was the time to deceased donor KT. Cox regression analysis was used to evaluate associations between the SRTR score, additional predictors, and time to KT. The SRTR model was revised by re-estimating individual coefficients and updated by incorporating significant predictors. Discriminative performance was assessed using Harrell’s C-index. The original SRTR model performed poorly in our cohort, with a C-index of 0.521. After revision, the C-index slightly improved to 0.662. The updated model, incorporating nine predictors (sex, blood group, diabetes, peripheral vascular disease, ESKD etiology, dialysis vintage, history of prior kidney transplantation, panel reactive antibody, and anti-HBs antibody status), achieved a C-index of 0.744, indicating good discriminative performance. The updated prediction model demonstrated good predictive performance and may be applied to optimize the management of transplant candidates on the waiting list in the Thai population. Not applicable.