Prognostic factors for renal transplant graft survival in a retrospective cohort of 1000 cases: The role of desensitization therapy

Advancements in Artificial Intelligence for Kidney Transplantology: A Comprehensive Review of Current Applications and Predictive Models

Background: Artificial intelligence is rapidly advancing within the domains of medicine and transplantology. In this comprehensive review, we provide an in-depth exploration of current AI methodologies, with a particular emphasis on machine learning and deep learning techniques, and their diverse subtypes. These technologies are revolutionizing how data are processed, analyzed, and applied in clinical decision making. Methods: A meticulous literature review was conducted with a focus on the application of artificial intelligence in kidney transplantation. Four research questions were formulated to establish the aim of the review. Results: We thoroughly examined the general applications of AI in the medical field, such as feature selection, dimensionality reduction, and clustering, which serve as foundational tools for complex data analysis. This includes the development of predictive models for transplant rejection, the optimization of personalized immunosuppressive therapies, the algorithmic matching of donors and recipients based on multidimensional criteria, and the sophisticated analysis of histopathological images to improve the diagnostic accuracy. Moreover, we present a detailed comparison of existing AI-based algorithms designed to predict kidney graft survival in transplant recipients. In this context, we focus on the variables incorporated into these predictive models, providing a critical analysis of their relative importance and contribution to model performance. Conclusions: This review highlights the significant advancements made possible through AI and underscores its potential to enhance both clinical outcomes and the precision of medical interventions in the field of transplantology.

Prediction of early recovery of graft function after living donor liver transplantation in children

For end-stage liver disease in children, living donor liver transplantation (LDLT) is often the important standard curative treatment. However, there is a lack of research on early recovery of graft function after pediatric LDLT. This is a single-center, ambispective cohort study. We collected the demographic and clinicopathological data of donors and recipients, and determined the risk factors of postoperative delayed recovery of hepatic function (DRHF) by univariate and multivariate Logistic analyses. 181 cases were included in the retrospective cohort and 50 cases in the prospective cohort. The incidence of DRHF after LDLT in children was 29.4%, and DRHF could well evaluate the early recovery of graft function after LDLT. Through Logistic analyses and AIC score, preoperative liver function of donors, ischemia duration level of the liver graft, Ln (Cr of recipients before operation) and Ln (TB of recipients on the 3rd day after operation) were predictive indicators for DRHF after LDLT in children. Using the above factors, we constructed a predictive model to evaluate the incidence of postoperative DRHF. Self-verification and prospective internal verification showed that this prediction model had good accuracy and clinical applicability. In conclusion, we pointed many risk factors for early delayed recovery of graft function after LDLT in children, and developed a visual and personalized predictive model for them, offering valuable insights for clinical management.

Differences between Very Highly Sensitized Kidney Transplant Recipients as Identified by Machine Learning Consensus Clustering

Background and Objectives: The aim of our study was to categorize very highly sensitized kidney transplant recipients with pre-transplant panel reactive antibody (PRA) ≥ 98% using an unsupervised machine learning approach as clinical outcomes for this population are inferior, despite receiving increased allocation priority. Identifying subgroups with higher risks for inferior outcomes is essential to guide individualized management strategies for these vulnerable recipients. Materials and Methods: To achieve this, we analyzed the Organ Procurement and Transplantation Network (OPTN)/United Network for Organ Sharing (UNOS) database from 2010 to 2019 and performed consensus cluster analysis based on the recipient-, donor-, and transplant-related characteristics in 7458 kidney transplant patients with pre-transplant PRA ≥ 98%. The key characteristics of each cluster were identified by calculating the standardized mean difference. The post-transplant outcomes were compared between the assigned clusters. Results: We identified two distinct clusters and compared the post-transplant outcomes among the assigned clusters of very highly sensitized kidney transplant patients. Cluster 1 patients were younger (median age 45 years), male predominant, and more likely to have previously undergone a kidney transplant, but had less diabetic kidney disease. Cluster 2 recipients were older (median 54 years), female predominant, and more likely to be undergoing a first-time transplant. While patient survival was comparable between the two clusters, cluster 1 had lower death-censored graft survival and higher acute rejection compared to cluster 2. Conclusions: The unsupervised machine learning approach categorized very highly sensitized kidney transplant patients into two clinically distinct clusters with differing post-transplant outcomes. A better understanding of these clinically distinct subgroups may assist the transplant community in developing individualized care strategies and improving the outcomes for very highly sensitized kidney transplant patients.

Graft and Patient Survival Rates in Kidney Transplantation, and Their Associated Factors: A Systematic Review and Meta-Analysis

BACKGROUND

End-Stage Renal Disease (ESRD) is an irreversible impairment of kidney function that leads to permanently dependent on alternative therapies such as hemodialysis, peritoneal dialysis, and renal transplantation. This study aimed to systematically investigate the survival rate of patients with renal transplantation, graft, and its related factors in Iran.

METHODS

This systematic review and meta-analysis drew on articles indexed in six international and one internal databases (Medline/PubMed, ProQuest, Scopus, Embase, SID, and Web of knowledge) until Nov 2020. The reporting of the present study was performed in terms of PRISMA statement. All analyzes were performed using the STATA software.

RESULTS

Overall, 367 titles from 6 databases were evaluated of which 86 articles met the inclusion criteria. According to the random model, the graft survival rate at one, three, five, and 10 years were 92.48%, 85.08%, 79.96% and 68.15% respectively. Additionally, the patient survival rates at one, three, five, and 10 years were 91.27%, 86.46%, 81.17% and 78.15% respectively. There was a significant relationship between the age recipient and three-year graft survival rate (P=0.021). Additionally, there was an inverse and significant relationship between the donor age and 10-year patient survival rate (P=0.011).

CONCLUSION

The patient and graft survival in transplanted kidney patients is comparable with most developed countries.