Prediction of Three-Year Mortality After Deceased Donor Kidney Transplantation in Adults with Pre-Transplant Donor and Recipient Variables

Prediction of Renal Graft Function 1 Year After Adult Deceased-Donor Kidney Transplantation Using Variables Available Prior to Transplantation

BACKGROUND Kidney transplantation is still the best therapy for patients with end-stage renal disease, but the demand for donor organs persistently surpasses the supply. A prognostic model using pre-transplant data for the prediction of renal graft function would be helpful to optimize organ allocation and avoid futile transplantations. MATERIAL AND METHODS Retrospective data of 2431 patients who underwent kidney transplantation between January 01, 2000, and December 31, 2012 with subsequent ten-year clinical follow-up in our transplant center were analyzed. Of these, 1172 patients met the inclusion criteria. Multivariable regression modelling was used to develop a prognostic model for the prediction of graft function after 1 year utilizing only pre-transplant data. The final model was assessed with the area under the receiver operating characteristic (AUROC) curve. RESULTS Donor age, donor serum creatinine, recipient body mass index, re-transplantations beyond the second kidney transplantation, and cold ischemia time had an independent, significant influence on graded renal graft function 1 year after kidney transplantation. AUROC analysis of the prognostic model was >0.700 for all GFR categories except KDIGO G5, indicating high sensitivity and specificity of prediction. CONCLUSIONS For improvement of renal graft function, organs from older donors or donors with high serum creatinine should not be used in obese recipients and for re-transplantations beyond the second one. Cold ischemia time should be as short as possible.

Seeking Standardized Definitions for HLA-incompatible Kidney Transplants: A Systematic Review

BACKGROUND

There is no standard definition for "HLA incompatible" transplants. For the first time, we systematically assessed how HLA incompatibility was defined in contemporary peer-reviewed publications and its prognostic implication to transplant outcomes.

METHODS

We combined 2 independent searches of MEDLINE, EMBASE, and the Cochrane Library from 2015 to 2019. Content-expert reviewers screened for original research on outcomes of HLA-incompatible transplants (defined as allele or molecular mismatch and solid-phase or cell-based assays). We ascertained the completeness of reporting on a predefined set of variables assessing HLA incompatibility, therapies, and outcomes. Given significant heterogeneity, we conducted narrative synthesis and assessed risk of bias in studies examining the association between death-censored graft failure and HLA incompatibility.

RESULTS

Of 6656 screened articles, 163 evaluated transplant outcomes by HLA incompatibility. Most articles reported on cytotoxic/flow T-cell crossmatches (n = 98). Molecular genotypes were reported for selected loci at the allele-group level. Sixteen articles reported on epitope compatibility. Pretransplant donor-specific HLA antibodies were often considered (n = 143); yet there was heterogeneity in sample handling, assay procedure, and incomplete reporting on donor-specific HLA antibodies assignment. Induction (n = 129) and maintenance immunosuppression (n = 140) were frequently mentioned but less so rejection treatment (n = 72) and desensitization (n = 70). Studies assessing death-censored graft failure risk by HLA incompatibility were vulnerable to bias in the participant, predictor, and analysis domains.

CONCLUSIONS

Optimization of transplant outcomes and personalized care depends on accurate HLA compatibility assessment. Reporting on a standard set of variables will help assess generalizability of research, allow knowledge synthesis, and facilitate international collaboration in clinical trials.

The promise of machine learning applications in solid organ transplantation

Solid-organ transplantation is a life-saving treatment for end-stage organ disease in highly selected patients. Alongside the tremendous progress in the last several decades, new challenges have emerged. The growing disparity between organ demand and supply requires optimal patient/donor selection and matching. Improvements in long-term graft and patient survival require data-driven diagnosis and management of post-transplant complications. The growing abundance of clinical, genetic, radiologic, and metabolic data in transplantation has led to increasing interest in applying machine-learning (ML) tools that can uncover hidden patterns in large datasets. ML algorithms have been applied in predictive modeling of waitlist mortality, donor–recipient matching, survival prediction, post-transplant complications diagnosis, and prediction, aiming to optimize immunosuppression and management. In this review, we provide insight into the various applications of ML in transplant medicine, why these were used to evaluate a specific clinical question, and the potential of ML to transform the care of transplant recipients. 36 articles were selected after a comprehensive search of the following databases: Ovid MEDLINE; Ovid MEDLINE Epub Ahead of Print and In-Process & Other Non-Indexed Citations; Ovid Embase; Cochrane Database of Systematic Reviews (Ovid); and Cochrane Central Register of Controlled Trials (Ovid). In summary, these studies showed that ML techniques hold great potential to improve the outcome of transplant recipients. Future work is required to improve the interpretability of these algorithms, ensure generalizability through larger-scale external validation, and establishment of infrastructure to permit clinical integration.

A simple score to predict early death after kidney transplantation

BACKGROUND

Few studies have focused on risk stratification for premature death after transplantation. However, stratification of individual risk is an essential step in personalized care.

MATERIAL AND METHODS

We have developed a risk score of early post-transplant death (ORLY score) in a prospective multicentre cohort including 942 patients and validated our model in a retrospective independent replication cohort including 874 patients.

RESULTS

60 patients (6.4%) from the prospective cohort died during the first three-year post-transplant. Age, male gender, diabetes, dialysis duration and chronic respiratory failure were associated with early post-transplant death. The multivariable model exhibited good discrimination ability (C-index = 0.78, 95%CI [0.75-0.81]). ORLY score highly predicted early death after transplantation (1.34; 95%CI, 1.22 to 1.48 for each increase of 1 point in score; P < .001). The predictive value of the score in the validation cohort was close to that observed in the experimental cohort (1.41; 95%CI, 1.27 to 1.56 for each increase of 1 point in score; P < .001). Merging the two cohorts, four categories of risk could be individualized: low, 0-5 (n = 522, mean risk, 1%); intermediate, 6-7 (n = 739, mean risk 4.7%); moderate, 8-10 (n = 429, mean risk 10%); and high risk 11-15 (n = 132, mean risk 19%).

CONCLUSIONS

The ORLY score discriminates patients with high risk of early death.