Introduction: Living-donor nephrectomy (LDN) is the most valuable source of organs for kidney transplantation worldwide. The current preoperative evaluation of a potential living donor candidate does not take into account formal estimation of postoperative renal function decline after surgery using validated prediction models. The aim of this study was to summarize the available models to predict the mid- to long-term renal function following LDN, aiming to support both clinicians and patients during the decision-making process.
Evidence acquisition: A systematic review of the English-language literature was conducted following the principles highlighted by the European Association of Urology (EAU) guidelines and following the PRISMA 2020 recommendations. The protocol was registered in PROSPERO on December 10, 2022 (registration ID: CRD42022380198). In the qualitative analysis we selected the models including only preoperative variables.
Evidence synthesis: After screening and eligibility assessment, six models from six studies met the inclusion criteria. All of them relied on retrospective patient cohorts. According to PROBAST, all studies were evaluated as high risk of bias. The models included different combinations of variables (ranging between two to four), including donor-/kidney-related factors, and preoperative laboratory tests. Donor age was the variable more often included in the models (83%), followed by history of hypertension (17%), Body Mass Index (33%), renal volume adjusted by body weight (33%) and body surface area (33%). There was significant heterogeneity in the model building strategy, the main outcome measures and the model's performance metrics. Three models were externally validated.
Conclusions: Few models using preoperative variables have been developed and externally validated to predict renal function after LDN. As such, the evidence is premature to recommend their use in routine clinical practice. Future research should be focused on the development and validation of user-friendly, robust prediction models, relying on granular large multicenter datasets, to support clinicians and patients during the decision-making process.