Preserved Kidney Volume, Body Mass Index, and Age Are Significant Preoperative Factors for Predicting Estimated Glomerular Filtration Rate in Living Kidney Donors at 1 Year After Donation

Prediction models for postoperative renal function after living donor nephrectomy: a systematic review

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.

Reproducibility of Computed Tomography Volumetry for Predicting Post-Donation Remnant Renal Function: A Retrospective Analysis

BACKGROUND

Recent studies indicate that split renal function calculated by computed tomography (CT) volumetry is equally or more useful than that calculated by nuclear renography for donated kidney side selection. However, it remains unclear if CT volumetry accurately reflects split renal function as measured by nuclear renography. Therefore, this study aimed to evaluate the reproducibility of CT volumetry.

METHODS

Data from 141 donors who underwent living donor nephrectomy at Nara Medical University from March 2007 to June 2021 were reviewed. The correlation and agreement between the predicted postdonation estimated glomerular filtration rate (eGFR) by ^99mTc-diethylenetriamine penta-acetic acid (DTPA) scintigraphy and by CT volumetry were evaluated by the Pearson's correlation coefficient and Bland-Altman analysis, respectively. Moreover, a comparison in split renal function categorization between ^99mTc-DTPA scan and CT volumetry was performed.

RESULTS

A total of 133 donors were included in the analysis. There was high correlation between the predicted postdonation eGFR by ^99mTc-DTPA scintigraphy and by CT. Moreover, there was agreement in the predicted postdonation eGFR between ^99mTc-DTPA scintigraphy and CT volumetry (Bland-Altman analysis [bias, 95% limits of agreement]; 0.83%, -5.6% to 7.3%). However, in one of 17 donors with absolute split renal function greater than 10% by ^99mTc-DTPA scintigraphy, this clinically significant difference was missed by CT volumetry.

CONCLUSION

There are donors for whom a clinically significant split renal function is not accurately reflected in CT volumetry. Future studies need to amend this discrepancy.

CT-measured Cortical Volume Ratio Is an Accurate Alternative to Nuclear Medicine Split Scan Ratio Among Living Kidney Donors

BACKGROUND

The 125I-iothalamate clearance and 99mTc diethylenetriamine-pentaacetic acid (99mTc-DTPA) split scan nuclear medicine studies are used among living kidney donor candidates to determine measured glomerular filtration rate (mGFR) and split scan ratio (SSR). The computerized tomography-derived cortical volume ratio (CVR) is a novel measurement of split kidney function and can be combined with predonation estimated GFR (eGFR) or mGFR to predict postdonation kidney function. Whether predonation SSR predicts postdonation kidney function better than predonation CVR and whether predonation mGFR provides additional information beyond predonation eGFR are unknown.

METHODS

We performed a single-center retrospective analysis of 204 patients who underwent kidney donation between June 2015 and March 2019. The primary outcome was 1-y postdonation eGFR. Model bases were created from a measure of predonation kidney function (mGFR or eGFR) multiplied by the proportion that each nondonated kidney contributed to predonation kidney function (SSR or CVR). Multivariable elastic net regression with 1000 repetitions was used to determine the mean and 95% confidence interval of R2, root mean square error (RMSE), and proportion overprediction ≥15 mL/min/1.73 m2 between models.

RESULTS

In validation cohorts, eGFR-CVR models performed best (R2, 0.547; RMSE, 9.2 mL/min/1.73 m2, proportion overprediction 3.1%), whereas mGFR-SSR models performed worst (R2, 0.360; RMSE, 10.9 mL/min/1.73 m2, proportion overprediction 7.2%) (P < 0.001 for all comparisons).

CONCLUSIONS

These findings suggest that predonation CVR may serve as an acceptable alternative to SSR during donor evaluation and furthermore, that a model based on CVR and predonation eGFR may be superior to other methods.

Recycling of predictors used to estimate glomerular filtration rate: Insight into lateral collinearity

Background

One overlooked problem in statistical analysis is lateral collinearity, a phenomenon that may occur when the outcome variable derives from the predictors. In nephrology this issue is seen with the use of estimated glomerular filtration rate (eGFR) as an outcome and age, sex, and ethnicity as predictors. In this study with simulated data, we aim to illustrate this problem.

Methods

We randomly generated unrelated data to estimate eGFR by common equations.

Results

Using simulated data, we show that age, gender, and ethnicity (recycled predictors variables) are statistically significantly correlated with eGFR in linear regression analysis. Whereas the initial obvious conclusion is that age, sex, and ethnicity are strong predictors of eGFR, more rigorous interpretation suggests that this is a byproduct of the mathematical model produced when deriving new predictors from another.

Conclusion

While statistical models have the ability to identify vertical collinearity (predictor-predictor), lateral collinearity (predictor-outcome) is seldom identified and discussed in statistical analysis. Therefore, caution is needed when interpreting the correlation between age, gender, and ethnicity with eGFR derived from regression analyses.