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López-Abad A, Pecoraro A, Boissier R, Piana A, Prudhomme T, Hevia V, Catucci CL, Dönmez MI, Breda A, Serni S, Territo A, Campi R. Prediction models for postoperative renal function after living donor nephrectomy: a systematic review. Minerva Urol Nephrol 2024; 76:148-156. [PMID: 38742550 DOI: 10.23736/s2724-6051.24.05556-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
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.
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Affiliation(s)
- Alicia López-Abad
- Department of Urology, Virgen de la Arrixaca University Hospital, Murcia, Spain
- Unit of Urological Robotic Surgery and Renal Transplantation, Careggi Hospital, University of Florence, Florence, Italy
| | - Alessio Pecoraro
- Unit of Urological Robotic Surgery and Renal Transplantation, Careggi Hospital, University of Florence, Florence, Italy
| | - Romain Boissier
- Department of Urology and Renal Transplantation, La Conception University Hospital, Marseille, France
| | - Alberto Piana
- Division of Urology, Department of Oncology, University of Turin, Turin, Italy
| | - Thomas Prudhomme
- Department of Urology, Kidney Transplantation and Andrology, Toulouse Rangueil University Hospital, Toulouse, France
| | - Vital Hevia
- Urology Department, Hospital Universitario Ramón y Cajal, Alcalá University, Madrid, Spain
| | - Claudia L Catucci
- Unit of Urological Robotic Surgery and Renal Transplantation, Careggi Hospital, University of Florence, Florence, Italy
| | - Muhammet I Dönmez
- Division of Pediatric Urology, Department of Urology, Istanbul Faculty of Medicine, University of Istanbul, Istanbul, Türkiye
| | - Alberto Breda
- Department of Urology, Puigvert Foundation, Autonomous University of Barcelona, Barcelona, Spain
| | - Sergio Serni
- Unit of Urological Robotic Surgery and Renal Transplantation, Careggi Hospital, University of Florence, Florence, Italy
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
| | - Angelo Territo
- Department of Urology, Puigvert Foundation, Autonomous University of Barcelona, Barcelona, Spain
| | - Riccardo Campi
- Unit of Urological Robotic Surgery and Renal Transplantation, Careggi Hospital, University of Florence, Florence, Italy -
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
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Nishimura N, Hori S, Tomizawa M, Yoneda T, Morizawa Y, Gotoh D, Nakai Y, Miyake M, Torimoto K, Tanaka N, Fujimoto K. Reproducibility of Computed Tomography Volumetry for Predicting Post-Donation Remnant Renal Function: A Retrospective Analysis. Transplant Proc 2023; 55:288-294. [PMID: 36922263 DOI: 10.1016/j.transproceed.2023.02.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 02/03/2023] [Indexed: 03/14/2023]
Abstract
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.
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Affiliation(s)
| | - Shunta Hori
- Department of Urology, Nara Medical University, Kashihara, Nara, Japan
| | - Mitsuru Tomizawa
- Department of Urology, Nara Medical University, Kashihara, Nara, Japan
| | - Tatsuo Yoneda
- Department of Urology, Nara Medical University, Kashihara, Nara, Japan
| | - Yosuke Morizawa
- Department of Urology, Nara Medical University, Kashihara, Nara, Japan
| | - Daisuke Gotoh
- Department of Urology, Nara Medical University, Kashihara, Nara, Japan
| | - Yasushi Nakai
- Department of Urology, Nara Medical University, Kashihara, Nara, Japan
| | - Makito Miyake
- Department of Urology, Nara Medical University, Kashihara, Nara, Japan
| | - Kazumasa Torimoto
- Department of Urology, Nara Medical University, Kashihara, Nara, Japan
| | - Nobumichi Tanaka
- Department of Urology, Nara Medical University, Kashihara, Nara, Japan
| | - Kiyohide Fujimoto
- Department of Urology, Nara Medical University, Kashihara, Nara, Japan.
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Montgomery JR, Brown CS, Zondlak AN, Walsh KW, Kozlowski JE, Pinsky AM, Herriman EA, Sussman J, Lu Y, Stein EB, Shankar PR, Sung RS, Woodside KJ. CT-measured Cortical Volume Ratio Is an Accurate Alternative to Nuclear Medicine Split Scan Ratio Among Living Kidney Donors. Transplantation 2021; 105:2596-2605. [PMID: 33950636 DOI: 10.1097/tp.0000000000003676] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
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.
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Affiliation(s)
- John R Montgomery
- Department of Surgery, Section of Transplant Surgery, University of Michigan, Ann Arbor, MI
- Center for Healthcare Outcomes & Policy, University of Michigan, Ann Arbor, MI
| | - Craig S Brown
- Department of Surgery, Section of Transplant Surgery, University of Michigan, Ann Arbor, MI
- Center for Healthcare Outcomes & Policy, University of Michigan, Ann Arbor, MI
| | | | - Kevin W Walsh
- Medical School, University of Michigan, Ann Arbor, MI
| | | | | | - Emily A Herriman
- Department of Surgery, Section of Transplant Surgery, University of Michigan, Ann Arbor, MI
| | - Jeremy Sussman
- Division of General Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor, MI
| | - Yee Lu
- Division of Nephrology, Department of Medicine, University of Michigan, Ann Arbor, MI
| | - Erica B Stein
- Division of Abdominal Radiology, Department of Radiology, University of Michigan, Ann Arbor, MI
| | - Prasad R Shankar
- Division of Abdominal Radiology, Department of Radiology, University of Michigan, Ann Arbor, MI
- Michigan Radiology Quality Collaborative, University of Michigan, Ann Arbor, MI
| | - Randall S Sung
- Department of Surgery, Section of Transplant Surgery, University of Michigan, Ann Arbor, MI
| | - Kenneth J Woodside
- Department of Surgery, Section of Transplant Surgery, University of Michigan, Ann Arbor, MI
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de Andrade LGM, Tedesco-Silva H. Recycling of predictors used to estimate glomerular filtration rate: Insight into lateral collinearity. PLoS One 2020; 15:e0228842. [PMID: 32045449 PMCID: PMC7012427 DOI: 10.1371/journal.pone.0228842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2019] [Accepted: 01/23/2020] [Indexed: 11/18/2022] Open
Abstract
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.
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