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Delanaye P, Pottel H, Cavalier E, Flamant M, Stehlé T, Mariat C. Diagnostic standard: assessing glomerular filtration rate. Nephrol Dial Transplant 2024; 39:1088-1096. [PMID: 37950562 DOI: 10.1093/ndt/gfad241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Indexed: 11/12/2023] Open
Abstract
Creatinine-based estimated glomerular filtration rate (eGFR) is imprecise at individual level, due to non-GFR-related serum creatinine determinants, including atypical muscle mass. Cystatin C has the advantage of being independent of muscle mass, a feature that led to the development of race- and sex-free equations. Yet, cystatin C-based equations do not perform better than creatinine-based equations for estimating GFR unless both variables are included together. The new race-free Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation had slight opposite biases between Black and non-Black subjects in the USA, but has poorer performance than that the previous version in European populations. The European Kidney Function Consortium (EKFC) equation developed in 2021 can be used in both children and adults, is more accurate in young and old adults, and is applicable to non-white European populations, by rescaling the Q factor, i.e. population median creatinine, in a potentially universal way. A sex- and race-free cystatin C-based EKFC, with the same mathematical design, has also be defined. New developments in the field of GFR estimation would be standardization of cystatin C assays, development of creatinine-based eGFR equations that incorporate muscle mass data, implementation of new endogenous biomarkers and the use of artificial intelligence. Standardization of different GFR measurement methods would also be a future challenge, as well as new technologies for measuring GFR. Future research is also needed into discrepancies between cystatin C and creatinine, which is associated with high risk of adverse events: we need to standardize the definition of discrepancy and understand its determinants.
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Affiliation(s)
- Pierre Delanaye
- Department of Nephrology-Dialysis-Transplantation, University of Liège (ULiege), CHU Sart Tilman, Liège, Belgium
- Department of Nephrology-Dialysis-Apheresis, Hôpital Universitaire Carémeau, Nîmes, France
| | - Hans Pottel
- Department of Public Health and Primary Care, KU Leuven Campus Kulak Kortrijk, Kortrijk, Belgium
| | - Etienne Cavalier
- Department of Clinical Chemistry, University of Liège (ULiege), CHU Sart Tilman, Liège, Belgium
| | - Martin Flamant
- Assistance Publique-Hôpitaux de Paris, Bichat Hospital, and Université Paris Cité, UMR 1149, Paris, France
| | - Thomas Stehlé
- Assistance Publique-Hôpitaux de Paris, Hôpitaux Universitaires Henri Mondor, Service de Néphrologie et Transplantation, Fédération Hospitalo-Universitaire « Innovative therapy for immune disorders », Créteil, France
| | - Christophe Mariat
- Service de Néphrologie, Dialyse et Transplantation Rénale, Hôpital Nord, CHU de Saint-Etienne, France
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Zheng Q, Ni X, Yang R, Jiao P, Wu J, Yang S, Chen Z, Liu X, Wang L. UroAngel: a single-kidney function prediction system based on computed tomography urography using deep learning. World J Urol 2024; 42:238. [PMID: 38627315 DOI: 10.1007/s00345-024-04921-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Accepted: 01/16/2024] [Indexed: 04/19/2024] Open
Abstract
BACKGROUND Accurate estimation of the glomerular filtration rate (GFR) is clinically crucial for determining the status of obstruction, developing treatment strategies, and predicting prognosis in obstructive nephropathy (ON). We aimed to develop a deep learning-based system, named UroAngel, for non-invasive and convenient prediction of single-kidney function level. METHODS We retrospectively collected computed tomography urography (CTU) images and emission computed tomography diagnostic reports of 520 ON patients. A 3D U-Net model was used to segment the renal parenchyma, and a logistic regression multi-classification model was used to predict renal function level. We compared the predictive performance of UroAngel with the Modification of Diet in Renal Disease (MDRD), Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equations, and two expert radiologists in an additional 40 ON patients to validate clinical effectiveness. RESULTS UroAngel based on 3D U-Net convolutional neural network could segment the renal cortex accurately, with a Dice similarity coefficient of 0.861. Using the segmented renal cortex to predict renal function stage had high performance with an accuracy of 0.918, outperforming MDRD and CKD-EPI and two radiologists. CONCLUSIONS We proposed an automated 3D U-Net-based analysis system for direct prediction of single-kidney function stage from CTU images. UroAngel could accurately predict single-kidney function in ON patients, providing a novel, reliable, convenient, and non-invasive method.
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Affiliation(s)
- Qingyuan Zheng
- Department of Urology, Renmin Hospital of Wuhan University, 99 Zhang Zhi-Dong Road, Wuhan, 430060, Hubei, People's Republic of China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan, 430060, Hubei, People's Republic of China
| | - Xinmiao Ni
- Department of Urology, Renmin Hospital of Wuhan University, 99 Zhang Zhi-Dong Road, Wuhan, 430060, Hubei, People's Republic of China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan, 430060, Hubei, People's Republic of China
| | - Rui Yang
- Department of Urology, Renmin Hospital of Wuhan University, 99 Zhang Zhi-Dong Road, Wuhan, 430060, Hubei, People's Republic of China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan, 430060, Hubei, People's Republic of China
| | - Panpan Jiao
- Department of Urology, Renmin Hospital of Wuhan University, 99 Zhang Zhi-Dong Road, Wuhan, 430060, Hubei, People's Republic of China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan, 430060, Hubei, People's Republic of China
| | - Jiejun Wu
- Department of Urology, Renmin Hospital of Wuhan University, 99 Zhang Zhi-Dong Road, Wuhan, 430060, Hubei, People's Republic of China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan, 430060, Hubei, People's Republic of China
| | - Song Yang
- Department of Urology, Renmin Hospital of Wuhan University, 99 Zhang Zhi-Dong Road, Wuhan, 430060, Hubei, People's Republic of China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan, 430060, Hubei, People's Republic of China
| | - Zhiyuan Chen
- Department of Urology, Renmin Hospital of Wuhan University, 99 Zhang Zhi-Dong Road, Wuhan, 430060, Hubei, People's Republic of China
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan, 430060, Hubei, People's Republic of China
| | - Xiuheng Liu
- Department of Urology, Renmin Hospital of Wuhan University, 99 Zhang Zhi-Dong Road, Wuhan, 430060, Hubei, People's Republic of China.
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan, 430060, Hubei, People's Republic of China.
| | - Lei Wang
- Department of Urology, Renmin Hospital of Wuhan University, 99 Zhang Zhi-Dong Road, Wuhan, 430060, Hubei, People's Republic of China.
- Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan, 430060, Hubei, People's Republic of China.
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Zhang L, Richter LR, Kim T, Hripcsak G. Evaluating and Improving the Performance and Racial Fairness of Algorithms for GFR Estimation. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.07.24300943. [PMID: 38260285 PMCID: PMC10802656 DOI: 10.1101/2024.01.07.24300943] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Data-driven clinical prediction algorithms are used widely by clinicians. Understanding what factors can impact the performance and fairness of data-driven algorithms is an important step towards achieving equitable healthcare. To investigate the impact of modeling choices on the algorithmic performance and fairness, we make use of a case study to build a prediction algorithm for estimating glomerular filtration rate (GFR) based on the patient's electronic health record (EHR). We compare three distinct approaches for estimating GFR: CKD-EPI equations, epidemiological models, and EHR-based models. For epidemiological models and EHR-based models, four machine learning models of varying computational complexity (i.e., linear regression, support vector machine, random forest regression, and neural network) were compared. Performance metrics included root mean squared error (RMSE), median difference, and the proportion of GFR estimates within 30% of the measured GFR value (P30). Differential performance between non-African American and African American group was used to assess algorithmic fairness with respect to race. Our study showed that the variable race had a negligible effect on error, accuracy, and differential performance. Furthermore, including more relevant clinical features (e.g., common comorbidities of chronic kidney disease) and using more complex machine learning models, namely random forest regression, significantly lowered the estimation error of GFR. However, the difference in performance between African American and non-African American patients did not decrease, where the estimation error for African American patients remained consistently higher than non-African American patients, indicating that more objective patient characteristics should be discovered and included to improve algorithm performance.
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Affiliation(s)
- Linying Zhang
- Department of Biomedical Informatics Columbia University, New York, NY, USA
- Institute for Informatics, Data Science, and Biostatistics Washington University in St. Louis, St. Louis, MO, USA
| | - Lauren R Richter
- Department of Biomedical Informatics Columbia University, New York, NY, USA
| | - Tevin Kim
- Department of Biomedical Informatics Columbia University, New York, NY, USA
| | - George Hripcsak
- Department of Biomedical Informatics Columbia University, New York, NY, USA
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