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      Prediction tool for renal adaptation after living kidney donation using interpretable machine learning

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          Abstract

          Introduction

          Post-donation renal outcomes are a crucial issue for living kidney donors considering young donors’ high life expectancy and elderly donors’ comorbidities that affect kidney function. We developed a prediction model for renal adaptation after living kidney donation using interpretable machine learning.

          Methods

          The study included 823 living kidney donors who underwent nephrectomy in 2009–2020. AutoScore, a machine learning-based score generator, was used to develop a prediction model. Fair and good renal adaptation were defined as post-donation estimated glomerular filtration rate (eGFR) of ≥ 60 mL/min/1.73 m 2 and ≥ 65% of the pre-donation values, respectively.

          Results

          The mean age was 45.2 years; 51.6% were female. The model included pre-donation demographic and laboratory variables, GFR measured by diethylenetriamine pentaacetate scan, and computed tomography kidney volume/body weight of both kidneys and the remaining kidney. The areas under the receiver operating characteristic curve were 0.846 (95% confidence interval, 0.762–0.930) and 0.626 (0.541–0.712), while the areas under the precision-recall curve were 0.965 (0.944–0.978) and 0.709 (0.647–0.788) for fair and good renal adaptation, respectively. An interactive clinical decision support system was developed. 1

          Conclusion

          The prediction tool for post-donation renal adaptation showed good predictive capability and may help clinical decisions through an easy-to-use web-based application.

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          Most cited references34

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          A new equation to estimate glomerular filtration rate.

          Equations to estimate glomerular filtration rate (GFR) are routinely used to assess kidney function. Current equations have limited precision and systematically underestimate measured GFR at higher values. To develop a new estimating equation for GFR: the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation. Cross-sectional analysis with separate pooled data sets for equation development and validation and a representative sample of the U.S. population for prevalence estimates. Research studies and clinical populations ("studies") with measured GFR and NHANES (National Health and Nutrition Examination Survey), 1999 to 2006. 8254 participants in 10 studies (equation development data set) and 3896 participants in 16 studies (validation data set). Prevalence estimates were based on 16,032 participants in NHANES. GFR, measured as the clearance of exogenous filtration markers (iothalamate in the development data set; iothalamate and other markers in the validation data set), and linear regression to estimate the logarithm of measured GFR from standardized creatinine levels, sex, race, and age. In the validation data set, the CKD-EPI equation performed better than the Modification of Diet in Renal Disease Study equation, especially at higher GFR (P < 0.001 for all subsequent comparisons), with less bias (median difference between measured and estimated GFR, 2.5 vs. 5.5 mL/min per 1.73 m(2)), improved precision (interquartile range [IQR] of the differences, 16.6 vs. 18.3 mL/min per 1.73 m(2)), and greater accuracy (percentage of estimated GFR within 30% of measured GFR, 84.1% vs. 80.6%). In NHANES, the median estimated GFR was 94.5 mL/min per 1.73 m(2) (IQR, 79.7 to 108.1) vs. 85.0 (IQR, 72.9 to 98.5) mL/min per 1.73 m(2), and the prevalence of chronic kidney disease was 11.5% (95% CI, 10.6% to 12.4%) versus 13.1% (CI, 12.1% to 14.0%). The sample contained a limited number of elderly people and racial and ethnic minorities with measured GFR. The CKD-EPI creatinine equation is more accurate than the Modification of Diet in Renal Disease Study equation and could replace it for routine clinical use. National Institute of Diabetes and Digestive and Kidney Diseases.
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            Evaluation and management of chronic kidney disease: synopsis of the kidney disease: improving global outcomes 2012 clinical practice guideline.

            The Kidney Disease: Improving Global Outcomes (KDIGO) organization developed clinical practice guidelines in 2012 to provide guidance on the evaluation, management, and treatment of chronic kidney disease (CKD) in adults and children who are not receiving renal replacement therapy. The KDIGO CKD Guideline Development Work Group defined the scope of the guideline, gathered evidence, determined topics for systematic review, and graded the quality of evidence that had been summarized by an evidence review team. Searches of the English-language literature were conducted through November 2012. Final modification of the guidelines was informed by the KDIGO Board of Directors and a public review process involving registered stakeholders. The full guideline included 110 recommendations. This synopsis focuses on 10 key recommendations pertinent to definition, classification, monitoring, and management of CKD in adults.
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              Global Epidemiology of End-Stage Kidney Disease and Disparities in Kidney Replacement Therapy

              <b><i>Background:</i></b> The global epidemiology of end-stage kidney disease (ESKD) reflects each nation’s unique genetic, environmental, lifestyle, and sociodemographic characteristics. The response to ESKD, particularly regarding kidney replacement therapy (KRT), depends on local disease burden, culture, and socioeconomics. Here, we explore geographic variation and global trends in ESKD incidence and prevalence and examine variations in KRT modality, practice patterns, and mortality. We conclude with a discussion on disparities in access to KRT and strategies to reduce ESKD global burden and to improve access to treatment in low- and middle-income countries (LMICs). <b><i>Summary:</i></b> From 2003 to 2016, incidence rates of treated ESKD were relatively stable in many higher income countries but rose substantially predominantly in East and Southeast Asia. The prevalence of treated ESKD has increased worldwide, likely due to improving ESKD survival, population demographic shifts, higher prevalence of ESKD risk factors, and increasing KRT access in countries with growing economies. Unadjusted 5-year survival of ESKD patients on KRT was 41% in the USA, 48% in Europe, and 60% in Japan. Dialysis is the predominant KRT in most countries, with hemodialysis being the most common modality. Variations in dialysis practice patterns account for some of the differences in survival outcomes globally. Worldwide, there is a greater prevalence of KRT at higher income levels, and the number of people who die prematurely because of lack of KRT access is estimated at up to 3 times higher than the number who receive treatment. <b><i>Key Messages:</i></b> Many people worldwide in need of KRT as a life-sustaining treatment do not receive it, mostly in LMICs where health care resources are severely limited. This large treatment gap demands a focus on population-based prevention strategies and development of affordable and cost-effective KRT. Achieving global equity in KRT access will require concerted efforts in advocating effective public policy, health care delivery, workforce capacity, education, research, and support from the government, private sector, nongovernmental, and professional organizations.
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                Author and article information

                Contributors
                Journal
                Front Med (Lausanne)
                Front Med (Lausanne)
                Front. Med.
                Frontiers in Medicine
                Frontiers Media S.A.
                2296-858X
                14 July 2023
                2023
                : 10
                : 1222973
                Affiliations
                [1] 1Division of Nephrology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine , Seoul, Republic of Korea
                [2] 2Department of Biomedical Systems Informatics, Yonsei University College of Medicine , Seoul, Republic of Korea
                [3] 3Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University , Seoul, Republic of Korea
                [4] 4Smart Health Lab, Research Institute of Future Medicine, Samsung Medical Center , Seoul, Republic of Korea
                [5] 5Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine , Seoul, Republic of Korea
                Author notes

                Edited by: Kyu Ha Huh, Yonsei University, Republic of Korea

                Reviewed by: Byung Ha Chung, The Catholic University of Korea, Republic of Korea; Myung-Gyu Kim, Korea University, Republic of Korea

                *Correspondence: Hye Ryoun Jang, shinehr@ 123456skku.edu

                These authors share first authorship

                Article
                10.3389/fmed.2023.1222973
                10375292
                393eb974-04c3-4bf9-ad6f-043093556300
                Copyright © 2023 Jeon, Yu, Song, Jung, Lee, Lee, Huh, Cha and Jang.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 15 May 2023
                : 22 June 2023
                Page count
                Figures: 3, Tables: 3, Equations: 0, References: 35, Pages: 10, Words: 6406
                Funding
                Funded by: National Research Foundation of Korea, doi 10.13039/501100003725;
                Award ID: 2022R1A2B5B01001298
                Award ID: 2019R1A5A2027340
                Funded by: Korean Fund for Regenerative Medicine (KFRM)
                Award ID: 22A0302L1-01
                Funded by: Ministry of Health & Welfare, Republic of Korea
                Award ID: HR22C1363
                Funded by: Ministry of Science and ICT
                Funded by: Ministry of Health & Welfare
                Categories
                Medicine
                Original Research
                Custom metadata
                Nephrology

                kidney transplantation,renal adaptation,living donor,machine learning,autoscore

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