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<!DOCTYPE html> <html > <head> <meta charset="utf-8"> <meta rel="search" type="application/opensearchdescription+xml" href="/open_search.xml" title="Academia.edu"> <meta content="width=device-width, initial-scale=1" name="viewport"> <meta name="google-site-verification" content="bKJMBZA7E43xhDOopFZkssMMkBRjvYERV-NaN4R6mrs"> <meta name="csrf-param" content="authenticity_token" /> <meta name="csrf-token" content="cAbKugtyEB3JRX2h2XqvuaQ1UB33luHm89JHy+oKLdLY9HisvzKJYmapT2KXZrpluX8fy+vG7nNm3IaHmplZRg==" /> <meta name="citation_title" content="A useful scoring system for the prediction and management of delayed graft function following kidney transplantation from cadaveric donors" /> <meta name="citation_journal_title" content="Kidney International" /> <meta name="citation_author" content="Florent Borgne" /> <meta name="twitter:card" content="summary" /> <meta name="twitter:url" content="https://www.academia.edu/55455824/A_useful_scoring_system_for_the_prediction_and_management_of_delayed_graft_function_following_kidney_transplantation_from_cadaveric_donors" /> <meta name="twitter:title" content="A useful scoring system for the prediction and management of delayed graft function following kidney transplantation from cadaveric donors" /> <meta name="twitter:description" content="Delayed graft function (DGF) is a common complication in kidney transplantation and is known to be correlated with short- and long-term graft outcomes. 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Here we explored the possibility of developing a simple tool that could predict with good" /> <title>(PDF) A useful scoring system for the prediction and management of delayed graft function following kidney transplantation from cadaveric donors | Florent Borgne - Academia.edu</title> <link rel="canonical" href="https://www.academia.edu/55455824/A_useful_scoring_system_for_the_prediction_and_management_of_delayed_graft_function_following_kidney_transplantation_from_cadaveric_donors" /> <script async src="https://www.googletagmanager.com/gtag/js?id=G-5VKX33P2DS"></script> <script> window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'G-5VKX33P2DS', { cookie_domain: 'academia.edu', send_page_view: false, }); gtag('event', 'page_view', { 'controller': "single_work", 'action': "show", 'controller_action': 'single_work#show', 'logged_in': 'false', 'edge': 'unknown', // Send nil if there is no A/B test bucket, in case some records get logged // with missing data - that way we can distinguish between the two cases. // ab_test_bucket should be of the form <ab_test_name>:<bucket> 'ab_test_bucket': null, }) </script> <script> var $controller_name = 'single_work'; var $action_name = "show"; var $rails_env = 'production'; var $app_rev = '1bba2257721fc9a617e8a7d41e91ce5a46d8bfc4'; var $domain = 'academia.edu'; var $app_host = "academia.edu"; var $asset_host = "academia-assets.com"; var $start_time = new Date().getTime(); var $recaptcha_key = "6LdxlRMTAAAAADnu_zyLhLg0YF9uACwz78shpjJB"; var $recaptcha_invisible_key = "6Lf3KHUUAAAAACggoMpmGJdQDtiyrjVlvGJ6BbAj"; var $disableClientRecordHit = false; </script> <script> window.require = { config: function() { return function() {} } } </script> <script> window.Aedu = window.Aedu || {}; window.Aedu.hit_data = null; window.Aedu.serverRenderTime = new Date(1734016846000); window.Aedu.timeDifference = new Date().getTime() - 1734016846000; </script> <script type="application/ld+json">{"@context":"https://schema.org","@type":"ScholarlyArticle","abstract":"Delayed graft function (DGF) is a common complication in kidney transplantation and is known to be correlated with short- and long-term graft outcomes. Here we explored the possibility of developing a simple tool that could predict with good confidence the occurrence of DGF and could be helpful in current clinical practice. We built a score, tentatively called DGFS, from a French multicenter and prospective cohort of 1844 adult recipients of deceased donor kidneys collected since 2007, and computerized in the Données Informatisées et VAlidées en Transplantation databank. Only five explicative variables (cold ischemia time, donor age, donor serum creatinine, recipient body mass index, and induction therapy) contributed significantly to the DGF prediction. These were associated with a good predictive capacity (area under the ROC curve at 0.73). The DGFS calculation is facilitated by an application available on smartphones, tablets, or computers at www.divat.fr/en/online-calculators/dg...","author":[{"@context":"https://schema.org","@type":"Person","name":"Florent Borgne"}],"contributor":[],"dateCreated":"2021-10-04","dateModified":"2021-10-04","headline":"A useful scoring system for the prediction and management of delayed graft function following kidney transplantation from cadaveric donors","image":"https://attachments.academia-assets.com/71316911/thumbnails/1.jpg","inLanguage":"en","keywords":["Kidney transplantation","Adolescent","Humans","Child","Kidney","Female","Infant","Body Mass Index","Clinical Sciences","Cadaver","Aged","Adult","Age Factors","Area Under Curve","Creatinine","Delayed graft function","Cohort Studies","Child preschool","Immunosuppressive Agents","Decision Support Techniques"],"publication":"Kidney International","publisher":{"@context":"https://schema.org","@type":"Organization","name":null},"sourceOrganization":[{"@context":"https://schema.org","@type":"EducationalOrganization","name":"univ-nantes"}],"thumbnailUrl":"https://attachments.academia-assets.com/71316911/thumbnails/1.jpg","url":"https://www.academia.edu/55455824/A_useful_scoring_system_for_the_prediction_and_management_of_delayed_graft_function_following_kidney_transplantation_from_cadaveric_donors"}</script><link rel="stylesheet" media="all" href="//a.academia-assets.com/assets/single_work_page/loswp-102fa537001ba4d8dcd921ad9bd56c474abc201906ea4843e7e7efe9dfbf561d.css" /><link rel="stylesheet" media="all" href="//a.academia-assets.com/assets/design_system/body-8d679e925718b5e8e4b18e9a4fab37f7eaa99e43386459376559080ac8f2856a.css" /><link rel="stylesheet" media="all" href="//a.academia-assets.com/assets/design_system/button-3cea6e0ad4715ed965c49bfb15dedfc632787b32ff6d8c3a474182b231146ab7.css" /><link rel="stylesheet" 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Here we explored the possibility of developing a simple tool that could predict with good confidence the occurrence of DGF and could be helpful in current clinical practice. We built a score, tentatively called DGFS, from a French multicenter and prospective cohort of 1844 adult recipients of deceased donor kidneys collected since 2007, and computerized in the Données Informatisées et VAlidées en Transplantation databank. Only five explicative variables (cold ischemia time, donor age, donor serum creatinine, recipient body mass index, and induction therapy) contributed significantly to the DGF prediction. These were associated with a good predictive capacity (area under the ROC curve at 0.73). The DGFS calculation is facilitated by an application available on smartphones, tablets, or computers at www.divat.fr/en/online-calculators/dg...","publication_name":"Kidney International"},"document_type":"paper","pre_hit_view_count_baseline":null,"quality":"high","language":"en","title":"A useful scoring system for the prediction and management of delayed graft function following kidney transplantation from cadaveric donors","broadcastable":false,"draft":null,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [36968195]; window.loswp.locale = "en"; window.loswp.countryCode = "SG"; window.loswp.cwvAbTestBucket = ""; window.loswp.designVariant = "ds_vanilla"; window.loswp.fullPageMobileSutdModalVariant = "full_page_mobile_sutd_modal"; window.loswp.useOptimizedScribd4genScript = false; window.loginModal = {}; window.loginModal.appleClientId = 'edu.academia.applesignon';</script><script defer="" src="https://accounts.google.com/gsi/client"></script><div class="ds-loswp-container"><div class="ds-work-card--grid-container"><div class="ds-work-card--container js-loswp-work-card"><div class="ds-work-card--cover"><div class="ds-work-cover--wrapper"><div class="ds-work-cover--container"><button class="ds-work-cover--clickable js-swp-download-button" data-signup-modal="{"location":"swp-splash-paper-cover","attachmentId":71316911,"attachmentType":"pdf"}"><img alt="First page of “A useful scoring system for the prediction and management of delayed graft function following kidney transplantation from cadaveric donors”" class="ds-work-cover--cover-thumbnail" src="https://0.academia-photos.com/attachment_thumbnails/71316911/mini_magick20211004-4360-4ousds.png?1633362393" /><img alt="PDF Icon" class="ds-work-cover--file-icon" src="//a.academia-assets.com/images/single_work_splash/adobe_icon.svg" /><div class="ds-work-cover--hover-container"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span><p>Download Free PDF</p></div><div class="ds-work-cover--ribbon-container">Download Free PDF</div><div class="ds-work-cover--ribbon-triangle"></div></button></div></div></div><div class="ds-work-card--work-information"><h1 class="ds-work-card--work-title">A useful scoring system for the prediction and management of delayed graft function following kidney transplantation from cadaveric donors</h1><div class="ds-work-card--work-authors ds-work-card--detail"><a class="ds-work-card--author js-wsj-grid-card-author ds2-5-body-md ds2-5-body-link" data-author-id="36968195" href="https://univ-nantes.academia.edu/FlorentBorgne"><img alt="Profile image of Florent Borgne" class="ds-work-card--author-avatar" src="//a.academia-assets.com/images/s65_no_pic.png" />Florent Borgne</a></div><div class="ds-work-card--detail"><p class="ds-work-card--detail ds2-5-body-sm">Kidney International</p><div class="ds-work-card--work-metadata"><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">visibility</span><p class="ds2-5-body-sm" id="work-metadata-view-count">…</p></div><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">description</span><p class="ds2-5-body-sm">10 pages</p></div><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">link</span><p class="ds2-5-body-sm">1 file</p></div></div><script>(async () => { const workId = 55455824; 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if (!viewCountBody) { throw new Error('Failed to find work views element'); } viewCountBody.textContent = `${commaizedViewCount} views`; } catch (error) { // Remove the whole views element if there was some issue parsing. document.getElementById('work-metadata-view-count')?.parentNode?.remove(); throw new Error(`Failed to parse view count: ${viewCount}`, error); } }; // If the DOM is still loading, wait for it to be ready before updating the view count. if (document.readyState === "loading") { document.addEventListener('DOMContentLoaded', () => { updateViewCount(viewCount); }); // Otherwise, just update it immediately. } else { updateViewCount(viewCount); } })();</script></div><p class="ds-work-card--work-abstract ds-work-card--detail ds2-5-body-md">Delayed graft function (DGF) is a common complication in kidney transplantation and is known to be correlated with short- and long-term graft outcomes. Here we explored the possibility of developing a simple tool that could predict with good confidence the occurrence of DGF and could be helpful in current clinical practice. We built a score, tentatively called DGFS, from a French multicenter and prospective cohort of 1844 adult recipients of deceased donor kidneys collected since 2007, and computerized in the Données Informatisées et VAlidées en Transplantation databank. Only five explicative variables (cold ischemia time, donor age, donor serum creatinine, recipient body mass index, and induction therapy) contributed significantly to the DGF prediction. These were associated with a good predictive capacity (area under the ROC curve at 0.73). The DGFS calculation is facilitated by an application available on smartphones, tablets, or computers at www.divat.fr/en/online-calculators/dg...</p><div class="ds-work-card--button-container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--work-card","attachmentId":71316911,"attachmentType":"pdf","workUrl":"https://www.academia.edu/55455824/A_useful_scoring_system_for_the_prediction_and_management_of_delayed_graft_function_following_kidney_transplantation_from_cadaveric_donors"}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--work-card","attachmentId":71316911,"attachmentType":"pdf","workUrl":"https://www.academia.edu/55455824/A_useful_scoring_system_for_the_prediction_and_management_of_delayed_graft_function_following_kidney_transplantation_from_cadaveric_donors"}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div></div></div></div><div data-auto_select="false" data-client_id="331998490334-rsn3chp12mbkiqhl6e7lu2q0mlbu0f1b" data-doc_id="71316911" data-landing_url="https://www.academia.edu/55455824/A_useful_scoring_system_for_the_prediction_and_management_of_delayed_graft_function_following_kidney_transplantation_from_cadaveric_donors" data-login_uri="https://www.academia.edu/registrations/google_one_tap" data-moment_callback="onGoogleOneTapEvent" id="g_id_onload"></div><div class="ds-top-related-works--grid-container"><div class="ds-related-content--container ds-top-related-works--container"><h2 class="ds-related-content--heading">Related papers</h2><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="0" data-entity-id="14503011" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/14503011/A_clinical_scoring_system_highly_predictive_of_long_term_kidney_graft_survival">A clinical scoring system highly predictive of long-term kidney graft survival</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="33453030" href="https://independent.academia.edu/PascalDaguin">Pascal Daguin</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Kidney International, 2010</p><p class="ds-related-work--abstract ds2-5-body-sm">Determining early surrogate markers of long-term graft outcome is important for optimal medical management. In order to identify such markers, we used clinical information from a cross-validated French database (Données Informatisées et VAlidées en Transplantation) of 2169 kidney transplant recipients to construct a composite score 1 year after transplantation. This Kidney Transplant Failure Score took into account a series of</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"A clinical scoring system highly predictive of long-term kidney graft survival","attachmentId":44108725,"attachmentType":"pdf","work_url":"https://www.academia.edu/14503011/A_clinical_scoring_system_highly_predictive_of_long_term_kidney_graft_survival","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/14503011/A_clinical_scoring_system_highly_predictive_of_long_term_kidney_graft_survival"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="1" data-entity-id="63255744" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/63255744/A_Simple_Tool_to_Predict_Outcomes_After_Kidney_Transplant">A Simple Tool to Predict Outcomes After Kidney Transplant</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="62037816" href="https://independent.academia.edu/AjayIsrani">Ajay Israni</a></div><p class="ds-related-work--metadata ds2-5-body-xs">American Journal of Kidney Diseases, 2010</p><p class="ds-related-work--abstract ds2-5-body-sm">Background: Surprisingly few tools have been developed to predict outcomes after kidney transplant. Study Design: Retrospective observational cohort study. Setting & Participants: Adult patients from US Renal Data System (USRDS) data who underwent deceased donor kidney transplant in 2000-2006. Predictor: Full and abbreviated prediction tools for graft loss using candidate predictor variables available in the USRDS registry, including data from the Organ Procurement and Transplantation Network and the Centers for Medicare & Medicaid Services End-Stage Renal Disease Program. Outcomes: Graft loss within 5 years, defined as return to maintenance dialysis therapy, preemptive retransplant, or death with a functioning graft. Measurements: We used Cox proportional hazards analyses to develop separate tools for assessment (1) pretransplant, (2) at 7 days posttransplant, and (3) at 1 year posttransplant to predict subsequent risk of graft loss within 5 years of transplant. We used measures of discrimination and explained variation to determine the number of variables needed to predict outcomes at each assessment time in the full and abbreviated equations, creating simple user-friendly prediction tools. Results: Although we could identify 32, 29, and 18 variables that predicted graft loss assessed pretransplant and at 7 days and 1 year posttransplant ("full" models), 98% of the discriminatory ability and Ͼ80% of the variability explained by the full models could be achieved using only 11, 8, and 6 variables, respectively. Limitations: Comorbidity data were from the Centers for Medicare & Medicaid Medical Evidence Report, which may significantly underreport comorbid conditions; C statistic values may indicate only modest ability to discriminate risk for an individual patient. Conclusions: This method produced risk-prediction tools that can be used easily by patients and clinicians to aid in understanding the absolute and relative risk of graft loss within 5 years of transplant.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"A Simple Tool to Predict Outcomes After Kidney Transplant","attachmentId":75743463,"attachmentType":"pdf","work_url":"https://www.academia.edu/63255744/A_Simple_Tool_to_Predict_Outcomes_After_Kidney_Transplant","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/63255744/A_Simple_Tool_to_Predict_Outcomes_After_Kidney_Transplant"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="2" data-entity-id="109328212" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/109328212/Early_prediction_of_renal_graft_function_Analysis_of_a_multi_centre_multi_level_data_set">Early prediction of renal graft function: Analysis of a multi-centre, multi-level data set</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="42521709" href="https://humboldt-uni.academia.edu/MichalOrGuil">Michal Or-Guil</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2021</p><p class="ds-related-work--abstract ds2-5-body-sm">ABSTRACTIntroductionLong-term graft survival rates after renal transplantation are still moderate. We aimed to build an early predictor of an established long-term outcomes marker, the glomerular filtration rate (eGFR) one year post-transplant (eGFR-1y).Materials and MethodsA large cohort of 376 patients was characterized for a multi-level bio-marker panel including gene expression, cytokines, metabolomics and antibody reactivity profiles. Almost one thousand samples from the pre-transplant and early post-transplant period were analysed. Machine learning-based predictors were built employing stacked generalization.ResultsPre-transplant data led to a prediction achieving a Pearson’s correlation coefficient of r=0.39 between measured and predicted eGFR-1y. Two weeks post-transplant, the correlation was improved to r=0.63, and at the third month, to r=0.76. eGFR values were remarkably stable throughout the first year post-transplant and were the best estimators of eGFR-1y already two w...</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Early prediction of renal graft function: Analysis of a multi-centre, multi-level data set","attachmentId":107487145,"attachmentType":"pdf","work_url":"https://www.academia.edu/109328212/Early_prediction_of_renal_graft_function_Analysis_of_a_multi_centre_multi_level_data_set","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/109328212/Early_prediction_of_renal_graft_function_Analysis_of_a_multi_centre_multi_level_data_set"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="3" data-entity-id="59805335" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/59805335/A_Prognostic_Tool_for_Individualized_Prediction_of_Graft_Failure_Risk_within_Ten_Years_after_Kidney_Transplantation">A Prognostic Tool for Individualized Prediction of Graft Failure Risk within Ten Years after Kidney Transplantation</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="104136081" href="https://independent.academia.edu/DankoStamenic">Danko Stamenic</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Journal of Transplantation</p><p class="ds-related-work--abstract ds2-5-body-sm">Identification of patients at risk of kidney graft loss relies on early individual prediction of graft failure. Data from 616 kidney transplant recipients with a follow-up of at least one year were retrospectively studied. A joint latent class model investigating the impact of serum creatinine (Scr) time-trajectories and onset of de novo donor-specific anti-HLA antibody (dnDSA) on graft survival was developed. The capacity of the model to calculate individual predicted probabilities of graft failure over time was evaluated in 80 independent patients. The model classified the patients in three latent classes with significantly different Scr time profiles and different graft survivals. Donor age contributed to explaining latent class membership. In addition to the SCr classes, the other variables retained in the survival model were proteinuria measured one-year after transplantation (HR=2.4, p=0.01), pretransplant non-donor-specific antibodies (HR=3.3, p&lt;0.001), and dnDSA in patien...</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"A Prognostic Tool for Individualized Prediction of Graft Failure Risk within Ten Years after Kidney Transplantation","attachmentId":73538147,"attachmentType":"pdf","work_url":"https://www.academia.edu/59805335/A_Prognostic_Tool_for_Individualized_Prediction_of_Graft_Failure_Risk_within_Ten_Years_after_Kidney_Transplantation","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/59805335/A_Prognostic_Tool_for_Individualized_Prediction_of_Graft_Failure_Risk_within_Ten_Years_after_Kidney_Transplantation"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="4" data-entity-id="52889997" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/52889997/The_impact_of_deceased_donor_maintenance_on_delayed_kidney_allograft_function_A_machine_learning_analysis">The impact of deceased donor maintenance on delayed kidney allograft function: A machine learning analysis</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="44167977" href="https://independent.academia.edu/Tain%C3%A1VerasSandesFreitas">Tainá Veras Sandes Freitas</a></div><p class="ds-related-work--metadata ds2-5-body-xs">PLOS ONE</p><p class="ds-related-work--abstract ds2-5-body-sm">Background This study evaluated the risk factors for delayed graft function (DGF) in a country where its incidence is high, detailing donor maintenance-related (DMR) variables and using machine learning (ML) methods beyond the traditional regression-based models. Methods A total of 443 brain dead deceased donor kidney transplants (KT) from two Brazilian centers were retrospectively analyzed and the following DMR were evaluated using predictive modeling: arterial blood gas pH, serum sodium, blood glucose, urine output, mean arterial pressure, vasopressors use, and reversed cardiac arrest. Results Most patients (95.7%) received kidneys from standard criteria donors. The incidence of DGF was 53%. In multivariable logistic regression analysis, DMR variables did not impact on DGF occurrence. In post-hoc analysis including only KT with cold ischemia time<21h (n = 220), urine output in 24h prior to recovery surgery (OR = 0.639, 95%CI 0.444-0.919) and serum sodium (OR = 1.030, 95%CI 1.052-1.379) were risk factors for DGF. Using elastic net regularized regression model and ML analysis (decision tree, neural network and support vector machine), urine output and other DMR variables emerged as DGF predictors: mean arterial pressure, � 1 or high dose vasopressors and blood glucose. Conclusions Some DMR variables were associated with DGF, suggesting a potential impact of variables reflecting poor clinical and hemodynamic status on the incidence of DGF.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"The impact of deceased donor maintenance on delayed kidney allograft function: A machine learning analysis","attachmentId":69934388,"attachmentType":"pdf","work_url":"https://www.academia.edu/52889997/The_impact_of_deceased_donor_maintenance_on_delayed_kidney_allograft_function_A_machine_learning_analysis","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/52889997/The_impact_of_deceased_donor_maintenance_on_delayed_kidney_allograft_function_A_machine_learning_analysis"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="5" data-entity-id="89758710" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/89758710/Prediction_system_for_risk_of_allograft_loss_in_patients_receiving_kidney_transplants_international_derivation_and_validation_study">Prediction system for risk of allograft loss in patients receiving kidney transplants: international derivation and validation study</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="57820070" href="https://independent.academia.edu/CarmenLefaucheur">Carmen Lefaucheur</a></div><p class="ds-related-work--metadata ds2-5-body-xs">BMJ, 2019</p><p class="ds-related-work--abstract ds2-5-body-sm">ObjectiveTo develop and validate an integrative system to predict long term kidney allograft failure.DesignInternational cohort study.SettingThree cohorts including kidney transplant recipients from 10 academic medical centres from Europe and the United States.ParticipantsDerivation cohort: 4000 consecutive kidney recipients prospectively recruited in four French centres between 2005 and 2014. Validation cohorts: 2129 kidney recipients from three centres in Europe and 1428 from three centres in North America, recruited between 2002 and 2014. Additional validation in three randomised controlled trials (NCT01079143, EudraCT 2007-003213-13, and NCT01873157).Main outcome measureAllograft failure (return to dialysis or pre-emptive retransplantation). 32 candidate prognostic factors for kidney allograft survival were assessed.ResultsAmong the 7557 kidney transplant recipients included, 1067 (14.1%) allografts failed after a median post-transplant follow-up time of 7.12 (interquartile rang...</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Prediction system for risk of allograft loss in patients receiving kidney transplants: international derivation and validation study","attachmentId":93511474,"attachmentType":"pdf","work_url":"https://www.academia.edu/89758710/Prediction_system_for_risk_of_allograft_loss_in_patients_receiving_kidney_transplants_international_derivation_and_validation_study","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/89758710/Prediction_system_for_risk_of_allograft_loss_in_patients_receiving_kidney_transplants_international_derivation_and_validation_study"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="6" data-entity-id="17676914" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/17676914/Prediction_of_3_yr_cadaveric_graft_survival_based_on_pre_transplant_variables_in_a_large_national_dataset">Prediction of 3-yr cadaveric graft survival based on pre-transplant variables in a large national dataset</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="37594249" href="https://utah.academia.edu/SusanHorn">Susan Horn</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Clinical Transplantation, 2003</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Prediction of 3-yr cadaveric graft survival based on pre-transplant variables in a large national dataset","attachmentId":39652747,"attachmentType":"pdf","work_url":"https://www.academia.edu/17676914/Prediction_of_3_yr_cadaveric_graft_survival_based_on_pre_transplant_variables_in_a_large_national_dataset","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/17676914/Prediction_of_3_yr_cadaveric_graft_survival_based_on_pre_transplant_variables_in_a_large_national_dataset"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="7" data-entity-id="124854097" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/124854097/Developing_Clinical_Prognostic_Models_to_Predict_Graft_Survival_after_Renal_Transplantation_Comparison_of_Statistical_and_Machine_Learning_Models">Developing Clinical Prognostic Models to Predict Graft Survival after Renal Transplantation: Comparison of Statistical and Machine Learning Models</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="64182764" href="https://independent.academia.edu/MahtemeBekeleMuleta">Mahteme Bekele Muleta</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Research Square (Research Square), 2024</p><p class="ds-related-work--abstract ds2-5-body-sm">Introduction: Renal transplantation is a critical treatment that can save the lives of individuals who are suffering from end-stage renal disease (ESRD), but graft failure remains a significant concern. Accurate prediction of graft survival after renal transplantation is crucial as it enables clinicians to identify patients at higher risk of graft failure. This study aimed to develop clinical prognostic models for predicting graft survival after renal transplantation and compare the performance of various statistical and machine learning models. Methodology: The study utilized data from a retrospective cohort of renal transplant recipients at the Ethiopian National Kidney Transplantation Center from September 2015 to February 2022. Various statistical and machine learning models were evaluated based on their discrimination, calibration, and interpretability. The comparison of models included standard Cox, Lasso-Cox, Ridge-Cox, Elastic net-Cox, Random Survival Forest, and Stochastic Gradient Boosting. The prognostic predictors of graft survival were selected based on the significance and relative importance of variables in different models. Results: The study analyzed a total of 278 completed cases and observed the event of graft failure in 21 patients. The median graft survival time was 33 months, and the mean hazard of graft failure was 0.0755. The results revealed that the 1-year, 3-year, and 5-year graft survival rates are 0.936, 0.924, and 0.914 respectively. The study found that the Random Survival Forest and Stochastic Gradient Boosting models demonstrated the best calibration and discrimination performance shown by an equal AUC of 0.97 and the overlapped calibration plots. On the other hand, the Cox proportional hazards model has the highest interpretability and established superior accuracy in estimating survival probabilities, as evidenced by its lowest Brier score of 0.000071. The current study indicates that an episode of chronic rejection, recipient residence, an episode of acute 3 rejection, post-transplant urological complications, post-transplant nonadherence, blood urea nitrogen level, and number of post-transplant admissions were consistently identified as the top significant prognostic predictors of renal graft survival. Conclusions: The Random Survival Forest and Stochastic Gradient Boosting models demonstrated superior calibration and discrimination performance, while the Cox proportional hazards model offered accurate estimation of survival probabilities and interpretability. Clinicians should consider the trade-off between performance and interpretability when choosing a model. Incorporating these findings into clinical practice can improve risk stratification, enable early interventions, and inform personalized management strategies for kidney transplant recipients.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Developing Clinical Prognostic Models to Predict Graft Survival after Renal Transplantation: Comparison of Statistical and Machine Learning Models","attachmentId":119003038,"attachmentType":"pdf","work_url":"https://www.academia.edu/124854097/Developing_Clinical_Prognostic_Models_to_Predict_Graft_Survival_after_Renal_Transplantation_Comparison_of_Statistical_and_Machine_Learning_Models","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/124854097/Developing_Clinical_Prognostic_Models_to_Predict_Graft_Survival_after_Renal_Transplantation_Comparison_of_Statistical_and_Machine_Learning_Models"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="8" data-entity-id="20868666" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/20868666/Predicting_the_outcome_of_renal_transplantation">Predicting the outcome of renal transplantation</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="42178038" href="https://independent.academia.edu/ReinkeP">Petra Reinke</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Journal of the American Medical Informatics Association, 2012</p><p class="ds-related-work--abstract ds2-5-body-sm">Objective Renal transplantation has dramatically improved the survival rate of hemodialysis patients. However, with a growing proportion of marginal organs and improved immunosuppression, it is necessary to verify that the established allocation system, mostly based on human leukocyte antigen matching, still meets today's needs. The authors turn to machine-learning techniques to predict, from donorerecipient data, the estimated glomerular filtration rate (eGFR) of the recipient 1 year after transplantation. Design The patient's eGFR was predicted using donorerecipient characteristics available at the time of transplantation. Donors' data were obtained from Eurotransplant's database, while recipients' details were retrieved from Charité Campus Virchow-Klinikum's database. A total of 707 renal transplantations from cadaveric donors were included. Measurements Two separate datasets were created, taking features with <10% missing values for one and <50% missing values for the other. Four established regressors were run on both datasets, with and without feature selection.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Predicting the outcome of renal transplantation","attachmentId":41605952,"attachmentType":"pdf","work_url":"https://www.academia.edu/20868666/Predicting_the_outcome_of_renal_transplantation","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/20868666/Predicting_the_outcome_of_renal_transplantation"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="9" data-entity-id="84274655" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/84274655/Artificial_Intelligence_A_Tool_for_Risk_Assessment_of_Delayed_Graft_Function_in_Kidney_Transplant">Artificial Intelligence—A Tool for Risk Assessment of Delayed-Graft Function in Kidney Transplant</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="38604151" href="https://independent.academia.edu/KrajewskaMagdalena">Magdalena Krajewska</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Journal of Clinical Medicine, 2021</p><p class="ds-related-work--abstract ds2-5-body-sm">Delayed-graft function (DGF) might be responsible for shorter graft survival. Therefore, a clinical tool predicting its occurrence is vital for the risk assessment of transplant outcomes. In a single-center study, we conducted data mining and machine learning experiments, resulting in DGF predictive models based on random forest classifiers (RF) and an artificial neural network called multi-layer perceptron (MLP). All designed models had four common input parameters, determining the best accuracy and discriminant ability: donor’s eGFR, recipient’s BMI, donor’s BMI, and recipient–donor weight difference. RF and MLP designs, using these parameters, achieved an accuracy of 84.38% and an area under curve (AUC) 0.84. The model additionally implementing a donor’s age, gender, and Kidney Donor Profile Index (KDPI) accomplished an accuracy of 93.75% and an AUC of 0.91. The other configuration with the estimated post-transplant survival (EPTS) and the kidney donor risk profile (KDRI) achieve...</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Artificial Intelligence—A Tool for Risk Assessment of Delayed-Graft Function in Kidney Transplant","attachmentId":89354336,"attachmentType":"pdf","work_url":"https://www.academia.edu/84274655/Artificial_Intelligence_A_Tool_for_Risk_Assessment_of_Delayed_Graft_Function_in_Kidney_Transplant","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/84274655/Artificial_Intelligence_A_Tool_for_Risk_Assessment_of_Delayed_Graft_Function_in_Kidney_Transplant"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div></div></div><div class="ds-sticky-ctas--wrapper js-loswp-sticky-ctas hidden"><div class="ds-sticky-ctas--grid-container"><div class="ds-sticky-ctas--container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--sticky-ctas","attachmentId":71316911,"attachmentType":"pdf","workUrl":null}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--sticky-ctas","attachmentId":71316911,"attachmentType":"pdf","workUrl":null}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div></div></div><div class="ds-below-fold--grid-container"><div class="ds-work--container js-loswp-embedded-document"><div class="attachment_preview" data-attachment="Attachment_71316911" style="display: none"><div class="js-scribd-document-container"><div class="scribd--document-loading js-scribd-document-loader" style="display: block;"><img alt="Loading..." src="//a.academia-assets.com/images/loaders/paper-load.gif" /><p>Loading Preview</p></div></div><div style="text-align: center;"><div class="scribd--no-preview-alert js-preview-unavailable"><p>Sorry, preview is currently unavailable. You can download the paper by clicking the button above.</p></div></div></div></div><div class="ds-sidebar--container js-work-sidebar"><div class="ds-related-content--container"><h2 class="ds-related-content--heading">Related papers</h2><div class="ds-related-work--container js-related-work-sidebar-card" data-collection-position="0" data-entity-id="20705524" data-sort-order="default"><a class="ds-related-work--title js-related-work-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/20705524/Delayed_Graft_Function_and_Long_Term_Outcome_in_Kidney_Transplantation">Delayed Graft Function and Long-Term Outcome in Kidney Transplantation</a><div class="ds-related-work--metadata"><a class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="42183068" href="https://independent.academia.edu/AntonioMistretta">Antonio Mistretta</a><span>, </span><a class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="42007267" href="https://independent.academia.edu/AGiaquinta">A. Giaquinta</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Transplantation Proceedings, 2012</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Delayed Graft Function and Long-Term Outcome in Kidney Transplantation","attachmentId":41516326,"attachmentType":"pdf","work_url":"https://www.academia.edu/20705524/Delayed_Graft_Function_and_Long_Term_Outcome_in_Kidney_Transplantation","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-related-work-grid-card-view-pdf" href="https://www.academia.edu/20705524/Delayed_Graft_Function_and_Long_Term_Outcome_in_Kidney_Transplantation"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-related-work-sidebar-card" data-collection-position="1" data-entity-id="89483875" data-sort-order="default"><a class="ds-related-work--title js-related-work-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/89483875/Nomogram_for_Predicting_the_Likelihood_of_Delayed_Graft_Function_in_Adult_Cadaveric_Renal_Transplant_Recipients">Nomogram for Predicting the Likelihood of Delayed Graft Function in Adult Cadaveric Renal Transplant Recipients</a><div class="ds-related-work--metadata"><a class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="242723550" href="https://independent.academia.edu/DanielBrennan28">Daniel Brennan</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Journal of the American Society of Nephrology, 2003</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Nomogram for Predicting the Likelihood of Delayed Graft Function in Adult Cadaveric Renal Transplant Recipients","attachmentId":93281320,"attachmentType":"pdf","work_url":"https://www.academia.edu/89483875/Nomogram_for_Predicting_the_Likelihood_of_Delayed_Graft_Function_in_Adult_Cadaveric_Renal_Transplant_Recipients","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-related-work-grid-card-view-pdf" href="https://www.academia.edu/89483875/Nomogram_for_Predicting_the_Likelihood_of_Delayed_Graft_Function_in_Adult_Cadaveric_Renal_Transplant_Recipients"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-related-work-sidebar-card" data-collection-position="2" data-entity-id="56976966" data-sort-order="default"><a class="ds-related-work--title js-related-work-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/56976966/Predicting_early_renal_allograft_function_using_clinical_variables">Predicting early renal allograft function using clinical variables</a><div class="ds-related-work--metadata"><a class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="35220417" href="https://independent.academia.edu/JonniMoore">Jonni Moore</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Nephrology Dialysis …, 2007</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Predicting early renal allograft function using clinical variables","attachmentId":72099464,"attachmentType":"pdf","work_url":"https://www.academia.edu/56976966/Predicting_early_renal_allograft_function_using_clinical_variables","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-related-work-grid-card-view-pdf" href="https://www.academia.edu/56976966/Predicting_early_renal_allograft_function_using_clinical_variables"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-related-work-sidebar-card" data-collection-position="3" data-entity-id="111752362" data-sort-order="default"><a class="ds-related-work--title js-related-work-grid-card-title 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