{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T17:22:35Z","timestamp":1787505755780,"version":"build-2736575974"},"reference-count":72,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T00:00:00Z","timestamp":1765756800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T00:00:00Z","timestamp":1765756800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["dom-pubs.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Diabetes Obesity Metabolism"],"published-print":{"date-parts":[[2026,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Diabetic kidney disease (DKD) and diabetic nephropathy (DN) affect around 40% of diabetic patients but lack accurate risk prediction tools that include social determinants and demographic complexity. We developed and validated an ensemble machine learning model for three\u2010year DKD\/DN risk prediction with deployment readiness.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We analysed 18\u2009742 eligible adult type 2 diabetic patients from Prince Sultan Military Medical City (PSMMC) registry between 2019 and 2024 in Riyadh, Saudi Arabia. Using temporal patient\u2010level splitting, we developed a stacked ensemble model (LightGBM + CoxBoost) with several features including multiple literature\u2010informed imputed variables including family history, non\u2010steroidal anti\u2010inflammatory drug (NSAID) use, socioeconomic deprivation, diabetic retinopathy severity, and antihypertensive medications, imputed via Bayesian multiple imputation by chained equations (MICE) with external study priors. Primary outcome was incident\/progressive DKD\/DN within 3\u2009years' timeframe. We assessed discrimination, calibration, model utilisation, and algorithmic fairness.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The final model achieved excellent discrimination (receiver operating characteristic [AUROC] of 0.852, 95% CI 0.847\u20130.857) and near\u2010perfect calibration (slope 0.98, intercept \u22120.012) on multi\u2010trial validation. Decision curve evaluation demonstrated superior net benefit (+22 events prevented per 1000 patients at 10% threshold) compared to treat\u2010all strategies. Bootstrap validation showed minimal optimism in discrimination (\n                      <jats:italic>C<\/jats:italic>\n                      \u2010statistic optimism\u2009=\u20090.005). No algorithmic bias was detected across demographic subgroups (maximum |\u0394\u2010AUROC|\u2009=\u20090.010). Prior sensitivity analysis confirmed validity and significance (AUROC variation \u22640.008). The model was engineered and deployed as an interactive web\u2010based application (\n                      <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/nephrarisk.streamlit.app\/\">https:\/\/nephrarisk.streamlit.app\/<\/jats:ext-link>\n                      ).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Our developed and demonstrated model provided accurate and well\u2010fair DKD\/DN risk prediction with excellent calibration, allowing for better decision making with deployment as a web\u2010based research tool and framework for future prospective clinical validation. Further validation and testing are warranted from different centres and healthcare systems to increase confidence and dissemination of our model findings for better utilisation purposes in the future.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/dom.70385","type":"journal-article","created":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T11:10:22Z","timestamp":1765797022000},"page":"1997-2026","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Literature\u2010informed ensemble machine learning for three\u2010year diabetic kidney disease risk prediction in type 2 diabetes: Development, validation, and deployment of the\n                    <scp>PSMMC NephraRisk<\/scp>\n                    model"],"prefix":"10.1111","volume":"28","author":[{"given":"Ayla M.","family":"Tourkmani","sequence":"first","affiliation":[{"name":"Department of Family and Community Medicine Prince Sultan Military Medical City  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