{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T06:08:47Z","timestamp":1784182127089,"version":"3.55.0"},"reference-count":43,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T00:00:00Z","timestamp":1784160000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Digit. Health"],"abstract":"\n Background<\/jats:title>\n Diabetic kidney disease (DKD) shows heterogeneous responses to integrative medicine treatment (IMT). A critical unmet need in DKD management is the inability to predict IMT response, which is essential for advancing personalized treatment strategies.<\/jats:p>\n <\/jats:sec>\n \n Aim<\/jats:title>\n To develop and validate an explainable model for predicting likelihood of favorable outcome under IMT exposure in adult DKD patients.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n \n A retrospective cohort comprising 7,400 patients with diabetic kidney disease (DKD) from 2010 to 2018 was analyzed. Among them, 3,900 consecutive cases diagnosed between 2010 and 2014 were randomly divided in a 7:3 ratio into a training set (\n n<\/jats:italic>\n \u2009=\u20092,730) and an internal test set (\n n<\/jats:italic>\n \u2009=\u20091,170), while 3,500 cases from 2014 to 2018 served as an temporal validation cohort. Feature selection was performed using the Boruta algorithm, followed by LASSO regression, Random Forest, and XGBoost-SHAP analysis. Predictive models including logistic regression, LASSO, and XGBoost were developed and evaluated based on the area under the receiver operating characteristic curve (AUC). Further assessment of model performance included sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and the F1 score. The optimal XGBoost classifier was subsequently deployed as an interactive, single-page web application using the R Shiny framework.\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n XGBoost performed best (AUC\u2009=\u20090.783 in training, 0.715 in test and 0.762 in validation set), with 10 key variables, namely creatinine (cr), uric acid (ua), age, red blood cell count (rbc), urea, glucose (glu), platelet count (plt), calcium (ca), white blood cell count (wbc), and sodium (Na). The web app enabled real-time prediction (\n https:\/\/predictionfordkd.shinyapps.io\/Prediction\/<\/jats:ext-link>\n ).\n <\/jats:p>\n <\/jats:sec>\n \n Conclusion<\/jats:title>\n The model effectively predicts likelihood of favorable outcome under IMT exposure in DKD, aiding personalized treatment.<\/jats:p>\n <\/jats:sec>","DOI":"10.3389\/fdgth.2026.1803468","type":"journal-article","created":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T05:36:53Z","timestamp":1784180213000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Identification and validation of an explainable prediction model of favorable outcome under integrative medicine treatment exposure in DKD adult patients: a retrospective cohort study"],"prefix":"10.3389","volume":"8","author":[{"given":"Li","family":"Jiang","sequence":"first","affiliation":[{"name":"Diabetes Department of Integrative Medicine, China National Center for Integrated Traditional Chinese and Western Medicine, China-Japan Friendship Hospital","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haojun","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Clinical Medical Sciences, China-Japan Friendship Hospital","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanmei","family":"Wang","sequence":"additional","affiliation":[{"name":"Diabetes Department of Integrative Medicine, China National Center for Integrated Traditional Chinese and Western Medicine, China-Japan Friendship Hospital","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meihua","family":"Yan","sequence":"additional","affiliation":[{"name":"Institute of Clinical Medical Sciences, China-Japan Friendship Hospital","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiai","family":"Wu","sequence":"additional","affiliation":[{"name":"Diabetes Department of Integrative Medicine, China National Center for Integrated Traditional Chinese and Western Medicine, China-Japan Friendship Hospital","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,16]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"727","DOI":"10.3390\/jcm14030727","article-title":"Mechanisms, biomarkers, and treatment approaches for diabetic kidney disease: current insights and future perspectives","volume":"14","author":"Joumaa","year":"2025","journal-title":"J Clin Med"},{"key":"B2","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1186\/s13098-025-01726-4","article-title":"Decoding diabetic kidney disease: a comprehensive review of interconnected pathways, molecular mediators, and therapeutic 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