{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T07:03:33Z","timestamp":1789715013843,"version":"4.0.1"},"reference-count":52,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"7","content-domain":{"domain":["www.ahajournals.org"],"crossmark-restriction":true},"short-container-title":["JAHA"],"published-print":{"date-parts":[[2026,4,7]]},"abstract":"<jats:sec xml:lang=\"en\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p xml:lang=\"en\">Chronic kidney disease (CKD) and heart failure (HF) share pathophysiological mechanisms, rendering HF one of the most burdensome cardiovascular complication in CKD. Current HF prediction models, derived from the general population, exhibit limited accuracy in CKD, thus necessitating a CKD\u2010specific risk model and clinical implementation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec xml:lang=\"en\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p xml:lang=\"en\">The development set comprised 52\u2009251 patients with CKD from the China Renal Data System (70% training; 30% internal validation). External validation used 21\u2009798 patients from independent Chinese hospitals and 3323 UK Biobank participants. Outcome was 5\u2010year new\u2010onset HF. Five machine learning models were developed, with performance assessed via area under the curve and compared using DeLong test. The top\u2010performing extreme gradient boosting model was simplified via forward stepwise selection; feature importance quantified using Shapley additive explanations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec xml:lang=\"en\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p xml:lang=\"en\">\n                      In the Chinese external validation cohort, the extreme gradient boosting model outperformed others (area under the curve, 0.879 [95% CI, 0.871\u20130.887]; DeLong\n                      <jats:italic toggle=\"no\">P<\/jats:italic>\n                      &lt;0.05), with an area under the curve of 0.851 (95% CI, 0.831\u20130.870) in the UK Biobank cohort. The model was simplified to 9 predictors; Shapley additive explanations analysis ranked estimated glomerular filtration rate and albuminuria as among the most important features. In Chinese and UK Biobank external validation sets, the simplified extreme gradient boosting model showed a \u0394C\u2010statistic of 0.050 (0.045\u20130.056) and 0.006 (\u22120.013\u20130.024) versus the Atherosclerosis Risk in Communities risk score, and 0.194 (0.183\u20130.204) and 0.145 (0.107\u20130.182) versus the Predicting Risk of Cardiovascular Disease Events Equation, respectively. A web\u2010based calculator was deployed (\n                      <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/clinician.shinyapps.io\/HF_Risk_Predictor_for_CKD\/\">https:\/\/clinician.shinyapps.io\/HF_Risk_Predictor_for_CKD\/<\/jats:ext-link>\n                      ).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec xml:lang=\"en\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p xml:lang=\"en\">The 9\u2010variable extreme gradient boosting model tailored for patients with CKD may help predict HF risk in this high\u2010risk population. Validation across diverse populations is warranted to confirm its generalizability.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1161\/jaha.125.046156","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T11:09:12Z","timestamp":1773832152000},"update-policy":"https:\/\/doi.org\/10.1161\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":3,"title":["Development and Validation of a Machine Learning Model for Incident Heart Failure Prediction in Chronic Kidney Disease: A\u00a0Multicenter Cohort Study"],"prefix":"10.1161","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7348-8903","authenticated-orcid":false,"given":"Yi","family":"Lu","sequence":"first","affiliation":[{"name":"The Third Affiliated Hospital of Southern Medical University","place":["Guangzhou, China"]},{"name":"University of South China","place":["Hengyang, 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