{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T20:59:32Z","timestamp":1789419572073,"version":"build-2803163510"},"reference-count":74,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T00:00:00Z","timestamp":1778198400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Chronic kidney disease (CKD) confers a substantially elevated risk of cardiovascular (CV) mortality, primarily driven by heart failure (HF) and other CV complications. This study aimed to develop a nomogram for predicting incident HF and all-cause death in patients with CKD, and further identify individuals at high risk of these outcomes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We prospectively recruited 440 patients with CKD stages 3\u20135 and preserved ejection fraction (LVEF) from a nephropathy center between November 2020 to September 2024. Baseline clinical and demographic characteristics, biochemical and echocardiographic parameters, and data from quality-of-life scales were collected. Key predictors were identified using univariate Cox regression, Lasso, Random Forest, and XGBoost algorithms; these identified predictors were then integrated into a nomogram via multivariate Cox regression. Model performance was assessed using the C-index, ROC analysis, and calibration plots.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Over 29 months (IQR 24\u201335), 104(23.6%) patients met the primary composite endpoints, which included 62(14.1%) incident HF and 42(9.5%) all-cause death. After selecting variables through forward stepwise regression, age[HR 1.030,\n                      <jats:italic>P<\/jats:italic>\n                      = 0.016], growth differentiation factor 15(GDF-15) [HR 1.076,\n                      <jats:italic>P<\/jats:italic>\n                      &amp;lt; 0.001], lipoprotein(a)[Lp(a)] [HR = 1.011,\n                      <jats:italic>P<\/jats:italic>\n                      = 0.002], ferritin(FER) [HR = 1.009,\n                      <jats:italic>P<\/jats:italic>\n                      = 0.030], and EuroQol-5D (EQ-5D) score [HR 0.099,\n                      <jats:italic>P<\/jats:italic>\n                      = 0.015] were utilized to construct the nomogram. In the training cohort, the model achieved a C-index of 0.772. For temporal validation, time-dependent area under the ROC curve(AUC) values further confirmed its accuracy: 0.779 (95%CI, 0.696\u20130.863) at 24 months, 0.816(95%CI, 0.743\u20130.888) at 27 months, 0.836(95%CI, 0.768\u20130.903) at 32 months, and 0.850(95%CI, 0.771\u20130.929) at 36 months. Decision curve analysis (DCA) also demonstrated sustained clinical utility across a broad range of threshold probabilities.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study identified specific predictors of HF and all-cause mortality in CKD patients. This model has enhanced predictive ability for early identification of these risks, thereby facilitating timely clinical interventions to improve the prognosis of CKD patients.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmed.2026.1784717","type":"journal-article","created":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T05:50:37Z","timestamp":1778219437000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Development of a nomogram for predicting incident heart failure and all-cause mortality in patients with chronic kidney disease: a 3-year follow-up study"],"prefix":"10.3389","volume":"13","author":[{"given":"Yuxin","family":"Jiang","sequence":"first","affiliation":[{"name":"Department of Nephropathy, The First Affiliated Hospital of Anhui Medical University, Hefei","place":["Anhui, 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Anhui Medical University, Hefei","place":["Anhui, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Nephropathy, The First Affiliated Hospital of Anhui Medical University, Hefei","place":["Anhui, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Li","sequence":"additional","affiliation":[{"name":"Division of Life Sciences and Medicine, Department of Clinical Oncology, The First Affiliated Hospital of USTC, University of Science and Technology of China, Hefei","place":["Anhui, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Nephropathy, The First Affiliated Hospital of Anhui Medical University, Hefei","place":["Anhui, China"]},{"name":"Department of Biostatistics and Epidemiology, School of Public Health, Anhui Medical University, 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