Background: 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‐specific risk model and clinical implementation. Methods: The development set comprised 52 251 patients with CKD from the China Renal Data System (70% training; 30% internal validation). External validation used 21 798 patients from independent Chinese hospitals and 3323 UK Biobank participants. Outcome was 5‐year new‐onset HF. Five machine learning models were developed, with performance assessed via area under the curve and compared using DeLong test. The top‐performing extreme gradient boosting model was simplified via forward stepwise selection; feature importance quantified using Shapley additive explanations. Results: In the Chinese external validation cohort, the extreme gradient boosting model outperformed others (area under the curve, 0.879 [95% CI, 0.871–0.887]; DeLong P <0.05), with an area under the curve of 0.851 (95% CI, 0.831–0.870) 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 ΔC‐statistic of 0.050 (0.045–0.056) and 0.006 (−0.013–0.024) versus the Atherosclerosis Risk in Communities risk score, and 0.194 (0.183–0.204) and 0.145 (0.107–0.182) versus the Predicting Risk of Cardiovascular Disease Events Equation, respectively. A web‐based calculator was deployed ( https://clinician.shinyapps.io/HF_Risk_Predictor_for_CKD/ ). Conclusions: The 9‐variable extreme gradient boosting model tailored for patients with CKD may help predict HF risk in this high‐risk population. Validation across diverse populations is warranted to confirm its generalizability.

Objective: To develop and validate cluster-specific diagnostic models for osteoporosis in patients with chronic kidney disease (CKD) using routine clinical parameters. Methods: Data were obtained from the National Health and Nutrition Examination Survey. A total of 3,806 CKD patients were enrolled and randomly allocated to training (70%, n = 2,667) and internal validation (30%, n = 1,139) cohorts, with an additional external validation cohort from the China Renal Data System or Chinese Renal Disease Data System. K-means clustering was performed to identify distinct subgroups. Weighted univariate logistic regression, Least Absolute Shrinkage and Selection Operator and Boruta algorithm were used for feature selection. Final diagnostic models were constructed using weighted multivariable logistic regression. Model performance was assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA), and net reclassification improvement (NRI) and integrated discrimination improvement (IDI). Results: Three distinct CKD phenotypic clusters were identified; two were selected for model development. Group A retained six variables (sex, age, body mass index, serum uric acid, serum creatinine, and fracture history) with AUCs of 0.880, 0.839, and 0.815 across training, internal, and external (CRDS) validation. Group B retained seven variables (sex, age, marital status, red blood cell count, body mass index, race, and fracture history) with AUCs of 0.824, 0.806, and 0.808. Both models demonstrated favorable calibration and clinical utility. NRI revealed divergent subgroup effects: gains in Group B (0.0059) versus losses in Group A (-0.0149). Conclusions: Cluster-specific diagnostic models based on weighted logistic regression exhibited robust discriminative performance for osteoporosis diagnosis in CKD patients, with heterogeneous reclassification gains across phenotypic subgroups.