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.

BACKGROUND

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.

METHODS

We prospectively recruited 440 patients with CKD stages 3-5 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.

RESULTS

Over 29 months (IQR 24-35), 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, P = 0.016], growth differentiation factor 15(GDF-15) [HR 1.076, P < 0.001], lipoprotein(a)[Lp(a)] [HR = 1.011, P = 0.002], ferritin(FER) [HR = 1.009, P = 0.030], and EuroQol-5D (EQ-5D) score [HR 0.099, P = 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, 0.696-0.863) at 24 months, 0.816(95%CI, 0.743-0.888) at 27 months, 0.836(95%CI, 0.768-0.903) at 32 months, and 0.850(95%CI, 0.771-0.929) at 36 months. Decision curve analysis (DCA) also demonstrated sustained clinical utility across a broad range of threshold probabilities.

CONCLUSION

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.