This invention discloses a method for constructing a predictive model for
heart failure and all-cause mortality risk in patients with chronic
kidney disease (CKD). It relates to the field of model construction technology, and its key technical points are as follows: This invention uses G3a-G5 stage CKD patients with preserved
ejection fraction as the research subjects, establishes a prospective cohort, collects clinical indicators, biomarkers, and
quality of life scores, and uses new-onset
heart failure combined with all-cause mortality as the composite endpoint. After standardized follow-up and data preprocessing, univariate Cox regression, Lasso,
random forest, XGBoost combined with Venn diagrams are used to screen predictive factors. Age,
lipoprotein(a),
ferritin, GDF15, and EQ-5D
quality of life score are identified as independent predictors. A time-dependent
nomogram model is constructed, and multi-dimensional
efficacy validation is performed. This model is adapted to the specific pathophysiological state of CKD, with accurate predictions and good calibration. With accompanying online interactive tools, it can achieve individualized
risk assessment and stratified intervention for 24-36 months, solving the problems of poor applicability and inaccurate prediction in existing models, and has strong clinical applicability.