Variable selection and nomogram construction
REGRESSION, BONE METASTASES
We randomly partitioned the dataset into training and validation cohorts using R programming language. According to clinical significance, we screened a total of 33 prognostic-related variables in the first step, which consisted of 10 categorical variables and 23 continuous variables. The optimal cut-off values for these continuous variables were then determined using X-tile software (Version 3.6.1, Yale University), following which they were converted into categorical variables. The cut-off values obtained from the X-tile analysis were utilized to stratify patients into high-risk and low-risk groups based on the values of age, LDH, ALP, GGT, TP, ALB, GLB, GLU, BUN, CREAT, UA, CA, Pi, WBC, RBC, HGB, HCT, MCV, MCH, MCHC, PLT, LY%, MO%, and NE%. The corresponding cut-off values were as follows: 58 years, 357 U/L, 349 U/L, 125 U/L, 79.5 g/L, 37.09 g/L, 28.09 g/L, 5.09 mmol/L, 5.92 mmol/L, 67.99 μmol/L, 357 μmol/L, 2.34 mmol/L, 1.31 mmol/L, 10.3×10Cox proportional hazards regression analysis was performed using SPSS software to calculate the hazard ratio (HR) and 95% confidence interval (CI) for factors associated with OS. First, the variables with Based on the Cox regression model, we constructed a nomogram for the prediction of 1-, 3- and 5-year OS rates in BC patients with bone metastases by the package of 