R language software

Statistical analysis was conducted using the SPSS 25.0 software (IBM Corp., Armonk, NY, United States) and R language software (version 3.6.1, company, state, country). Patients were randomly divided into training (n = 356 cases) and validation (n = 152 cases) groups using R with a random sampling function. The univariate Cox regression analysis was performed to identify prognostic factors in the training group, and statistically significant variables (p < 0.05) were incorporated into a multivariate Cox risk regression model. Furthermore, the nomogram graph was constructed, with the C-index mapping the subject’s working receiver operating characteristic (ROC) curve and correction curve for decision curve analysis (DCA). In the correction curve, the closer the curve is to the ideal 45° guide, the closer the prediction is to the actual observation. The C-index and the area under the ROC curve (AUC) were used to evaluate the predicted value of the nomogram graph; the maximum C-index was 1 (representing 100% prediction accuracy), and the minimum value was 0.5 α. The closer the C-index was to 1, the better the accuracy of the prediction model. Using the DCA method, we could effectively determine the certainty and benefit of the prediction model. An α level of 0.05 was used.

Data entry and management were performed using Epidata software, version 3.1 (Epidata Association, Odense, Denmark). All statistical analyses were conducted with SPSS 22.0 (IBM, Armonk, NY, USA) and R language software (version 4.1.1). Continuous variables were expressed as the mean ± standard deviation, and categorical variables as frequencies (percentages). The chi-square test was used to compare categorical variables. The linear tendency was evaluated among several groups using trend test. The independent-sample t-test and one-way analysis of variance (ANOVA) were used to compare continuous variables among two or more groups. Multiple logistic regression analysis was used to assess the independence of the associations between obesity indicators and various abnormalities of peripheral arteries, and the odds ratio (OR) and 95% confidence interval (95% CI) was calculated. We also explored the nonlinear relationship between BMI and the risk of ABI ≤ 0.9 using a restricted cubic spline model by multivariable adjustment with three knots (at the 10th, 50th, and 90th percentiles). P < 0.05, which is two-sided, was considered significant.

Statistics were performed using the SPSS software (version 25.0, IBM Corp., Armonk, NY) and R language software (version 3.5.1, available at http://www.R-project.org). Normal distribution and equal variance were assessed by Kolmogorov–Smirnov and Levene tests. To evaluate inter-observer reliability for VOI drawing and radiomics analysis, intraclass correlation coefficient (ICC) test was performed. The “mRMRe”, “Glmnet”, and “pROC” packages were applied to execute mRMR algorithm, LASSO logistic regression, and ROC curves, respectively. AUCs of these models were compared by Delong test. P < 0.05 was considered statistically significant.