Statistical analysis
CKD
REGRESSION, HYPERTENSION
As in our previous risk model for developing hypertension, we used a similar analytical methodUnivariable and multivariable logistic regression analyses were performed for each variable to estimate the odds ratio and 95% confidence interval for CKD incidence. To create a risk score that predicts 5-year incidence of CKD, the following scores corresponding to standardized beta coefficients were assigned to each risk factor category for items that were significant in the multivariable logistic regression analysis, based on the methodology used in the Japan Epidemiology Collaboration on Occupational Health Study Group’s study: 1, β = 0.01–0.20; 2, β = 0.21–0.80; 3, β = 0.81–1.20; 4, β = 1.21–2.20; and 5, β > 2.20The discriminative performance of the score was assessed using the area under the curve (AUC) from the receiver operating characteristic (ROC) analysis. The performance of each score was assessed using sensitivity, specificity, positive predictive value, negative predictive value, and the Youden indexTo compensate for the weakness of the study being derived from data from a single institution, a sensitivity analysis was performed using the bootstrap resample method. The 95% bootstrap confidence interval of the odds ratios in multivariable logistic regression analysis were calculated based on 2500 bootstrap resamples. We then used the β coefficients for the risk factors that were significant in the logistic regression analysis to create an equation that directly calculates the proportion of CKD after 5 years, and assessed its predictive ability and reproducibility in the same way as for the score. In developing the equation, continuous variables such as age and eGFR were used as continuous values without categorization. We evaluated the calibration using calibration plots.All statistical analyses were performed using JMP Pro version 14 (SAS Institute, Cary, NC, USA) for Windows. Statistical significance was set at P < 0.05.