Model 1
   
Model 2
  

Variable

Exp(B)

95% CI

p

Exp(B)

95% CI

p


Intercept

0.003

0.000; 0.022

<.001

0.005

0.000; 0.050

<.001

Male sex

4.218

1.403; 14.207

0.014

4.167

1.050; 20.178

0.05

Age (y)  50

1.111

1.047; 1.194

0.002

1.083

1.011; 1.177

0.037

CKD Stage 4 vs. 3

3.290

1.068; 10.773

0.041

N/A

N/A

N/A

Carotid plaque

6.131

1.605; 27.983

0.011

17.387

2.750;175.88

0.006

Cutoff point
      
((FEP/FGF23) < 1/3.9)

3.915

1.346; 12.364

0.015

6.873

1.703; 35.999

0.011

 Exp (B) for the intercept measures the estimated odds of KI > 5 for the reference or zero values of the explanatory variables in the model. Exp (B) of the predictor measures the odds ratio (B) associated to the variable category or 1 unit change depending of the nature of the variable. The table provides with Exp (B) and 95% confidence intervals (CI) for variables with a statistically significant (p < 0.05) contribution to explain the magnitude of abdominal aortic calcification (AAC) in a multivariate logistic regression model comparing KI > 5 vs. KI = 0 for all patients (Model 1), or among patients with an estimated GFR below 30 ml/min (Model 2). The ratio FEP/FGF23 was introduced as a binary variable with a cutoff point of (FEP/FGF23) < (1/3.9) (or equivalently, log_{2}(FEP/FGF23) < log_{2}(1/3.9)). The ROC curve in Figure 2 shows the high sensitivity and specificity of the logistic regression analysis in Model 1 (Area under the ROC curve = 0.89 and good model calibration, as measured by HosmerLemenshow goodnessoffit test p = 0.95). For Model 2, the area under the ROC curve = 0.899 and HL test p = 0.55.