Table 2.
Results of the binary logistic regression analysis applied on urinary albumin, GM2AP and transferrin; age, BMI and eGFR CKD-EPI. BMI: body mass index; CKD-EPI: Chronic Kidney Disease Epidemiology Collaboration; eGFR: estimated glomerular filtration rate; GM2AP: GM2 ganglioside activator protein; −2LL: −2 log likelihood; R: correlation coefficient; SEM: standard error of the mean.
| Parameter | B (Mean ± SEM) | Wald | p-Value |
|---|---|---|---|
| Constant | −2.53 ± 0.40 | 40.60 | <0.001 |
| Urinary albumin | 0.11 ± 0.02 | 20.76 | <0.001 |
| Sensitivity: 95.4%; Specificity: 52.2%; Total percentage: 86.4% | |||
| Model summary: −2LL: 82.32; Cox and Snell’s R2: 0.24; Nagelkerke’s R2: 0.38 | |||
| Variables discarded by the model: Transferrin (p-value = 0.295) GM2AP (p-value = 0.051) Age (p-value = 0.913) BMI (p-value = 0.444) eGFR CKD-EPI (p-value = 0.736) | |||
The biomarker with the highest predictive capacity in this model was urinary albumin (p < 0.001). In this model, the predictive capacity is high (86.4%), and the inclusion of another second biomarker does not provide any significant improvement over it probably due to being redundant or collinear with urinary albumin. This fact was subsequently verified when baseline urinary excretion of urinary albumin was individually correlated for each patient with the maximum plasma creatinine that was shown after CM administration (Figure 3).