Table 3.
Prediction performance of the models in the control arm (N=107)
| Models | AUC (95% CI) | P* | Cut-off points (95% CI) |
NRI (95% CI) | P# | Accuracy (95% CI) |
Sensitivity (95% CI) |
Specificity (95% CI) |
PPV (95% CI) | NPV (95% CI) |
|---|---|---|---|---|---|---|---|---|---|---|
| fHb ratio | 0.70(0.59-0.80) | - | 2.03(1.96-2.33) | - | - | 0.71(0.58-0.80) | 0.73(0.40-0.88) | 0.69(0.47-0.92) | 0.81(0.70-0.93) | 0.59(0.43-0.76) |
| NGAL (ng/mg) | 0.60(0.43-0.73) | 0.249 | 195.96(26.16-1588.61) | 0.66(0.30-1.03)$ | p<0.001$ | 0.64(0.46-0.76) | 0.67(0.21-0.97) | 0.61(0.25-0.97) | 0.77(0.65-0.94) | 0.53(0.32-0.80) |
| NAG (mU/mg) | 0.52(0.40-0.65) | 0.033 | 45.25(10.77-448.83) | 0.69(0.33-1.05)$ | p<0.001$ | 0.58(0.40-0.73) | 0.55(0.11-0.98) | 0.63(0.10-1.00) | 0.77(0.61-1.00) | 0.49(0.32-0.86) |
| KIM-1 (ng/mg) | 0.56(0.43-0.68) | 0.098 | 0.49(0.20-1.25) | 0.34(−0.05-0.72)$ | P=0.089$ | 0.62(0.43-0.74) | 0.63(0.17-0.97) | 0.55(0.15-0.98) | 0.74(0.63-0.95) | 0.51(0.37-0.82) |
| Model A | 0.74(0.64-0.83) | 0.012 | 0.64(0.27-1.01) | p<0.001 | 0.74(0.62-0.83) | 0.76(0.47-0.95) | 0.70(0.44-0.94) | 0.83(0.72-0.95) | 0.64(0.45-0.85) | |
| Model B (Reference) | 0.63(0.51-0.74) | - | - | - | 0.63(0.48-0.75) | 0.60(0.22-0.93) | 0.69(0.30-0.98) | 0.80(0.66-0.97) | 0.51(0.36-0.72) |
Model A: fHb ratio+NGAL+NAG+KIM-1; Model B: NGAL+NAG+KIM-1; NAG, NGAL and KIM-1 were normalized with urinary creatinine concentration. We calculated *p value with “roc.test” function using “bootstrap” method with 2,000 replicates in “pROC” package.
We calculated cut-off points and 95%CI to achieve the maximal Youden’s index, using bootstrap-based method in R package “cutpointr” with 1,000 replicates. We used continuous NRI and its p# value using “improveProb” function in R package “Hmisc”.
indicating NRI and its p-value of fHb ratio when compared with NGAL, NAG, and KIM-1, respectively. We computed AUC and all the confusion matrix results using bootstrap-based method with 1,000 resamples. Accuracy is defined as the number of correct predictions divided by total number of predictions, which can be calculated as follows: Accuracy=. fHb, free hemoglobin; NAG, N-acetyl-β-D-glucosaminidase; NGAL, neutrophil gelatinase-associated lipocalin; KIM-1, kidney injury molecule-1. AUC, area under the receiver operating characteristic curve; CI, confidence interval; PPV, positive predictive value; NPV, negative predictive value; NRI, net reclassification index.