Table 3:
Model Performance
| Model | Validation Sample Size | AuROC (95% CI) | Best Cutoff | Prediction Category | Predicted Cases | True Success Rate | Accuracy | Sensitivity | Specificity |
|---|---|---|---|---|---|---|---|---|---|
| Baseline model | 142 | 0.65 (0.53, 0.77) | 0.17 | Predicted success (≥cutoff) | 67 | 23.9% | 0.58 | 0.64 | 0.56 |
| Predicted failure (<cutoff) | 75 | 12.0% | |||||||
| Week 6 model | 142 | 0.75 (0.64, 0.86) | 0.21 | Predicted success (≥cutoff) | 53 | 35.8% | 0.72 | 0.76 | 0.71 |
| Predicted failure (<cutoff) | 89 | 6.7% | |||||||
| Week 6 (HGB*ALB*VDZ Level)/(CRP*Weight) | 472 | 0.75 (0.70, 0.81) | 185.96 | Predicted success (≥cutoff) | 182 | 32.4% | 0.69 | 0.73 | 0.69 |
| Predicted failure (<cutoff) | 290 | 7.6% |
Number of predicted success is tp+fp; number of predicted failure is tn+fn; for predicted success, true success rate is tp/(tp+fp); for predicted failure, true success rate is fn/(tn+fn); accuracy is (tp+tn)/(tp+fp+tn+fn); sensitivity is tp/(tp+fn); specificity is tn/(tn+fp).