Table 3. Performance measures of models for distinguishing patients with ICH.
| Statistical model used | Sensitivity(95% CI) | Specificity(95% CI) | PPV(95% CI) | NPV(95% CI) | AUC(95% CI) | Slope(95% CI) | Intercept(95% CI) |
| Model training | |||||||
| XGBoost (19 variables) | 53%(47.3% to 57.7%) | 92%(90.0% to 93.1%) | 65%(59.8% to 69.7%) | 87%(84.9% to 88.6%) | 0.849(0.828 to 0.870) | 0.929(0.908 to 0.951) | 0.030(0.018 to 0.042) |
| Logistic regression | 50%(44.7% to 55.1%) | 90%(87.7% to 91.1%) | 58%(53.0% to 63.2%) | 86%(83.9% to 87.8%) | 0.801(0.777 to 0.826) | 1.078(1.057 to 1.099) | –0.023(–0.033 to –0.012) |
| XGBoost (9 variables) | 51%(45.7% to 56.1%) | 91%(89.3% to 92.5%) | 62%(57.1% to 67.1%) | 86%(84.4% to 88.2%) | 0.828(0.805 to 0.850) | 0.946(0.926 to 0.965) | 0.016(0.006 to 0.027) |
| Optimism-corrected performance | |||||||
| XGBoost (19 variables) | 47%(42.0% to 51.1%) | 90%(88.6% to 91.7%) | 58%(53.6% to 62.9%) | 85%(83.7% to 86.9%) | 0.801(0.783 to 0.819) | 0.912(0.894 to 0.931) | 0.031(0.020 to 0.041) |
| Logistic regression | 49%(44.3% to 54.4%) | 89%(87.6% to 91.0%) | 57%(52.2% to 63.4%) | 86%(83.8% to 87.8%) | 0.796(0.770 to 0.822) | 1.061(1.039 to 1.082) | –0.019(–0.029 to –0.008) |
| XGBoost (9 variables) | 48%(42.8% to 53.0%) | 90%(88.6% to 91.8%) | 59%(53.9% to 64.1%) | 86%(83.7% to 87.4%) | 0.799(0.778 to 0.820) | 0.934(0.917 to 0.952) | 0.018(0.009 to 0.028) |
AUCarea under the curveICHintracerebral haemorrhageNPVnegative predictive valuePPVpositive predictive valueXGBoosteXtreme Gradient Boosting