Table 4 Performance of DLM for predicting in-hospital mortality of patients with infectious diseases
From: Deep-learning model for screening sepsis using electrocardiography
Predictive model | AUC (95% CI) | SEN (95% CI) | SPE (95% CI) | PPV (95% CI) | NPV (95% CI) |
|---|---|---|---|---|---|
DLM using 12-lead ECG | 0.817 (0.793–0.840) | 0.785 (0.735–0.835) | 0.710 (0.697–0.724) | 0.137 (0.120–0.155) | 0.983 (0.978–0.987) |
DLM using 6-lead ECG | 0.815 (0.794–0.836) | 0.844 (0.799–0.888) | 0.682 (0.668–0.695) | 0.135 (0.118–0.152) | 0.987 (0.983–0.991) |
DLM using 1-lead ECG | 0.802 (0.780–0.825) | 0.945 (0.917–0.973) | 0.524 (0.509–0.539) | 0.105 (0.092–0.117) | 0.994 (0.991–0.997) |
SOFA score | 0.817 (0.786–0.847) | 0.734 (0.680–0.788) | 0.785 (0.773–0.797) | 0.167 (0.145–0.189) | 0.980 (0.976–0.985) |
Quick SOFA score | 0.797 (0.767–0.828) | 0.594 (0.534–0.654) | 0.885 (0.875–0.894) | 0.233 (0.200–0.265) | 0.974 (0.969–0.979) |
NEWS | 0.808 (0.777–0.839) | 0.738 (0.684–0.792) | 0.771 (0.759–0.784) | 0.160 (0.139–0.180) | 0.980 (0.976–0.985) |
MEWS | 0.778 (0.747–0.808) | 0.773 (0.722–0.825) | 0.670 (0.656–0.684) | 0.121 (0.105–0.137) | 0.981 (0.976–0.985) |
Lactate | 0.801 (0.758–0.844) | 0.646 (0.569–0.724) | 0.854 (0.829–0.879) | 0.466 (0.397–0.534) | 0.925 (0.905–0.944) |
WBC | 0.591 (0.552–0.630) | 0.504 (0.442–0.566) | 0.679 (0.665–0.694) | 0.086 (0.072–0.101) | 0.958 (0.951–0.965) |
C-reactive protein | 0.541 (0.499–0.583) | 0.300 (0.242–0.359) | 0.811 (0.799–0.823) | 0.084 (0.065–0.103) | 0.953 (0.946–0.960) |