The prediction models based on (i) clinical features (C model), (ii) radiological features (R model), and (iii) the combination of clinical features and radiological features (CR model) were developed. ROC analyses for the primary and validation cohort are shown in Table 4 and Fig. 3. The CR model yielded a maximum AUC of 0.986 (95% CI 0.966~1.000) in the primary cohort with the highest accuracy and specificity, which was 0.936 (95% CI 0.866~1.000) in the validation cohort. The AUC for the C model was 0.952 (95% CI 0.988~0.915) and 0.967 (95% CI 0.919~1.000) in the primary and validation cohorts, respectively. For the R model, the AUC of the two cohorts was 0.969 (95% CI 0.940~0.997) and 0.809 (95% CI 0.669~0.948), respectively.
Table 4 Performance of the individualized prediction models
Primary cohort (n = 98) Validation cohort (n = 38)
Models AUC 95% CI Accuracy Specificity Sensitivity AUC 95% CI Accuracy Specificity Sensitivity
C model 0.952 0.915~0.988 0.888 0.894 0.882 0.967 0.919~1.000 0.868 0.859 0.842
R model 0.969 0.940~0.997 0.929 0.851 1.000 0.809 0.669~0.948 0.684 0.368 1.000
CR model 0.986 0.966~1.000 0.959 0.957 0.961 0.936 0.866~1.000 0.763 0.789 0.737
C, R, and CR indicate the predicted model based on clinical features, radiological features, and the combination of clinical features and clinical radiological features, respectively. CI confidence interval
Fig. 3 ROC of the three models in primary and validation cohort curves. Comparison of receiver operating characteristic (ROC) curves among the radiological mode (R model), clinical model (C model), and the combination of clinical and radiological model (CR model) for the diagnosis of COVID-19 in the primary (a) and validation (b) cohorts