Figure legends:
FIGURE 1. Instructions for enrolling in the training cohort and
validation cohort cases.
FIGURE 2. A: Lasso regression coefficient path diagram; B:
Lasso regression cross-validation curve. Three predictors with non-zero
coefficients were obtained by Lasso regression (screening lambda by
10-fold cross-validation, based on lambda.1se, i.e., the maximum lambda
corresponding to an error mean within one standard deviation of the
minimum): age, CRP natural log-transformed value (InCRP), Monocytes
percentage (%Mon).
FIGURE 3. A: Nomogram of the pneumonia risk model; B-G: ROC
curves (Bootstrap=500 times), calibration curves, and DCA curves of the
pneumonia risk model in the training and validation cohorts. The ROC
curves show good discrimination of the pneumonia risk model in both the
training and validation cohorts. The calibration curves showed that the
pneumonia risk model has good calibration accuracy. The decision curve
analysis showed that the pneumonia risk model has high clinical value in
predicting the probability of pneumonia in SARS-CoV-2 infected patients.
FIGURE 4. Comparison of the models in the whole study cohort.
A. Receiver operator characteristic curves of the models are presented
to compare their discriminatory accuracy for predicting pneumonia risk.
P values show the AUC for the pneumonia risk model versus the AUCs for
predictors incorporated in the model alone; the predictive ability of
the predictors in the model individually and the overall predictive
power of the pneumonia risk model are contrasted via IDI.
B. Decision curve analyses comparing the net benefit of the nomogram of
the pneumonia risk model versus the other variables incorporated in the
nomogram alone are shown. AUC: area under the curve; CI: confidence
interval; IDI: Integrated discrimination improvement.
FIGURE 5. Correlation between the predicted probability of
pneumonia risk and pneumonia grade(A: Training cohort; B: Validation
cohort). A positive linear correlation was found between the predicted
pneumonia probability of the pneumonia risk model and pneumonia grade
using GAM.