Developing a Nomogram for Risk Prediction of Severe Hand-Foot-and-Mouth Disease in Children

Objective: Early recognition of children with severe Hand-Foot-and-Mouth disease (HFMD) is especially important, as severe cases are associated with poor prognosis. To accomplish this, authors designed a quantitative assessment tool to build a nomogram to assist in clinical diagnosis.

Methods: A total of 2332 HFMD patients were enrolled in this study; 1750 cases in the mild group and 582 cases in the severe group. Analysis of all of the data was performed using R software version 3.4.3. Multivariate logistic regression was utilized to screen predictors to construct a nomogram model. Finally, predictive performance of the model was evaluated using a receiver operating characteristic (ROC) curve and classifier calibration plot.

Results: A nomogram was constructed with five variables: age, peak temperature, fever duration, pathogen, and vomiting. For the nomogram, the area under the curve was 0.87, and the model prediction accuracy rate was 85.2%. Depending upon the comparison of the area under the ROC curve, the nomogram model was superior to the traditional pediatric clinical illness score (PCIS). With the help of the Hosmer-Lemeshow test and resampling model calibration curve, the fitting performance of the nomogram was stable.

Conclusions: With advantages such as simplicity, intuitiveness, and practicality, the nomogram (including age, peak temperature, fever duration, pathogen, and vomiting) is capable of predicting severe HFMD and has certain auxiliary value in clinical applications.