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

Indian Journal of Pediatrics
Bin WangYuanxiao Li

Abstract

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. 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. 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. With advantages such as simp...Continue Reading

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