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Published on: July 15, 2019
Development and Validation of Diagnostic Models for Hand-Foot-and-Mouth Disease in Children
Feng Zhuo1, Mengjie Yu2, Qiang Chen3
1Pediatric Cardiology Center, Jiangxi Provincial Children's Hospital, Nanchang, Jiangxi 330006, China.
Objective:
To find risk markers and develop new clinical predictive models for the differential diagnosis of hand-foot-and-mouth disease (HFMD) with varying degrees of disease.
Methods:
19766 children with HFMD and 64 clinical indexes were included in this study. The patients included in this study were divided into the mild patients' group (mild) with 12292 cases, severe patients' group (severe) with 6508 cases, and severe patients with respiratory failure group (severe-RF) with 966 cases. Single-factor analysis was carried out on 64 indexes collected from patients when they were admitted to the hospital, and the indexes with statistical differences were selected as the prediction factors. Binary multivariate logistic regression analysis was used to construct the prediction models and calculate the adjusted odds ratio (OR).
Results:
SP, DP, NEUT#, NEUT%, RDW-SD, RDW-CV, GGT, CK/CK-MB, and Glu were risk markers in mild/severe, mild/severe-RF, and severe/severe-RF. Glu was a diagnostic marker for mild/severe-RF (AUROC = 0.80, 95% CI: 0.78-0.82); the predictive model constructed by temperature, SP, MOMO%, EO%, RDW-SD, GLB, CRP, Glu, BUN, and Cl could be used for the differential diagnosis of mild/severe (AUROC > 0.84); the predictive model constructed by SP, age, NEUT#, PCT, TBIL, GGT, Mb, β2MG, Glu, and Ca could be used for the differential diagnosis of severe/severe-RF (AUROC > 0.76).
Conclusion:
By analyzing clinical indicators, we have found the risk markers of HFMD and established suitable predictive models.
Insights
This study identified key risk markers and developed predictive models for diagnosing hand-foot-and-mouth disease (HFMD) severity in children. These models aid in differentiating mild, severe, and respiratory failure cases.
Area of Science:
- Pediatrics
- Infectious Diseases
- Clinical Diagnostics
Background:
- Hand-foot-and-mouth disease (HFMD) is a common childhood illness with varying clinical presentations.
- Accurate differential diagnosis is crucial for appropriate management and predicting disease outcomes.
- Existing diagnostic tools may lack specificity in distinguishing between different HFMD severity levels.
Purpose of the Study:
- To identify significant risk markers associated with different degrees of HFMD.
- To develop and validate clinical predictive models for the differential diagnosis of HFMD severity.
- To improve the early identification of severe HFMD and respiratory failure.
Main Methods:
- A retrospective analysis of 19,766 pediatric HFMD cases.
- Inclusion of 64 clinical indexes at hospital admission for analysis.
- Application of single-factor analysis and binary multivariate logistic regression to identify risk factors and construct predictive models.
Main Results:
- Several clinical indexes, including Glucose (Glu), were identified as risk markers across different HFMD severity groups (mild, severe, severe-RF).
- A predictive model incorporating temperature, SP, MOMO%, EO%, RDW-SD, GLB, CRP, Glu, BUN, and Cl demonstrated high accuracy (AUROC > 0.84) for mild/severe HFMD.
- Another model using SP, age, NEUT#, PCT, TBIL, GGT, Mb, β2MG, Glu, and Ca showed effectiveness (AUROC > 0.76) in differentiating severe/severe-RF cases.
Conclusions:
- Clinical indicators can effectively identify risk markers for HFMD.
- Validated predictive models are established for the differential diagnosis of HFMD, aiding in clinical decision-making.
- These models enhance the ability to distinguish between varying degrees of HFMD severity, including respiratory failure.
