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Войти в исследования Disease markers · 2021
Development and Validation of Diagnostic Models for Hand-Foot-and-Mouth Disease in Children.
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Zhuo Feng, Yu Mengjie, Chen Qiang, Li Nuoya, Luo Li, Hu Meiying, Dong Qi, Hong Liang, Zhang Shouhua, Tao Qiang
Аннотация
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.
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.