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 (, 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 (); 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 (). Conclusion. By analyzing clinical indicators, we have found the risk markers of HFMD and established suitable predictive models.

中文翻译：

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儿童手足口病诊断模型的开发和验证

客观。寻找风险标记并开发新的临床预测模型，用于不同程度手足口病（HFMD）的鉴别诊断。方法。本研究共纳入19766名手足口病患儿，共64项临床指标。本研究纳入的患者分为轻度患者组（mild）12292例，重症患者组（severe）6508例，重症呼吸衰竭组（severe-RF）966例。对患者入院时收集的64项指标进行单因素分析，选取有统计学差异的指标作为预测因素。使用二元多元逻辑回归分析构建预测模型并计算调整后的优势比（OR）。结果。SP、DP、NEUT#、NEUT%、RDW-SD、RDW-CV、GGT、CK/CK-MB 和 Glu 是轻度/重度、轻度/重度 RF 和重度/重度 RF 的风险标记。Glu 是轻度/重度 RF 的诊断标记物（, 95% CI: 0.78-0.82); 由温度、SP、MOMO%、EO%、RDW-SD、GLB、CRP、Glu、BUN、Cl构建的预测模型可用于轻/重度的鉴别诊断（）；SP、年龄、NEUT#、PCT、TBIL、GGT、Mb、 β2MG 、Glu、Ca构建的预测模型可用于重度/重度RF的鉴别诊断（）。 结论。通过分析临床指标，我们找到了手足口病的危险标志物，并建立了合适的预测模型。