International Journal of Molecular Medical Science, 2025, Vol.15, No.2, 54-68 http://medscipublisher.com/index.php/ijmms 63 Figure 7 The forest map of different number of predictors was analyzed The pooled AUC of the pediatric internal medicine group was 0.82, and the prediction efficiency was above average, but the heterogeneity was extremely high. This is mainly due to the variety of variables selected and the unadjusted age stratification effect. Subsequent model optimization needs to strengthen the standardization of core variables. The pooled AUC of the pediatric surgery group was 0.92, with excellent prediction efficiency and low heterogeneity. Due to the concentration of patient risk factors and high consistency in the selection of model variables, the application value of targeted models in the perioperative period was highlighted. The pooled AUC of the ICU group was 0.81, which showed moderate prediction efficiency and very low heterogeneity. However, the AUC was relatively low, suggesting the need to integrate dynamic biomarkers to improve prediction accuracy. In addition, the study also found that the model with more than 5 predictive factors, because the information covered is more comprehensive, the various factors can complement and cooperate, so the model efficiency is significantly higher; On the contrary, models with predictive factors less than 5 lack consistency due to the large differences in factor selection and data samples among different studies, which leads to greater heterogeneity. 4 Discussion Childhood VTE risk prediction model is a tool to evaluate the possibility of VTE in children by collecting various relevant data of children and using statistical analysis. Building on comprehensive and accurate predictors and using the tool effectively can significantly reduce the risk of VTE in children. This study finds that in the process of developing risk prediction models, most studies have a large gap in the inclusion of predictive factors. As mentioned earlier, CVC is the most common predictive factor in children's VTE risk prediction models, but was not used in the study by Tiratrakoonseree et al. (2024). The model developed in this study reported an AUC value of 0.809, indicating high performance.This suggests that even without the inclusion of the typically prominent CVC factor, the model was still able to achieve a good level of discriminatory power in predicting VTE risk among children. It was noted that risk prediction models that are applicable to Western populations may not be applicable to Thailand, as significant risk factors may differ between populations (Milford et al., 2020). Previous studies on children's VTE risk prediction models have mainly focused on North America, particularly the United States. Due to limited resources and low usage of CVC in countries like Thailand, this predictive factor is not significant in
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