International Journal of Molecular Medical Science, 2025, Vol.15, No.2, 54-68 http://medscipublisher.com/index.php/ijmms 61 Table 3 PROBAST results included in the study Author (Year) Type of study Bias risk Applicability Totality Object of study Result Analysis Object of study Predictive factor Result Bias risk Applicability Sharathkumar et al. (2012) B + ？ ？ - + + + - + Yen et al. (2016) B + ？ ？ + + + + ? + Cairo et al. (2018) B + ？ ？ - + + + - + Marquez et al. (2016) B + ？ ？ + + + + ? + Kerris et al. (2020) B + ？ ？ - + + + - + Jaffray et al. (2021) B + ？ ？ + + + + ? + Kerlin et al. (2015) B + ？ ？ - + + + - + Papillon et al. (2023) B + ？ ？ + + + + ? + Spavor et al. (2016) B + ？ ？ - + + + - + Walker et al. (2021) B + ？ ？ + + + + ? + Jaffray et al. (2022) B + ？ ？ + + + + ? + Connelly et al. (2016) B + ？ ？ - + + + - + Tiratrakoonseree et al. (2024) B + ？ ？ - + + + - + Note: PROBAST, Prediction model Risk Of Bias Assessment Tool; A stands for “development only”, and B stands for “development and validation in the same study”; “+” indicates low risk of bias/low applicability concern, “-” Indicates high risk of bias/high applicability concern, and “?” Indicates unclear bias risk/unclear application 3.5 Includes meta-analysis to validate the model in the overview Including 13 studies in the review, due to inadequate reporting of model details and the absence of 95% confidence intervals in some studies, only 10 studies were included in the meta-analysis (Figure 3). The combined AUC was calculated using a random effects model, with a result of 0.84 (95% confidence interval: 0.80~0.88). The I² value was 96% (P<0.01). Due to the high heterogeneity in the results of meta-analysis, this study further explored the sources of heterogeneity by means of two methods of retention cross-validation and subgroup analysis (Figure 4; Figure 5; Figure 6; Figure 7). Firstly, the residual one method is used to carry out the analysis. With the help of the “metainf” function of the “meta” package in R software, we carried out a one-by-one deletion operation on the included documents to identify whether one or several documents had a significant impact on the overall heterogeneity. However, we did not find a clear cause of heterogeneity after completing the residual cross-validation. Based on this, we decided to conduct a follow-up subgroup analysis. 3.6 Subgroup analysis In the study on the risk prediction model of VTE in children, subgroup analysis was conducted according to the study population and the number of predictors of the risk prediction model (Figure 5; Figure 6; Figure 7). In this study, we analyzed the subgroup data and heterogeneity of the pediatric medical group, pediatric surgery group, and ICU group, and found that there were differences in the efficacy and heterogeneity of the model in each subgroup.
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