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Background: How to assess the importance of predictors in systematic reviews (SR) of prediction models remains largely unknown. The commonly used indicators of importance for predictors in individual models include parameter estimates, information entropy, etc., but they cannot be quantitatively synthesized through meta-analysis., Methods: We explored the synthesis method of the importance indicators in a simulation study, which mainly solved the following four methodological issues: (1) whether to synthesize the original values of the importance indicators or the importance ranks; (2) whether to normalize the importance ranks to a same dimension; (3) whether and how to impute the missing values in importance ranks; and (4) whether to weight the importance indicators according to the sample size of the model during synthesis. Then we used an empirical SR to illustrate the feasibility and validity of the synthesis method., Results: According to the simulation experiments, we found that ranking or normalizing the values of the importance indicators had little impact on the synthesis results, while imputation of missing values in the importance ranks had a great impact on the synthesis results due to the incorporation of variable frequency. Moreover, the results of means and weighted means of the importance indicators were similar. In consideration of accuracy and interpretability, synthesis of the normalized importance ranks by weighted mean was recommended. The synthesis method was used in the SR of prediction models for acute kidney injury. The importance assessment results were approved by experienced nephrologists, which further verified the reliability of the synthesis method., Conclusions: An importance assessment of predictors should be included in SR of prediction models, using the weighted mean of importance ranks normalized to a same dimension in different models., Competing Interests: Declarations. Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)

Establishing reference intervals (RIs) for pediatric patients is crucial in clinical decision-making, and there is a critical gap of pediatric RIs in China. However, the direct sampling technique for establishing RIs is resource-intensive and ethically challenging. Indirect estimation methods, such as unsupervised clustering algorithms, have emerged as potential alternatives for predicting reference intervals. This study introduces deep graph clustering methods into indirect estimation of pediatric reference intervals. Specifically, we propose a Density Graph Deep Embedded Clustering (DGDEC) algorithm, which incorporates a density feature extractor to enhance sample representation and provides additional perspectives for distinguishing different levels of health status among populations. Additionally, we construct an adjacency matrix by computing the similarity between samples after feature enhancement. The DGDEC algorithm leverages the adjacency matrix to capture the interrelationships between patients and divides patients into different groups, thereby estimating reference intervals for the potential healthy population. The experimental results demonstrate that when compared to other indirect estimation techniques, our method ensures the predicted pediatric reference intervals in different age and gender groups are closer to the true values while maintaining good generalization performance. Additionally, through ablation experiments, our study confirms that the similarity between patients and the multi-scale density features of samples can effectively describe the potential health status of patients., Competing Interests: Declaration of competing interest Authors state no conflict of interest., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Background: Continuous reference intervals (RIs) allow for more precise consideration of the dynamic changes of physiological development, which can provide new strategies for the presentation of laboratory test results. Our study aimed to establish continuous RIs using four different simulation methods so that the applicability of different methods could be further understood., Methods: The data of alkaline phosphatase (ALP) and serum creatinine (Cr) were obtained from the Pediatric Reference Interval in China study (PRINCE), in which healthy children aged 0-19 years were recruited. The improved non-parametric method, the radial smoothing method, the General Additive Model for Location Scale and Shape (GAMLSS), and Lambda-Median-Sigma (LMS) were used to develop continuous RIs. The accuracy and goodness of fit of the continuous RIs were evaluated based on the out of range (OOR) and Akaike Information Criterion (AIC) results., Results: Samples from 11,517 and 11,544 participants were used to estimate the continuous RIs of ALP and Cr, respectively. Time frames were partitioned to fulfill the following two criteria: sample size = 120 in each subgroup and mean difference = 2 between adjacent time frames. Cubic spline or penalized spline was used for curve smoothing. The RIs estimated by the four methods approximately overlapped. However, more obvious edge effects were shown in the curves fit by the non-parametric methods than the semi-parametric method, which may be attributed to insufficient sample size. The OOR values of all four methods were smaller than 10%., Conclusions: All four methods could be used to establish continuous RIs. GAMLSS and LMS are more reliable than the other two methods for dealing with edge effects.