Development of next-generation reference interval models to establish reference intervals based on medical data: current status, algorithms and future consideration

Ma, Chaochao; Yu, Zheng; Qiu, Ling

doi:10.1080/10408363.2023.2291379

Cited by 2 publications

(1 citation statement)

References 93 publications

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“…Conversely, next-generation RIs, or continuous RIs, enable a more detailed and more sensitive depiction of changes in analytes than discrete RIs, particularly age-dependent changes. Additionally, next-generation RIs have been widely used in recent years and are highly advantageous for various analytes [ [7] , [8] , [9] , [10] , [11] ] [ [7] , [8] , [9] , [10] , [11] ]. Among algorithms for estimating next-generation RIs, Generalized Additive Models for Location, Scale, and Shape (GAMLSS) employs parameters of the distribution (location, scale, skewness and kurtosis) to develop models and then estimate next-generation RIs [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Establishment of discrete reference interval and next-generation reference interval for copper and zinc during pregnancy using real-world data

Guan,

Ma,

Lin

et al. 2024

Heliyon

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Section: Introductionmentioning

confidence: 99%

Establishment of discrete reference interval and next-generation reference interval for copper and zinc during pregnancy using real-world data

Guan,

Ma,

Lin

et al. 2024

Heliyon

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Automated sex and age partitioning for the estimation of reference intervals using a regression tree model

Klawitter,

Böhm,

Tolios

et al. 2024

Journal of Laboratory Medicine

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Objectives Reference intervals (RI) play a decisive role in the interpretation of medical laboratory results. An important step in the determination of RI is age- and sex specific partitioning, which is usually based on an empirical approach by graphical representation. In this study, we evaluate an automated machine learning approach. Methods This study uses pediatric data from the CALIPER RI (Canadian laboratory initiative on pediatric reference intervals) study. The calculation of potential partitions is carried out using a regression tree model included in the rpart package of the statistical programming language R. The Harris & Boyd method is used to compare the corresponding partitions suggested by rpart and CALIPER. For better comparability, the reference ranges of the partitions of both approaches are then calculated using reflimR. Results Most of the partitions suggested by rpart or CALIPER show sufficient heterogeneity among themselves to justify age- and/or sex-specific RI partitioning. With only few individual exceptions, both methods yield comparable results. The partitions of both approaches for albumin and γ-glutamyltransferase are very similar to each other. For creatinine rpart suggests a slightly earlier distinction between the sexes. Alkaline phosphatase shows the most pronounced differences. In addition to a considerable earlier sex split, rpart suggests different age intervals for both sexes, resulting in three partitions for females and four partitions for males. Conclusions Our findings indicate that the automated analysis provided by rpart yields results that comparable to traditional methods. Nevertheless, the medical plausibility of the automatic suggestions needs to be validated by human experts.

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Development of next-generation reference interval models to establish reference intervals based on medical data: current status, algorithms and future consideration

Cited by 2 publications

References 93 publications

Establishment of discrete reference interval and next-generation reference interval for copper and zinc during pregnancy using real-world data

Establishment of discrete reference interval and next-generation reference interval for copper and zinc during pregnancy using real-world data

Automated sex and age partitioning for the estimation of reference intervals using a regression tree model

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