Establishment of Early Pregnancy Related Thyroid Hormone Models and Reference Intervals for Pregnant Women in China Based on Real World Data

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

Background

Copper and zinc are two trace elements that are essential to maintain normal pregnancy and fetal development. But only few research established specific reference intervals (RIs) for pregnant women. In this study, we aim to establish discrete RIs and next-generation RIs for copper and zinc during pregnancy by real-world data.

Method

We retrospectively collected 710 healthy pregnant women and 300 age-matched non-pregnant women attending the hospital and compared copper and zinc levels among them. Further, we analyzed multiple factors (gestational age, maternal age, and parity) that may affect copper and zinc during pregnancy by multivariate regression. Two types of reference intervals (RIs) of copper and zinc for pregnant women were established: discrete reference intervals (RIs) by non-parametric method and next-generation RIs by Generalized Additive Models for Location, Scale, and Shape (GAMLSS) model.

Result

Copper levels were higher (median: 1st trimester: 1203.00 μg/L, 2nd trimester: 1818.00 μg/L, 3rd trimester: 1795.00 μg/L) than in non-pregnant women (median: 900.00 μg/L, p＜0.001), whereas zinc levels were lower in pregnant women (median: 1st trimester: 836.00 μg/L, 2nd trimester: 639.00 μg/L, 3rd trimester: 618.00 μg/L) than in non-pregnant women (median: 767.00 μg/L, p＜0.001). Additionally, copper and zinc levels varied among trimesters. Moreover, copper and zinc were affected significantly by gestational age but maternal age only had a weak effect on them. Based on the effect of gestational age, we established discrete RIs partitioned by trimesters and next-generation RIs for copper and zinc respectively during pregnancy.

Conclusion

Taken together, this research elucidated the remarkable effect of gestational age on copper and zinc during pregnancy. The next-generation RIs visualized trends and subtle changes in copper and zinc levels during pregnancy. The discrepancy between discrete RIs and next-generation RIs suggested a more detailed continuous RIs could be considered for pregnant women.

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

Evidence derived from laboratory medicine plays a pivotal role in the diagnosis, treatment monitoring, and prognosis of various diseases. Reference intervals (RIs) are indispensable tools for assessing test results. The accuracy of clinical decision-making relies directly on the appropriateness of RIs. With the increase in real-world studies and advances in computational power, there has been increased interest in establishing RIs using big data. This approach has demonstrated cost-effectiveness and applicability across diverse scenarios, thereby enhancing the overall suitability of the RI to a certain extent. However, challenges persist when tests results are influenced by age and sex. Reliance on a single RI or a grouping of RIs based on age and sex can lead to erroneous interpretation of results with significant implications for clinical decision-making. To address this issue, the development of next generation of reference interval models has arisen at an historic moment. Such models establish a curve relationship to derive continuously changing reference intervals for test results across different age and sex categories. By automatically selecting appropriate RIs based on the age and sex of patients during result interpretation, this approach facilitates clinical decision-making and enhances disease diagnosis/treatment as well as health management practices. Development of next-generation reference interval models use direct or indirect sampling techniques to select reference individuals and then employed curve fitting methods such as splines, polynomial regression and others to establish continuous models. In light of these studies, several observations can be made: Firstly, to date, limited interest has been shown in developing next-generation reference interval models, with only a few models currently available. Secondly, there are a wide range of methods and algorithms for constructing such models, and their diversity may lead to confusion. Thirdly, the process of constructing next-generation reference interval models can be complex, particularly when employing indirect sampling techniques. At present, normative documents pertaining to the development of next-generation reference interval models are lacking. In summary, this review aims to provide an overview of the current state of development of next-generation reference interval models by defining them, highlighting inherent advantages, and addressing existing challenges. It also describes the process, advanced algorithms for model building, the tools required and the diagnosis and validation of models. Additionally, a discussion on the prospects of utilizing big data for developing next-generation reference interval models is presented. The ultimate objective is to equip clinical laboratories with the theoretical framework and practical tools necessary for developing and optimizing next-generation reference interval models to establish next-generation reference intervals while enhancing the use of medical data resources to facilitate precision medicine.

Current status and challenges in establishing reference intervals based on real-world data

Reference intervals (RIs) are the cornerstone for evaluation of test results in clinical practice and are invaluable in judging patient health and making clinical decisions. Establishing RIs based on clinical laboratory data is a branch of real-world data mining research. Compared to the traditional direct method, this indirect approach is highly practical, widely applicable, and low-cost. Improving the accuracy of RIs requires not only the collection of sufficient data and the use of correct statistical methods, but also proper stratification of heterogeneous subpopulations. This includes the establishment of age-specific RIs and taking into account other characteristics of reference individuals. Although there are many studies on establishing RIs by indirect methods, it is still very difficult for laboratories to select appropriate statistical methods due to the lack of formal guidelines. This review describes the application of real-world data and an approach for establishing indirect reference intervals (iRIs). We summarize the processes for establishing iRIs using real-world data and analyze the principle and applicable scope of the indirect method model in detail. Moreover, we compare different methods for constructing growth curves to establish age-specific RIs, in hopes of providing laboratories with a reference for establishing specific iRIs and giving new insight into clinical laboratory RI research. (201 words).