Establishment of Reference Interval and Aging Model of Homocysteine Using 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.

Effects of Using Different Indirect Techniques on the Calculation of Reference Intervals: Observational Study

BACKGROUND

Reference intervals (RIs) play an important role in clinical decision-making. However, due to the time, labor, and financial costs involved in establishing RIs using direct means, the use of indirect methods, based on big data previously obtained from clinical laboratories, is getting increasing attention. Different indirect techniques combined with different data transformation methods and outlier removal might cause differences in the calculation of RIs. However, there are few systematic evaluations of this.

OBJECTIVE

This study used data derived from direct methods as reference standards and evaluated the accuracy of combinations of different data transformation, outlier removal, and indirect techniques in establishing complete blood count (CBC) RIs for large-scale data.

METHODS

The CBC data of populations aged ≥18 years undergoing physical examination from January 2010 to December 2011 were retrieved from the First Affiliated Hospital of China Medical University in northern China. After exclusion of repeated individuals, we performed parametric, nonparametric, Hoffmann, Bhattacharya, and truncation points and Kolmogorov-Smirnov distance (kosmic) indirect methods, combined with log or BoxCox transformation, and Reed-Dixon, Tukey, and iterative mean (3SD) outlier removal methods in order to derive the RIs of 8 CBC parameters and compared the results with those directly and previously established. Furthermore, bias ratios (BRs) were calculated to assess which combination of indirect technique, data transformation pattern, and outlier removal method is preferrable.

RESULTS

Raw data showed that the degrees of skewness of the white blood cell (WBC) count, platelet (PLT) count, mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), and mean corpuscular volume (MCV) were much more obvious than those of other CBC parameters. After log or BoxCox transformation combined with Tukey or iterative mean (3SD) processing, the distribution types of these data were close to Gaussian distribution. Tukey-based outlier removal yielded the maximum number of outliers. The lower-limit bias of WBC (male), PLT (male), hemoglobin (HGB; male), MCH (male/female), and MCV (female) was greater than that of the corresponding upper limit for more than half of 30 indirect methods. Computational indirect choices of CBC parameters for males and females were inconsistent. The RIs of MCHC established by the direct method for females were narrow. For this, the kosmic method was markedly superior, which contrasted with the RI calculation of CBC parameters with high |BR| qualification rates for males. Among the top 10 methodologies for the WBC count, PLT count, HGB, MCV, and MCHC with a high-BR qualification rate among males, the Bhattacharya, Hoffmann, and parametric methods were superior to the other 2 indirect methods.

CONCLUSIONS

Compared to results derived by the direct method, outlier removal methods and indirect techniques markedly influence the final RIs, whereas data transformation has negligible effects, except for obviously skewed data. Specifically, the outlier removal efficiency of Tukey and iterative mean (3SD) methods is almost equivalent. Furthermore, the choice of indirect techniques depends more on the characteristics of the studied analyte itself. This study provides scientific evidence for clinical laboratories to use their previous data sets to establish RIs.