Reference intervals for serum cystatin C in neonates and children 30 days to 18 years old

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.

Determination of cystatin C reference interval for children in Croatia

Introduction

Cystatin C is considered an early marker of kidney damage. The aim was to determine the reference interval in children since this information was not available from the test manufacturer.

Materials and methods

Included were children aged 0 to 18 years undergoing routine check without history of any renal disease. Cystatin C was measured by the immunoturbidimetric method, and creatinine by the enzymatic method on a Cobas c501 analyzer (Roche Diagnostics, Manheim, Germany). Reference intervals were determined according to the CLSI C28-A3 guidelines using a robust method and a nonparametric percentile method, depending on the sample size. The Schwartz's formula was applied to estimate glomerular filtration (eGFR) from cystatin C.

Results

The cystatin C reference interval for children aged 1-18 years (N = 204, median 8 years) was from 0.61 mg/L (90% CI: 0.53 to 0.64) to 1.08 mg/L (90% CI: 1.07 to 1.14). Differences according to sex were not found. For children aged 0-1 years (N = 29, median 5 months), the reference interval was from 0.60 mg/L (90% CI: 0.48 to 0.72) to 1.49 mg/L (90% CI: 1.36 to 1.61). The sample size was too small to test the difference according to sex. The eGFR was 76 (70-88) mL/min/1.73m2 for males and 83 (74-92) mL/min/1.73m2 for females.

Conclusion

The cystatin C reference intervals for Croatian pediatric population according to age were determined. The cystatin C concentrations in children reach adulthood values after the first year. The cystatin C Schwartz's formula is applicable for eGFR calculation in children.

Reference intervals: past, present, and future

Clinical laboratory test results alone are of little value in diagnosing, treating, and monitoring health conditions; as such, a clinically actionable cutoff or reference interval is required to provide context for result interpretation. Healthcare practitioners base their diagnoses, follow-up treatments, and subsequent testing on these reference points. However, they may not be aware of inherent limitations related to the definition and derivation of reference intervals. Laboratorians are responsible for providing the reference intervals they report with results. Yet, the establishment and verification of reference intervals using conventional direct methods are complicated by resource constraints or unique patient demographics. To facilitate standardized reference interval best practices, multiple global scientific societies are actively drafting guidelines and seeking funding to promote these initiatives. Numerous national and international multicenter collaborations demonstrate the ability to leverage combined resources to conduct large reference interval studies by direct methods. However, not all demographics are equally accessible. Novel indirect methods are attractive solutions that utilize computational methods to define reference distributions and reference intervals from mixed data sets of pathologic and non-pathologic patient test results. In an effort to make reference intervals more accurate and personalized, individual-based reference intervals are shown to be more useful than population-based reference intervals in detecting clinically significant analyte changes in a patient that might otherwise go unrecognized when using wider, population-based reference intervals. Additionally, continuous reference intervals can provide more accurate ranges as compared to age-based partitions for individuals that are near the ends of the age partition. The advantages and disadvantages of different reference interval approaches as well as the advancement of non-conventional reference interval studies are discussed in this review.

Urinary cystatin C: pediatric reference intervals and comparative assessment as a biomarker of renal injury among children in the regions with high burden of CKDu in Sri Lanka

BACKGROUND

Cystatin C (Cys-C) is an emerging biomarker of renal diseases and its clinical use, particularly for screening the communities affected by chronic kidney disease of unknown etiology (CKDu), is hindered due to the lack of reference intervals (RIs) for diverse ethnic and age groups. The present study aimed to define RIs for urinary Cys-C (uCys-C) for a healthy pediatric population in Sri Lanka and in turn compare the renal function of the residential children in CKDu endemic and non-endemic regions in Sri Lanka.

METHODS

A cross-sectional study was conducted with 850 healthy children (10-17 years) from selected locations for reference interval establishment, while a total of 892 children were recruited for the comparative study. Urine samples were collected and analyzed for Cys-C, creatinine (Cr) and albumin. Cr-adjusted uCys-C levels were partitioned by age, and RIs were determined with quantile regression (2.5th, 50th and 97.5th quantiles) at 90% confidence interval.

RESULTS

The range of median RIs for uCys-C in healthy children was 45.94-64.44 ng/mg Cr for boys and 53.58-69.97 ng/mg Cr for girls. The median (interquartile range) uCys-C levels of children in the CKDu endemic and non-endemic regions were 58.18 (21.8-141.9) and 58.31 (23.9-155.3) ng/mg Cr with no significant difference (P = 0.781). A significant variation of uCys-C was noted in the children across age.

CONCLUSIONS

Notably high uCys-C levels were observed in children with elevated proteinuria. Thus, uCys-C could be a potential biomarker in identifying communities at high risk of CKDu susceptibility.

Arterial hypertension and cystatin C during neonatal physiologic dehydration

A reduced nephron number may play a role in the pathogenesis of arterial hypertension (AH), and it is well recognized that individual nephron endowment is widely variable. However, nephrons count is technically impossible in vivo. Based on the observation that subjects with a reduced nephron mass exhibit an increase in renal functional biomarkers during acute dehydration, we hypothesized that cystatin C concentration during neonatal physiological dehydration could identify subjects with reduced nephron endowment. This is a prospective, observational, cohort study enrolling healthy, caucasian, term neonates born after an uneventful pregnancy. Two groups of newborns were compared: neonates born to fathers on antihypertensive treatment (HF) versus those born to proven normotensive fathers older than 40 years of age (NF). Enrolled newborns underwent cystatin C determination at the time of newborn screening. Forty newborns with HF and 80 with NF were enrolled. No differences in baseline characteristics were observed between the two groups except for the number of hypertensive grandparents higher among newborns to HF (47.8% vs. 21.1%; p: 0.001). Cystatin C was significantly higher in newborns with HF (1.62 ± 0.30 mg/L vs 1.41 ± 0.27 mg/L; p < 0.001). Linear regression analysis corrected for confounders confirmed that paternal hypertension was the only variable significantly associated with high cystatin C level during post-natal dehydration. Besides offering new insights on the pathogenesis of familial hypertension, our results support the specific role of nephron endowment and suggest the possibility of identifying subjects at risk for reduced nephron endowment as early as at birth.