A preliminary study on the reference intervals of serum tumor marker in apparently healthy elderly population in southwestern China using real-world data

BACKGROUND

The aim is to establish and verify reference intervals (RIs) for serum tumor markers for an apparently healthy elderly population in Southwestern China using an indirect method.

METHODS

Data from 35,635 apparently healthy elderly individuals aged 60 years and above were obtained in West China Hospital from April 2020 to December 2021. We utilized the Box-Cox conversion combined with the Tukey method to normalize the data and eliminate outliers. Subgroups are divided according to gender and age to examine the division of RIs. The Z-test was used to compare differences between groups, and 95% distribution RIs were calculated using a nonparametric method.

RESULTS

In the study, we observed that the RIs for serum ferritin and Des-γ-carboxy prothrombin (DCP) were wider for men, ranging from 64.18 to 865.80 ng/ml and 14.00 to 33.00 mAU/ml, respectively, compared to women, whose ranges were 52.58 to 585.88 ng/ml and 13.00 to 29.00 mAU/ml. For other biomarkers, the overall RIs were established as follows: alpha-fetoprotein (AFP) 0-6.75 ng/ml, carcinoembryonic antigen (CEA) 0-4.85 ng/ml, carbohydrate antigen15-3 (CA15-3) for females 0-22.00 U/ml, carbohydrate antigen19-9 (CA19-9) 0-28.10 U/ml, carbohydrate antigen125 (CA125) 0-20.96 U/ml, cytokeratin 19 fragment (CYFRA21-1) 0-4.66 U/ml, neuron-specific enolase (NSE) 0-19.41 ng/ml, total and free prostate-specific antigens (tPSA and fPSA) for males 0-5.26 ng/ml and 0-1.09 ng/ml. The RIs for all these biomarkers have been validated through our rigorous processes.

CONCLUSION

This study preliminarily established 95% RIs for an apparently healthy elderly population in Southwestern China. Using real-world data and an indirect method, simple and reliable RIs for an elderly population can be both established and verified, which are suitable for application in various clinical laboratories.

Harmonization of indirect reference intervals calculation by the Bhattacharya method

OBJECTIVES

The aim of this study was to harmonize the criteria for the Bhattacharya indirect method Microsoft Excel Spreadsheet for reference intervals calculation to reduce between-user variability and use these criteria to calculate and evaluate reference intervals for eight analytes in two different years.

METHODS

Anonymized laboratory test results from outpatients were extracted from January 1st 2018 to December 31st 2019. To assure data quality, we examined the monthly results from an external quality control program. Reference intervals were determined by the Bhattacharya method with the St Vincent's hospital Spreadsheet firstly using original criteria and then using additional harmonized criteria defined in this study. Consensus reference intervals using the additional harmonized criteria were calculated as the mean of four users' lower and upper reference interval results. To further test the operation criteria and robustness of the obtained reference intervals, an external user validated the Spreadsheet procedure.

RESULTS

The extracted test results for all selected laboratory tests fulfilled the quality criteria and were included in the present study. Differences between users in calculated reference intervals were frequent when using the Spreadsheet. Therefore, additional criteria for the Spreadsheet were proposed and applied by independent users, such as: to set central bin as the mean of all the data, bin size as small as possible, at least three consecutive bins and a high proportion of bins within the curve.

CONCLUSIONS

The proposed criteria contributed to the harmonization of reference interval calculation between users of the Bhattacharya indirect method Spreadsheet.

Reference Intervals in Coagulation Analysis

Blood coagulation analysis is characterized by the application of a variety of materials, reagents, and analyzers for the determination of the same parameter, or analyte, by different laboratories worldwide. Accordingly, the application of common reference intervals, that, by definition, would represent a "range of values (of a certain analyte) that is deemed normal for a physiological measurement in healthy persons," is difficult to implement without harmonization of procedures. In fact, assay-specific reference intervals are usually established to allow for the discrimination of normal and abnormal values during evaluation of patient results. While such assay-specific reference intervals are often determined by assay manufacturers and subsequently adopted by customer laboratories, verification of transferred values is still mandatory to confirm applicability on site. The same is true for reference intervals that have been adopted from other laboratories, published information, or determined by indirect data mining approaches. In case transferable reference intervals are not available for a specific assay, a direct recruiting approach may or needs to be applied. In comparison to transferred reference interval verification, however, the direct recruiting approach requires a significantly higher number of well-defined samples to be collected and analyzed. In the present review, we aim to give an overview on the above-mentioned aspects and procedures, also with respect to relevant standards, regulations, guidelines, but also challenges for both, assay manufacturers and coagulation laboratories.

Changing Immunochemistry Platforms: Thyroid Function Test Comparison and Reference Intervals Based on Clinical Needs

Background

Diagnosis of thyroid dysfunction relies on thyroid stimulating hormone (TSH), free thyroxine (FT4), and free tri-iodothyronine (FT3) tests against valid reference intervals (RIs). We changed the immunoassay platform from Abbott Architect to Siemens Atellica and aimed to establish Atellica RIs based on laboratory information system (LIS) patient data.

Methods

Atellica thyroid hormone immunoassays were verified against those of Architect. Real-life patient results were retrieved from LIS. A single result per patient dataset was used to establish the RIs by the indirect method.

Results

Atellica and Architect assays correlated well but Atellica showed a positive bias between 13% and 53%, the largest for FT4. Variations of the Atellica assays were ≤4%. The 95% Atellica RIs were 0.4–3.8 mU/L for TSH, 0.9–1.6 ng/dL for FT4, and 227–416 pg/dL for FT3. Considering the accumulating clinical experience with Atellica, the RIs for clinical use were adjusted as 0.5–4.0 mU/L, 0.9–1.8 ng/dL, and 169–409 pg/dL, respectively.

Conclusions

We verified thyroid hormone RIs for Atellica by the indirect method for the first time. Our model proved reliable for selecting results of presumably healthy individuals from LIS data. Critical review of the RIs with local endocrinologists is essential.

Big data and reference intervals

Although reference intervals (RIs) play an important role in clinical diagnosis, there remain significant differences with respect to race, gender, age and geographic location. Accordingly, the Clinical Laboratory Standards Institute (CLSI) EP28-A3c has recommended that clinical laboratories establish RIs appropriate to their subject population. Unfortunately, the traditional and direct approach to establish RIs relies on the recruitment of a sufficient number of healthy individuals of various age groups, collection and testing of large numbers of specimens and accurate data interpretation. The advent of the big data era has, however, created a unique opportunity to "mine" laboratory information. Unfortunately, this indirect method lacks standardization, consensus support and CLSI guidance. In this review we provide a historical perspective, comprehensively assess data processing and statistical methods, and post-verification analysis to validate this big data approach in establishing laboratory specific RIs.

Blood sampling frequency as a proxy for comorbidity indices when identifying patient samples for review of reference intervals

OBJECTIVES

Indirect data mining methods have been proposed for review of published reference intervals (RIs), but methods for identifying patients with a low likelihood of disease are needed. Many indirect methods extract test results on patients with a low frequency blood sampling history to identify putative healthy individuals. Although it is implied there has been no attempt to validate if patients with a low frequency blood sampling history are healthy and if test results from these patients are suitable for RI review.

METHODS

Danish nationwide health registers were linked with a blood sample database, recording a population of 316,337 adults over a ten-year period. Comorbidity indexes were defined from registrations of hospital diagnoses and redeemed prescriptions of drugs. Test results from patients identified as having a low disease burden were used for review of RIs from the Nordic Reference Interval Project (NORIP).

RESULTS

Blood sampling frequency correlated with comorbidity Indexes and the proportion of patients without disease conditions were enriched among patients with a low number of blood samples. RIs based on test results from patients with only 1-3 blood samples per decade were for many analytes identical compared to NORIP RIs. Some analytes showed expected incongruences and gave conclusive insights into how well RIs from a more than 10 years old multi-center study (NORIP) performed on current pre-analytical and analytical methods.

CONCLUSIONS

Blood sampling frequency enhance the selection of healthy individuals for reviewing reference intervals, providing a simple method solely based on laboratory data without the addition of clinical information.

Verifying Clinically Derived Reference Intervals for Daily Excretion Rates of Fractionated Metanephrines Using Modern Indirect Reference Interval Models

OBJECTIVES

Biochemical testing of urinary metanephrines is useful in the diagnosis and monitoring of pheochromocytoma and paragangliomas. We investigated the feasibility of mixture decomposition (ie, indirect) methods in verifying clinically derived reference intervals for urinary deconjugated metanephrine metabolites.

METHODS

Urinary 24-hour metanephrine and normetanephrine excretion results were extracted from our data warehouse and intervals were estimated by the modern variant of the Hoffmann method, maximum likelihood estimation (MLE), and gamma mixture model using R software.

RESULTS

Hoffmann, MLE, and gamma mixture models provided metanephrine and normetanephrine intervals that closely matched those derived from clinical studies. However, three-component MLE and gamma models were required for normetanephrine in adult women because the Hoffmann method was not suitable. Some data transformations caused blending of the mixed distributions and subsequent widening of the reference interval estimation, emphasizing the importance of careful data transformation for Hoffmann and MLE analyses. Gamma mixture models gave overall good agreement without the need for data transformation.

CONCLUSIONS

Indirect methods have utility in verifying reference intervals in 24-hour urine specimens collected by patients. We emphasize the benefits of applying multiple decomposition methods to corroborate findings and careful application of data transformation when using Gaussian-based models.

Reference intervals for 12 clinical laboratory tests in a Danish population: The Lolland-Falster Health Study

Reference intervals (RIs), developed as part of the Nordic Reference Interval Project 2000 (NORIP) are widely used in most European laboratories. We aimed to examine the validity of the NORIP RIs by establishing RIs for 12 frequently used laboratory tests based on data from a local Danish population and compare these local RIs with the NORIP RIs. Using an a posteriori direct sampling approach, blood sample data were assessed from 11,138 participants aged 18+ years in the Lolland-Falster Health Study (LOFUS), of whom 2154 turned out to meet criteria for being healthy for inclusion in establishing RIs according to the NORIP methodology. The 2.5th and 97.5th percentiles were calculated for alanine aminotransferase (ALAT), albumin, alkaline phosphatase, bilirubin, creatinine, hemoglobin, high-density lipoprotein cholesterol, iron, low-density lipoprotein cholesterol, thrombocytes, total cholesterol, and triglycerides. When comparing our estimates with the NORIP, the lower reference limits (RLs) for bilirubin and iron were lower, and higher for ALAT, thrombocytes and triglycerides. Upper RLs were lower for albumin (males and females ≥70 years), bilirubin and iron, but higher for alkaline phosphatase, triglycerides and for creatinine in men. In LOFUS, approximately 20% of the participants were healthy and qualified for inclusion in the establishment of RIs. Several of the local RIs differed from the NORIP RIs.

Gender and age-specific reference intervals of common biochemical analytes in Chinese population: Derivation using real laboratory data

BACKGROUND

Indirect sampling methods are not only inexpensive but also efficient for establishing reference intervals (RIs) using clinical data. This study was conducted to select fully normal records to establish ageand gender-specific RIs for common biochemical analytes by laboratory data mining.

METHODS

In total, 280,206 records from 2014 to 2018 were obtained from Peking Union Medical College Hospital. Common biochemical analytes total protein, albumin, total bilirubin (TBil), direct bilirubin (DBil), alanine aminotransferase (ALT), glutamyltranspeptidase (GGT), alkaline phosphatase (ALP), aspartate aminotransferase (AST), lactate dehydrogenase (LDH), potassium, sodium, chlorine, calcium, urea, glucose, uric acid (UA), inorganic phosphorus, creatinine (Cr), total cholesterol, triglyceride, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol] were measured using an automatic analyzer. Sources of variation were identified by multiple regression analysis. The 2.5th and 97.5th percentiles were calculated as the lower and upper limits of the RIs, respectively.

RESULTS

Gender was the major source of variation among the 13 common biochemical analytes with an rp > 0.15. In contrast to the value listed in the WS/T 404, nearly all RIs established in this study were significantly narrower. Furthermore, age-specific RIs should be determined for DBil, LDH, and urea, whereas gender-specific RIs are suggested for GGT, LDH, and urea.

CONCLUSIONS

We recommend that gender-specific RIs should be established for ALT, AST, GGT, DBil, TBil, UA, and Cr as well as genderand age-specific RIs for urea and ALP. Through indirect sampling, ageand gender-specific RIs for common biochemical analytes were established and analyzed.

Application of adult reference intervals in children

The aim of this study was to evaluate to which extend adult reference intervals (RIs) could be applied in children. A local paediatric population (aged 1 to < 20 years), based on first draw samples from general practitioners (GPs), was established. Children with samples taken at a hospital or > 3 samples from GPs were excluded. Analytes evaluated included haematological, liver and pancreatic function, kidney function, electrolytes, and metabolism parameters. Applicability of adult RIs in children aged 1-17 years was evaluated using individuals aged 18-19 years as reference groups for the adult RIs. The local population consisted of 31,024 children with 282,721 analyses in total. For each analyte, 17 age strata and two gender strata were established. Partitioning was not warranted in 51% of the male strata and in 69% of the female strata. Adult RIs could be applied in 42% for children aged 1-< 10 years, 57% for children aged 10-< 15 years, and 85% for children aged 15-<18 years.Conclusion: for certain analytes, there is no need to partition between adult and paediatric RIs, but a need for age- and gender-specific RIs remains for several clinical laboratory tests.What is Known:• Establishing paediatric reference intervals (RIs) is time consuming, costly, and not feasible for many laboratories. Transference of RIs established elsewhere often leads to misclassification of paediatric laboratory results.• Adult RIs are often more easily established and validated.What is New:• Adult RIs can be applied to children as young as 2 years for some analytes. Conversely, for some analytes, adult RIs cannot be applied in children aged 1-17 years.• Laboratory data can be applied in evaluating the need for partitioning in reference intervals.

A study on reference interval transference via linear regression

Background Reference intervals (RIs) transference can expand the applicability of established RIs. However, the study on transference methodology is insufficient, and RIs validation based on small samples cannot adequately identify transferred risk under complex situations. This study aimed to find appropriate conditions to ensure the effect of transference. Methods We established the RIs of Roche and Beckman systems for 27 analytes based on 681 healthy individuals. Roche RIs were converted into the Beckman RIs using linear regression (least squares method) which is divided into two methods - Methodref (500 test numbers with relatively narrow data range) and Methodep (80 test numbers with relatively wide data range). Taking the RIs established by Beckman results as standard, we assessed the accuracy, precision and trueness of transferred results under various conditions. Results A total of 29.6% and 48.1% of analytes were consistent between the two systems for the lower and upper reference limits, respectively. The concordance rates between transferred and measured RIs for Methodref were up to 74.1% and 92.6%, which were better than Methodep (44.4% and 59.3%). The CV of transferred reference limits decreased gradually with increasing test number under the same data range. For most analytes, excluding some electrolyte tests, we could obtain accurate results when r > 0.800 and the test number was sufficient regardless of the regression equation types. Conclusions Transferability of RIs is affected by many factors, such as correlation, test number, regression equation type, and quality requirement. To reduce the risk of transference, it is very important to select right method with reasonable conditions.

A new indirect estimation of reference intervals: truncated minimum chi-square (TMC) approach

All known direct and indirect approaches for the estimation of reference intervals (RIs) have difficulties in processing very skewed data with a high percentage of values at or below the detection limit. A new model for the indirect estimation of RIs is proposed, which can be applied even to extremely skewed data distributions with a relatively high percentage of data at or below the detection limit. Furthermore, it fits better to some simulated data files than other indirect methods. The approach starts with a quantile-quantile plot providing preliminary estimates for the parameters (λ, μ, σ) of the assumed power normal distribution. These are iteratively refined by a truncated minimum chi-square (TMC) estimation. The finally estimated parameters are used to calculate the 95% reference interval. Confidence intervals for the interval limits are calculated by the asymptotic formula for quantiles, and tolerance limits are determined via bootstrapping. If age intervals are given, the procedure is applied per age interval and a spline function describes the age dependency of the reference limits by a continuous function. The approach can be performed in the statistical package R and on the Excel platform.

Paediatric reference intervals are heterogeneous and differ considerably in the classification of healthy paediatric blood samples

The aim was to elude differences in published paediatric reference intervals (RIs) and the implementations hereof in terms of classification of samples. Predicaments associated with transferring RIs published elsewhere are addressed. A local paediatric (aged 0 days to < 18 years) population of platelet count, haemoglobin level and white blood cell count, based on first draw samples from general practitioners was established. PubMed was used to identify studies with transferable RIs. The classification of local samples by the individual RIs was evaluated. Transference was done in accordance with the Clinical and Laboratory Standards Institute EP28-A3C guideline. Validation of transference was done using a quality demand based on biological variance. Twelve studies with a combined 28 RIs were transferred onto the local population, which was derived from 20,597 children. Studies varied considerably in methodology and results. In terms of classification, up to 63% of the samples would change classification from normal to diseased, depending on which RI was applied. When validating the transferred RIs, one RI was implementable in the local population. Conclusion: Published paediatric RIs are heterogeneous, making assessment of transferability problematic and resulting in marked differences in classification of paediatric samples, thereby potentially affecting diagnosis and treatment of children. What is Known: • Reference intervals (RIs) are fundamental for the interpretation of paediatric samples and thus correct diagnosis and treatment of the individual child. • Guidelines for the establishment of adult RIs exist, but there are no specific recommendations for establishing paediatric RIs, which is problematic, and laboratories often implement RIs published elsewhere as a consequence. What is New: • Paediatric RIs published in peer-reviewed scientific journals differ considerably in methodology applied for the establishment of the RI. • The RIs show marked divergence in the classification of local samples from healthy children.

A simple transformation independent method for outlier definition

BACKGROUND

Definition and elimination of outliers is a key element for medical laboratories establishing or verifying reference intervals (RIs). Especially as inclusion of just a few outlying observations may seriously affect the determination of the reference limits. Many methods have been developed for definition of outliers. Several of these methods are developed for the normal distribution and often data require transformation before outlier elimination.

METHODS

We have developed a non-parametric transformation independent outlier definition. The new method relies on drawing reproducible histograms. This is done by using defined bin sizes above and below the median. The method is compared to the method recommended by CLSI/IFCC, which uses Box-Cox transformation (BCT) and Tukey's fences for outlier definition. The comparison is done on eight simulated distributions and an indirect clinical datasets.

RESULTS

The comparison on simulated distributions shows that without outliers added the recommended method in general defines fewer outliers. However, when outliers are added on one side the proposed method often produces better results. With outliers on both sides the methods are equally good. Furthermore, it is found that the presence of outliers affects the BCT, and subsequently affects the determined limits of current recommended methods. This is especially seen in skewed distributions. The proposed outlier definition reproduced current RI limits on clinical data containing outliers.

CONCLUSIONS

We find our simple transformation independent outlier detection method as good as or better than the currently recommended methods.

Validation of plasma thyroxine and triiodothyronine methods on the ADVIA Centaur® XP

Standardization programs for thyroid hormones have revealed bias between immunochemical methods and the reference method ED-ID-LC/MS. Lack of standardization between methods, suboptimal reference intervals and replacement of serum with plasma may compromise the capability of the immunochemical thyroid methods to diagnose thyroid disease. To accommodate the demand for faster turn-around times for laboratory replies, we replaced serum with plasma on some serum CE marked thyroid methods. This forced us to do on-board analytical correction for the plasma total T4 (TT4) method on ADVIA Centaur^® XP. We, next, validated the capability of the ADVIA Centaur^® XP thyroid methods on plasma by (1) first carrying out a prospective method comparison with the ED-ID-LC/MS reference method using collected plasma samples, (2) we verified the clinical reference intervals by analyzing collected plasma samples from healthy individuals, and (3) retrospectively compared laboratory results from two different time periods using serum TT4 and serum total triiodothyronine (TT3) versus plasma free thyroxine (FT4) and plasma TT3, respectively, to diagnose thyroid disease. The plasma FT4 method displayed a negative concentration-dependent bias against the reference method. This bias was apparently counteracted by a fitted reference interval for the plasma FT4 method. Indeed, overt hyperthyroid disease was found in 1.0% and 1.1% of the cases using serum and plasma and overt hypothyroid condition were in 1.3% and 0.6% of the cases using serum and plasma, respectively. In conclusion, the ADVIA Centaur^® XP FT4 method displayed a negative bias at high plasma FT4 concentrations against the reference method, but the diagnostic performance was not compromised due to a fitted reference interval.