Indirect Reference Intervals: Harnessing the Power of Stored Laboratory Data

Feasibility of using real-world free thyroxine data from the US and Europe to enable fast and efficient transfer of reference intervals from one population to another

Objectives

The direct approach for determining reference intervals (RIs) is not always practical. This study aimed to generate evidence that a real-world data (RWD) approach could be applied to transfer free thyroxine RIs determined in one population to a second population, presenting an alternative to performing multiple RI determinations.

Design and methods

Two datasets (US, n = 10,000; Europe, n = 10,000) were created from existing RWD. Descriptive statistics, density plots and cumulative distributions were produced for each data set and comparisons made. Cumulative probabilities at the lower and upper limits of the RIs were identified using an empirical cumulative distribution function. According to these probabilities, estimated percentiles for each dataset and estimated differences between the two sets of percentiles were obtained by case resampling bootstrapping. The estimated differences were then evaluated against a pre-determined acceptance criterion of ≤7.8% (inter-individual biological variability). The direct approach was used to validate the RWD approach.

Results

The RWD approach provided similar descriptive statistics for both populations (mean: US = 16.1 pmol/L, Europe = 16.4 pmol/L; median: US = 15.4 pmol/L, Europe = 15.8 pmol/L). Differences between the estimated percentiles at the upper and lower limits of the RIs fulfilled the pre-determined acceptance criterion and the density plots and cumulative distributions demonstrated population homogeneity. Similar RI distributions were observed using the direct approach.

Conclusions

This study provides evidence that a RWD approach can be used to transfer RIs determined in one population to another.

The BioRef Infrastructure, a Framework for Real-Time, Federated, Privacy-Preserving, and Personalized Reference Intervals: Design, Development, and Application

BACKGROUND

Reference intervals (RIs) for patient test results are in standard use across many medical disciplines, allowing physicians to identify measurements indicating potentially pathological states with relative ease. The process of inferring cohort-specific RIs is, however, often ignored because of the high costs and cumbersome efforts associated with it. Sophisticated analysis tools are required to automatically infer relevant and locally specific RIs directly from routine laboratory data. These tools would effectively connect clinical laboratory databases to physicians and provide personalized target ranges for the respective cohort population.

OBJECTIVE

This study aims to describe the BioRef infrastructure, a multicentric governance and IT framework for the estimation and assessment of patient group-specific RIs from routine clinical laboratory data using an innovative decentralized data-sharing approach and a sophisticated, clinically oriented graphical user interface for data analysis.

METHODS

A common governance agreement and interoperability standards have been established, allowing the harmonization of multidimensional laboratory measurements from multiple clinical databases into a unified "big data" resource. International coding systems, such as the International Classification of Diseases, Tenth Revision (ICD-10); unique identifiers for medical devices from the Global Unique Device Identification Database; type identifiers from the Global Medical Device Nomenclature; and a universal transfer logic, such as the Resource Description Framework (RDF), are used to align the routine laboratory data of each data provider for use within the BioRef framework. With a decentralized data-sharing approach, the BioRef data can be evaluated by end users from each cohort site following a strict "no copy, no move" principle, that is, only data aggregates for the intercohort analysis of target ranges are exchanged.

RESULTS

The TI4Health distributed and secure analytics system was used to implement the proposed federated and privacy-preserving approach and comply with the limitations applied to sensitive patient data. Under the BioRef interoperability consensus, clinical partners enable the computation of RIs via the TI4Health graphical user interface for query without exposing the underlying raw data. The interface was developed for use by physicians and clinical laboratory specialists and allows intuitive and interactive data stratification by patient factors (age, sex, and personal medical history) as well as laboratory analysis determinants (device, analyzer, and test kit identifier). This consolidated effort enables the creation of extremely detailed and patient group-specific queries, allowing the generation of individualized, covariate-adjusted RIs on the fly.

CONCLUSIONS

With the BioRef-TI4Health infrastructure, a framework for clinical physicians and researchers to define precise RIs immediately in a convenient, privacy-preserving, and reproducible manner has been implemented, promoting a vital part of practicing precision medicine while streamlining compliance and avoiding transfers of raw patient data. This new approach can provide a crucial update on RIs and improve patient care for personalized medicine.

Unsupervised machine learning method for indirect estimation of reference intervals for chronic kidney disease in the Puerto Rican population

Reference intervals (RIs) for clinical laboratory values are extremely important for diagnostics and treatment of patients. However, the determination of these ranges is costly and time-consuming. As a result, often different unverified RIs are used in practice for the same analyte and the same range is used for all patients despite evidence that the values are gender, age, and ethnicity dependent. Moreover, the abnormal flags are rudimentary, merely indicating if a value is within the RI. At the same time, clinical lab data generated in the everyday medical practice contains a wealth of information, that given the correct methodology, can help determine the RIs for each specific segment of the population, including populations that suffer from health disparities. In this work, we develop unsupervised machine learning methods, based on Gaussian mixtures, to determine RIs of analytes related to chronic kidney disease, using millions of routine lab results for the Puerto Rican population. We show that the measures are both gender and age dependent and we find evidence for normal age-related organ function deterioration and failure. We also show that the joint distribution of measures improves the diagnostic value of the lab results.

Neonatal reference intervals for thyroid stimulating hormone and free thyroxine assayed on a Siemens Atellica® IM analyzer: a cross sectional study

BACKGROUND

Deriving population specific reference intervals (RIs) or at the very least verifying any RI before adoption is good laboratory practice. Siemens has provided RIs for thyroid stimulating hormone (TSH) and free thyroxine (FT4) determined on their Atellica® IM analyzer for all age groups except the neonatal age group which provides a challenge for laboratories that intend to use it to screen for congenital hypothyroidism (CH) and other thyroid disorders in neonates. We set out to determine RIs for TSH and FT4 using data obtained from neonates undergoing routine screening for CH at the Aga Khan University Hospital, Nairobi, Kenya.

METHODOLOGY

TSH and FT4 data for neonates aged 30 days and below were extracted from the hospital management information system for the period March 2020 to June 2021. A single episode of testing for the same neonate was included provided both TSH and FT4 were done on the same sample. RI determination was performed using a non-parametric approach.

RESULTS

A total of 1243 testing episodes from 1218 neonates had both TSH and FT4 results. A single set of test results from each neonate was used to derive RIs. Both TSH and FT4 declined with increase in age with a more marked decline seen in the first 7 days of life. There was a positive correlation between logFT4 and logTSH (r_s (1216) = 0.189, p = < 0.001). We derived TSH RIs for the age groups 2-4 days (0.403-7.942 µIU/mL) and 5-7 days (0.418-6.319 µIU/mL), and sex specific RIs for males (0.609-7.557 µIU/mL) and females (0.420-6.189 µIU/mL) aged 8-30 days. For FT4, separate RIs were derived for the age groups 2-4 days (1.19-2.59 ng/dL), 5-7 days (1.21-2.29 ng/dL) and 8-30 days (1.02-2.01 ng/dL).

CONCLUSION

Our neonatal RIs for TSH and FT4 are different from those published or recommended by Siemens. The RIs will serve as a guide for the interpretation of thyroid function tests in neonates from sub-Saharan Africa where routine screening for congenital hypothyroidism using serum samples is done on the Siemens Atellica® IM analyzer.

Utilization of five data mining algorithms combined with simplified preprocessing to establish reference intervals of thyroid-related hormones for non-elderly adults

BACKGROUND

Despite the extensive research on data mining algorithms, there is still a lack of a standard protocol to evaluate the performance of the existing algorithms. Therefore, the study aims to provide a novel procedure that combines data mining algorithms and simplified preprocessing to establish reference intervals (RIs), with the performance of five algorithms assessed objectively as well.

METHODS

Two data sets were derived from the population undergoing a physical examination. Hoffmann, Bhattacharya, Expectation Maximum (EM), kosmic, and refineR algorithms combined with two-step data preprocessing respectively were implemented in the Test data set to establish RIs for thyroid-related hormones. Algorithm-calculated RIs were compared with the standard RIs calculated from the Reference data set in which reference individuals were selected following strict inclusion and exclusion criteria. Objective assessment of the methods is implemented by the bias ratio (BR) matrix.

RESULTS

RIs of thyroid-related hormones are established. There is a high consistency between TSH RIs established by the EM algorithm and the standard TSH RIs (BR = 0.063), although EM algorithms seems to perform poor on other hormones. RIs calculated by Hoffmann, Bhattacharya, and refineR methods for free and total triiodo-thyronine, free and total thyroxine respectively are close and match the standard RIs.

CONCLUSION

An effective approach for objectively evaluating the performance of the algorithm based on the BR matrix is established. EM algorithm combined with simplified preprocessing can handle data with significant skewness, but its performance is limited in other scenarios. The other four algorithms perform well for data with Gaussian or near-Gaussian distribution. Using the appropriate algorithm based on the data distribution characteristics is recommended.

Definitions and major prerequisites of direct and indirect approaches for estimating reference limits

Reference intervals are established either by direct or indirect approaches. Whereas the definition of direct is well established, the definition of indirect is still a matter of debate. In this paper, a general definition that covers all indirect models presently in use is proposed. With the upcoming popularity of indirect models, it has become evident that further partitioning strategies are required to minimize the risk of patients' false classifications. With indirect methods, such partitions are much easier to execute than with direct methods. The authors believe that the future of reference interval estimation belongs to indirect models with big data pools either from one laboratory or combined from several regional centres (if necessary). Independent of the approach applied, the quality assurance of the pre-analytical and analytical phase, considering biological variables and other confounding factors, is essential.

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.

Indirect Method for Estimation of Reference Intervals of Inflammatory Markers

Background

The direct method for reference interval (RI) estimating is limited due to the requirement of resources, difficulties in defining a non-diseased population, or ethical problems in obtaining samples. We estimated the RI for inflammatory biomarkers using an indirect method (RII).

Methods

C-reactive protein (CRP), erythrocyte sedimentation rate (ESR) and presepsin (PSEP) data of patients visiting a single hospital were retrieved from April 2009 to April 2021. Right-skewed data were transformed using the Box-Cox transformation method. A mixed population of non-diseased and diseased distributions was assumed, followed by latent profile analysis for the two classes. The intersection point of the distribution curve was estimated as the RI. The influence of measurement size was evaluated as the ratio of abnormal values and adjustment (n×bandwidth) of the distribution curve.

Results

The RIs estimated by the proposed RII method (existing method) were as follows: CRP, 0-4.1 (0-4.7) mg/L; ESR, 0-10.2 (0-15) mm/hr and PSEP, 0-411 (0-300) pg/mL. Measurement sizes ≥2,500 showed stable results. An abnormal-to-normal value ratio of 0.5 showed the most accurate result for CRP. Adjustment values ≤5 or >5 were applicable for a measurement size <25,000 or ≥25,000, respectively.

Conclusions

The proposed RII method could provide additional information for RI verification or estimation with some limitations.

Establishment of trimester-specific reference intervals for thyroid stimulating hormone and free thyroxine during pregnancy in southwest China by indirect method

OBJECTIVE

A series of physiological changes in thyroid function occur during pregnancy and differ from those non-pregnant women. This study aimed to establish the pregnancy-specific reference intervals of TSH and FT4 using an indirect method based on the healthy pregnant women from southwest China population.

METHODS

Thyroid function test results which available on the Laboratory Information System (LIS) were collected from the pregnancies who visited the Obstetric Clinic or the Department of Gynecology between 1 January 2015, and 30 December 2020. We grouped the data by trimesters to establish the reference intervals (RIs) based on the clinical consensus of different levels of TSH and FT4 at different weeks of gestation. All arrangements were referenced to the document CLSI EP28-A3C.

RESULTS

A total of 33,040 thyroid function test results of pregnant women, aged 31 (28,33) years were statistical analyzed. Estimated RIs for TSH and FT4 in the first, second and third trimesters corresponding to the 2.5^th and 97.5^th percentiles in TPOAb negative were 0.02-5.23, 0.03-5.24, 0.37-5.68 mIU/L, 11.66-20.69, 10.1-18.59, 9.85-16.86pmol/L, respectively.

CONCLUSION

This study provides trimester-specific RIs for TSH and FT4 among healthy pregnant women in southwest China which guides clinicians to diagnosis and screen for thyroid disorders in this region.

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.

Data mining of pediatric reference intervals

Laboratory tests are essential to assess the health status and to guide patient care in individuals of all ages. The interpretation of quantitative test results requires availability of appropriate reference intervals, and reference intervals in children have to account for the extensive physiological dynamics with age in many biomarkers. Creation of reference intervals using conventional approaches requires the sampling of healthy individuals, which is opposed by ethical and practical considerations in children, due to the need for a large number of blood samples from healthy children of all ages, including neonates and young infants. This limits the availability and quality of pediatric reference intervals, and ultimately negatively impacts pediatric clinical decision-making. Data mining approaches use laboratory test results and clinical information from hospital information systems to create reference intervals. The extensive number of available test results from laboratory information systems and advanced statistical methods enable the creation of pediatric reference intervals with an unprecedented age-related accuracy for children of all ages. Ongoing developments regarding the availability and standardization of electronic medical records and of indirect statistical methods will further improve the benefit of data mining for pediatric reference intervals.

Indirect estimation of reference intervals for thyroid parameters using ADVIA Centaur XP analyser

Background

The aim of this study was to determine the reference intervals (RIs) for thyroid stimulating hormone (TSH), free thyroxine (FT4), free triiodothyronine (FT3) and FT3/FT4 ratio using indirect methods.

Methods

We analyzed 1256 results TSH, FT4 and FT3 collected from a laboratory information system between 2017 and 2021. All measurements were performed on a Siemens ADVIA Centaur XP analyzer using the chemiluminescent immunoassay. We calculated the values of the 2.5th and 97.5th percentiles as recommended by the IFCC (CLSI C28-A3).

Results

The RIs derived for TSH, FT4, FT3 and FT3/FT4 ratio were 0.34-4.10 mIU/L, 11.3-20.6 pmol/L, 3.5-6.32 pmol/L and 0.21-0.47, respectively. We found a significant difference between calculated RIs for the TSH and FT4 and those recommended by the manufacturer. Also, FT3 values were significantly higher in the group younger than 30 years relative to the fourth decade (5.26 vs. 5.02, p=0.005), the fifth decade (5.26 vs. 4.94, p=0.001), the sixth decade (5.26 vs. 4.87, p<0.001), the seventh decade (5.26 vs. 4.79, p<0.001) and the group older than 70 years old (5.26 vs. 4.55, p<0.001). Likewise, we found for TSH values and FT3/FT4 ratio a significant difference (p <0.001) between different age groups.

Conclusions

The establishing RIs for the population of the Republic of Srpska were significantly differed from the recommended RIs by the manufacturer for TSH and FT4. Our results encourage other laboratories to develop their own RIs for thyroid parameters by applying CLSI recommendations.

Establishment of Pediatric Reference Intervals for Routine Laboratory Tests in Korean Population: A Retrospective Multicenter Analysis

Background

Reference intervals defined for adults or children of other ethnicities cannot be applied in the evaluation of Korean pediatric patients. Pediatric reference intervals are difficult to establish because children are in their growing stage and their physiology changes continuously. We aimed to establish reference intervals for routine laboratory tests for Korean pediatric patients through retrospective multicenter data analysis.

Methods

Preoperative laboratory test results from 1,031 pediatric patients aged 0 month–18 years who underwent minor surgeries in four university hospitals were collected. Age- and sex-specific reference intervals for routine laboratory tests were defined based on the Clinical and Laboratory Standards Institute (CLSI) EP28-A3c guidelines.

Results

The pediatric reference intervals determined in this study were different from existing adult reference intervals and pediatric reference intervals for other ethnicities. Most tests required age-specific partitioning, and some of those required sex-specific partitioning for at least one age-partitioned subgroup. Erythrocyte sedimentation rate, monocyte percentage, basophil percentage, activated partial thromboplastin time, glucose, cholesterol, albumin, bilirubin, chloride, and C-reactive protein did not show any difference between age- or sex-partitioned subgroups.

Conclusions

We determined Korean pediatric reference intervals for hematology, coagulation, and chemistry tests by indirect sampling based on medical record data from multiple institutions. These reference intervals would be valuable for clinical evaluations in the Korean pediatric population.

refineR: A Novel Algorithm for Reference Interval Estimation from Real-World Data

Reference intervals are essential for the interpretation of laboratory test results in medicine. We propose a novel indirect approach to estimate reference intervals from real-world data as an alternative to direct methods, which require samples from healthy individuals. The presented refineR algorithm separates the non-pathological distribution from the pathological distribution of observed test results using an inverse approach and identifies the model that best explains the non-pathological distribution. To evaluate its performance, we simulated test results from six common laboratory analytes with a varying location and fraction of pathological test results. Estimated reference intervals were compared to the ground truth, an alternative indirect method (kosmic), and the direct method (N = 120 and N = 400 samples). Overall, refineR achieved the lowest mean percentage error of all methods (2.77%). Analyzing the amount of reference intervals within ± 1 total error deviation from the ground truth, refineR (82.5%) was inferior to the direct method with N = 400 samples (90.1%), but outperformed kosmic (70.8%) and the direct method with N = 120 (67.4%). Additionally, reference intervals estimated from pediatric data were comparable to published direct method studies. In conclusion, the refineR algorithm enables precise estimation of reference intervals from real-world data and represents a viable complement to the direct method.

The importance of correct stratifications when comparing directly and indirectly estimated reference intervals

OBJECTIVES

There are generally two major reasons for the comparison of reference intervals (RIs): when externally determined RIs (from the literature or provided by a manufacturer) are compared with presently used intra-laboratory RIs and when indirectly estimated RIs are compared with directly established RIs. Discrepancies within these comparisons may occur for two reasons: 1. the pre-analytical and/or analytical conditions do not agree and/or 2. biological variables influencing the establishment of RIs have not been considered adequately. If directly and indirectly estimated reference intervals (RIs) are compared with each other, they very often agree. Sometimes, however, a comparison may differ, with the reason for any discrepancy not being further studied. A major reason for differences in the comparison of RIs is that the requirement for stratification has been neglected.

METHODS

The present report outlines the consequences to RI comparison if stratification is neglected during RI determination with the main variables affecting RIs being sex and age. Alanine aminotransferase was chosen as an example in which the RIs depend on both these factors.

RESULTS

Both direct and indirect approaches lead to erroneous RIs if stratification for variables which are known to affect the estimation of RIs is not performed adequately. However, failing to include a required stratification in procedures for directly determined RIs affects the outcome in a different way to indirectly determined RIs.

CONCLUSIONS

The resulting difference between direct and indirect RIs is often misinterpreted as an incorrect RI estimation of the indirect method.

The establishment of neuron-specific enolase reference interval for the healthy population in southwest China

To investigate and establish a reference interval (RI) of neuron-specific enolase (NSE) in southwest China's healthy population by using the laboratory information system database. A total of 86957 periodic health examination individuals of the medical examination center in West China Hospital from 2016 to 2018 were included in the study. We used the Box-Cox conversion combined with the Tukey method to normalize the data and eliminate the outliers, and the normal distribution method and the nonparametric method to estimate the 95% distribution RI. The NSE 95% distribution RI we established in healthy populations in southwest China through normal distribution and nonparametric method were 0-19.64 ng/ml and 0-20.46 ng/ml, respectively. The obtained RIs verification conformed to the standard and was significantly different from the reagent instruction(P < 0.05). The RI established by the nonparametric method was superior to the RI of the normal distribution method and reagent instruction(P < 0.05). We initially established an NSE RI that was suitable for the healthy southwest China population. The Box-Cox conversion combined with the Tukey method and nonparametric method is a reliable and straightforward indirect method for reference interval acquisition, which is suitable for the promotion and application of clinical laboratory.

Establishment of thromboelastography reference intervals by indirect method and relevant factor analyses

Thromboelastography (TEG) as a global coagulation test has been continuously developed for many decades in either research or clinical practice. The versatility of TEG test leads to difficulty in standardization and result interpretation. Reference intervals (RIs) of TEG may be one of the most controversial factors that influence its wide applications. RIs establishment with the traditional method is time‐consuming and laborious as well as beyond general laboratory capability. Indirect method using stored data and with statistical calculation and small cost is emerging as an alternative approach for RIs determination. Gender, age, or both affect RIs and must be taken into account before RIs estimation. The present study retrospectively collected a total of 930 TEG results as subjects and established RIs with indirect method for Kaolin‐activated TEG, including the parameters of R, K, αAngle, MA, and CI. Furthermore, gender, age, and gender‐dependent age subsets analyses were performed to determine their effects on RIs of TEG. In this study, we found that TEG parameters showed more hypercoagulability in female than male, most of the measured TEG variables were significantly associated with aging, but only in male statistical significance was found among different age stratification and 60‐year‐old could be considered as cutting point to differentiate coagulation ability in male. In addition, RIs of TEG were estimated by indirect method suitably and verified to be valid in our study. Finally, the RIs of TEG by indirect method were basically significantly different to the RIs recommended by manufacturer, but the consistent percentage is relatively high in the most of measured parameters. In conclusion, it is suggestive that the indirect method for RIs establishment is feasible, but relevant factors, such as gender and age, specifically gender‐dependent age effect, should be considered before RIs determinations.

Paediatric Reference Intervals: Current Status, Gaps, Challenges and Future Considerations

Establishing paediatric reference intervals (RIs) is a challenging task due to difficulties in subject recruitment, collection of adequate blood volume, and the inherent physiological changes of many biomarkers with age. Despite these challenges, several national and international initiatives have demonstrated: (a) the feasibility of prospectively designed paediatric RI studies; (b) the development of continuous RIs; and (c) the comparison of reference values across analyser types to harmonise paediatric RIs. Whilst these studies have improved the interpretation of paediatric test results and compliance with international accreditation (ISO15189) requirements, several gaps and challenges in translating current paediatric RIs into routine laboratory practice remain. Future priorities for paediatric RI studies include: (a) determination of the impact of discrete versus continuous RIs, analyser-specific versus harmonised RIs, and prospective collection versus data mining on the proportion of results outside the RIs; (b) understanding the clinical implications of analyser-to-analyser variation in reference values and use of evidence-based paediatric harmonised RIs where applicable; (c) adaptation of laboratory information systems to incorporate continuous RIs; (d) further understanding of the biological variation in paediatric biomarkers; (e) studies to address the paucity of accurate data for neonatal RI development; (f) periodic demonstration of RIs being clinically 'fit-for purpose'; and (g) agreement and policy updates for use of modern, best practice statistical methods in estimation of paediatric RIs. Furthermore, in vitro diagnostic manufacturers may require incentivised paediatric RI studies and publications through co-ordinated grants and collaboration at end-user sites to reduce the burden on sole users.