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).

A pipeline for the fully automated estimation of continuous reference intervals using real-world data

Reference intervals are essential for interpreting laboratory test results. Continuous reference intervals precisely capture physiological age-specific dynamics that occur throughout life, and thus have the potential to improve clinical decision-making. However, established approaches for estimating continuous reference intervals require samples from healthy individuals, and are therefore substantially restricted. Indirect methods operating on routine measurements enable the estimation of one-dimensional reference intervals, however, no automated approach exists that integrates the dependency on a continuous covariate like age. We propose an integrated pipeline for the fully automated estimation of continuous reference intervals expressed as a generalized additive model for location, scale and shape based on discrete model estimates using an indirect method (refineR). The results are free of subjective user-input, enable conversion of test results into z-scores and can be integrated into laboratory information systems. Comparison of our results to established and validated reference intervals from the CALIPER and PEDREF studies and manufacturers' package inserts shows good agreement of reference limits, indicating that the proposed pipeline generates high-quality results. In conclusion, the developed pipeline enables the generation of high-precision percentile charts and continuous reference intervals. It represents the first parameter-less and fully automated solution for the indirect estimation of continuous reference intervals.

Red Cell Distribution Width to Platelet Count Ratio Reference Intervals in Premature Infants Beyond the First Week of Life

Objective: Red cell distribution width (RDW) is a parameter of complete blood count (CBC). The RDW to platelet count ratio (RPR) is a new index that has been shown to reflect the severity of inflammation. We aim to determine the reference interval (RI) of RPR for premature newborns. Study design: The medical records of preterm infants who were followed up between January 2016 and December 2018 were reviewed. CBC levels were measured in 144 infants at <72 hours of age. Results: CBCs of infants (gestational age from 28 to 35weeks) had a RI of 0.038-0.126 for the RPR. The RI for RPR in infants with a gestational age of 32-35weeks was 0.042-0.129; and the RI for infants at 28-31weeks was 0.022-0.121. Conclusion: Establishment of RI for RPR in premature infants will allow clinical correlation of RPR alterations in this population.

Estimation of Reference Intervals from Routine Data Using the refineR Algorithm-A Practical Guide

BACKGROUND

Accurate reference intervals are essential for the interpretation of laboratory test results. Typically, they are determined by the central 95% range of test results from a predefined reference population. As these direct studies can face practical and ethical challenges, indirect methods using routine measurements offer an alternative approach.

METHODS

We provide step-by-step guidance on how to apply an indirect method in practice using refineR, the most recently published indirect method, and showcase the application by evaluating real-world data of 12 prespecified analytes. Measurements were retrieved from ARUP Laboratories' data warehouse, and were obtained from routine patient testing on cobas c502 or e602 analyzers. Test results were prefiltered and cleaned and, if necessary, physiologically partitioned prior to estimating reference intervals using refineR. Estimated reference intervals were then compared to established intervals provided by the manufacturer.

RESULTS

For most analytes, the reference intervals estimated by refineR were comparable to those provided by the manufacturer, shown by overlapping confidence intervals at both reference limits, or only the upper or lower limit. For thyroid-stimulating hormone, refineR estimated higher reference limits, while estimates for prealbumin were lower compared to the established reference interval.

CONCLUSIONS

We applied the refineR algorithm to a variety of real-world data sets resulting in reference intervals similar to intervals previously established by direct methods. We further provide practical guidance and a code example on how to apply an indirect method in a real-world scenario facilitating their access and thus their use in laboratory settings.

RIbench: A Proposed Benchmark for the Standardized Evaluation of Indirect Methods for Reference Interval Estimation

BACKGROUND

Indirect methods leverage real-world data for the estimation of reference intervals. These constitute an active field of research, and several methods have been developed recently. So far, no standardized tool for evaluation and comparison of indirect methods exists.

METHODS

We provide RIbench, a benchmarking suite for quantitative evaluation of any existing or novel indirect method. The benchmark contains simulated test sets for 10 biomarkers mimicking routine measurements of a mixed distribution of non-pathological (reference) values and pathological values. The non-pathological distributions represent 4 common distribution types: normal, skewed, heavily skewed, and skewed-and-shifted. To identify strengths and weaknesses of indirect methods, test sets have varying sample sizes and pathological distributions differ in location, extent of overlap, and fraction. For performance evaluation, we use an overall benchmark score and sub-scores derived from absolute z-score deviations between estimated and true reference limits. We illustrate the application of RIbench by evaluating and comparing the Hoffmann method and 4 modern indirect methods -TML (Truncated-Maximum-Likelihood), kosmic, TMC (Truncated-Minimum-Chi-Square), and refineR- against one another and against a nonparametric direct method (n = 120).

RESULTS

For the modern indirect methods, pathological fraction and sample size had a strong influence on the results: With a pathological fraction up to 20% and a minimum sample size of 5000, most methods achieved results comparable or superior to the direct method.

CONCLUSIONS

We present RIbench, an open-source R-package, for the systematic evaluation of existing and novel indirect methods. RIbench can serve as a tool for enhancement of indirect methods, improving the estimation of reference intervals.