Separating disease and health for indirect reference intervals

Kenneth A Sikaris

doi:10.1515/labmed-2020-0157

Abstract

Abstract The indirect approach to defining reference intervals operates ‘a posteriori’, on stored laboratory data. It relies on being able to separate healthy and diseased populations using one or both of clinical techniques or statistical techniques. These techniques are also fundamental in a priori, direct reference interval approaches. The clinical techniques rely on using clinical data that is stored either in the electronic health record or within the laboratory database, to exclude patients with possible disease. It depends on the investigators understanding of the data and the pathological impacts on tests. The statistical technique relies on identifying a dominant, apparently healthy, typically Gaussian distribution, which is unaffected by the overlapping populations with higher (or lower) results. It depends on having large databases to give confidence in the extrapolation of the narrow portion of overall distribution representing unaffected individuals. The statistical issues involved can be complex, and can result in unintended bias, particularly when the impacts of disease and the physiological variations in the data are under appreciated.

Highlights

Diagnostic testing essentially aims to support the clinician in answering specific clinical questions
Laboratorians may be familiar with receiver operator characteristic (ROC) curves that decrease the specificity for health against the sensitivity for disease, in order to define a cut-off that has intermediate specificity and sensitivity
It is important to acknowledge that ROC cut-offs are not reference intervals, which are, by definition, solely concerned with specificity for an apparently healthy reference population

Summary

Introduction

Diagnostic testing essentially aims to support the clinician in answering specific clinical questions. Direct reference interval studies aim to identify a population of healthy individuals ‘a priori’, and describe the confidence limits of their distribution of results. Indirect reference intervals aim to identify the apparently healthy distribution of results ‘a posteriori’ from a database that includes both healthy and diseased individuals [2]. A Gaussian reference interval reflects the many factors (usually >>20) that cause variation of results between healthy individuals This is no different to what laboratory scientists intrinsically understand regarding analysis, where measurement uncertainty follows the Gaussian distribution because there are numerous determinants that affect every step in analysis including calibration errors, sampling errors, lot to lot reaction variations and reading errors. This makes it difficult to know where to draw the line for partitioning, and some analytes change continuously from childhood to puberty e.g. rising creatinine (representing muscular development) and rising and falling ALP (representing the changes in bone development) [96]

Conclusions

Findings

Summary