Big data and reference intervals: rationale, current practices, harmonization and standardization prerequisites and future perspectives of indirect determination of reference intervals using routine data

Abstract

Reference intervals are commonly used as a decision-making tool. In this review, we provide an overview on “big data” and reference intervals, describing the rationale, current practices including statistical methods, essential prerequisites concerning data quality, including harmonization and standardization, and future perspectives of the indirect determination of reference intervals using routine laboratory data.

Indirect determination of biochemistry reference intervals using outpatient data

“…Consequently, in laboratory medicine, where quantitative data is generated every day, machine learning, data mining, business intelligence and related concepts are starting to be used for different purposes including analytical and quality management [ 25 ]. For the determination of reference intervals, for which classical (direct) recommendations are laborious and expensive, various statistical (indirect) methods have been developed using big data [ 4 ]. It is important to remark that some specialists are concerned about the possible bias due to the presence of unhealthy individuals in the dataset.…”

“…Fourth, we selected one statistical method (NUMBER method) to calculate reference intervals, and compared these with the reference limit estimator method [ 24 ]. Several statistical methods have been proposed so far but no consensus or official recommendations about ‘which method to use when’ are available yet [ 4 ]. We recommend that, on an international level, indirect (statistical) reference interval methods are compared, in order to reach consensus on criteria to decide which statistical method should be applied for which test.…”

“…Test results were excluded when phlebotomy was performed in the hospital (inpatients), drug addiction centres, mental health centres, external emergency centres, the prison women centre, or at home (e.g. when primary care patients could not visit the laboratory due to illness) since we expected substantial differences in health status in these settings that can add noise to the data [ 4 ]. We performed sensitivity analyses to compare the distribution between all the included centres, showing no signs of sample or sex bias between centres (results not shown).…”

“…According to ISO15189:2012, it is the responsibility of the laboratory to either validate them, find reference intervals from other sources or calculate the appropriate reference intervals for their method and population. Two different approaches to calculate reference intervals could be used: (a) The procedure recommended by the International Federation of Clinical Chemistry (IFCC), known as the direct method and [ 2 , 3 ] (b) an alternative approach, known as the indirect method [ 4 ].…”

“…It starts by acquiring a general overview of the total population by analysing clinical data from the laboratory information system (LIS) and, from this, filtering to uncover the distribution of the reference population and the reference intervals. This approach has several advantages, since ‘big’ analytical data is more accessible nowadays [ 4 ]. Automation has increased in clinical laboratories.…”