Reference Intervals With Indirect Methods in Italy

Overview

Reference intervals are an essential tool for the clinical interpretation of laboratory test results. Traditionally, these interval are determined using samples from healthy individuals, a process that is resource-intensive, time-consuming, and require the active recruitment of healthy volunteers. In recent years, due to the increasing availability of electronic health record (EHR) databases and the growing number of laboratory tests, it is possible to determine the reference intervals indirectly. This approach relies on the analysis of routine data acquired in clinical laboratories, eliminating the need for active recruiting healthy subjects and significantly reducing costs. Moreover, the method has the potential to eliminate the selection bias of an ultra-healthy population typical of the direct methods. The indirect methods for determining reference intervals have evolved from simple strategies of isolating the healthy population using sample metadata, to sophisticated statistical models that effectively distinguish normal from pathological distributions. One of the advanced techniques, RefineR, has reached an excellent combination of accuracy, robustness, and computational efficiency, outperforming previous methods. It has been implemented as an open-source R package, facilitating its application in real-world settings. In recognition of these advantages, the IFCC (International Federation of Clinical Chemistry and Laboratory Medicine), through its Committee on Reference Intervals and Decision Limits (C-RIDL), has promoted the adoption of indirect methods for determining reference intervals, highlighting the advantages of this strategy, including greater speed, lower costs, and the absence of a need to recruit healthy donors. Furthermore, a recent study has highlighted age-related physiological variations in hemoglobin levels in elderly population. This underscores the need for defining age-specific reference intervals which are currently absent from most laboratory reports, potentially impacting diagnostic accuracy.

Primary outcome measures

• Define indirect reference intervals for complete blood count (CBC) parameters by analyzing large-scale retrospective routine laboratory data. [Time frame: up to 3 years]

Secondary outcome measures (3)

• Assess regional differences by comparing results across 10 Italian regions. [Time frame: up to 3 years]
• Establish age- and sex-specific reference intervals [Time frame: up to 3 years]
• Evaluate inter-instrument variations by considering results obtained from different analytical platforms. [Time frame: up to 3 years]

Eligibility criteria

Inclusion criteria

• no one

Exclusion criteria

• no one

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: Yes

Study design

Observational model

Cohort

Study locations

Italy · 24 centers

• ASL Bari, Ospedale della Murgia, Altamura — Altamura
• Azienda Ospedaliero-Universitaria Ospedali Riuniti di Ancona — Ancona
• Azienda USL Toscana Sud Est, Arezzo — Arezzo
• Azienda ULSS 1 Dolomiti — Belluno
• Azienda Ospedaliero-Universitaria Careggi, Firenze — Florence
• IRCCS Istituto Clinico Humanitas, Milano — Milan
• IRCCS Ospedale San Raffaele, Milano — Milan
• Azienda Ospedaliero-Universitaria di Modena — Modena
• … and 16 more centers

Identifiers

NCT: NCT07433777 · CRO-2025-025

Primary sources (government registries)

• ClinicalTrials.gov (US NIH)

View this study on ClinicalTrials.gov ↗