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Reference Intervals With Indirect Methods in Italy
Sponsor: Centro di Riferimento Oncologico - Aviano
Summary
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.
Key Details
Gender
All
Age Range
0 Years - 100 Years
Study Type
OBSERVATIONAL
Enrollment
1000000
Start Date
2025-10-08
Completion Date
2028-06-01
Last Updated
2026-02-25
Healthy Volunteers
Yes
Conditions
Locations (24)
ASL Bari, Ospedale della Murgia, Altamura
Altamura, Altamura, Italy
Azienda Ospedaliero-Universitaria Ospedali Riuniti di Ancona
Ancona, Ancona, Italy
Azienda USL Toscana Sud Est, Arezzo
Arezzo, Arezzo, Italy
Azienda ULSS 1 Dolomiti
Belluno, Belluno, Italy
Azienda Ospedaliero-Universitaria Careggi, Firenze
Florence, Firenze, Italy
IRCCS Istituto Clinico Humanitas, Milano
Milan, Milano, Italy
IRCCS Ospedale San Raffaele, Milano
Milan, Milano, Italy
Azienda Ospedaliero-Universitaria di Modena
Modena, Modena, Italy
Bianalisi Carate Brianza, Monza e Brianza
Monza, Monza, Italy
Azienda Ospedaliero-Universitaria Maggiore della Carità di Novara
Novara, Novara, Italy
Azienda Ospedale Università Padova
Padova, Padova, Italy
Azienda Ospedaliera Universitaria Policlinico 'Paolo Giaccone' di Palermo
Palermo, Palermo, Italy
Centro di Riferimento Oncolgico -IRCCS
Aviano, Pordenone, Italy
Azienda Sanitaria Friuli Occidentale, Ospedale di Pordenone
Pordenone, Pordenone, Italy
Azienda Ospedaliera Universitaria Senese,
Siena, Siena, Italy
Azienda Sanitaria Universitaria Giuliano Isontina
Trieste, Trieste, Italy
Azienda Sanitaria Universitaria Friuli Centrale, Ospedale di Udine
Udine, Udine, Italy
AULSS 3 Serenissima, Ospedale di Mestre
Mestre, Venezia, Italy
Azienda Ospedaliera Universitaria Integrata di Verona
Verona, Verona, Italy
AULSS 8 Berica, Ospedale S. Bortolo di Vicenza
Vicenza, Vicenza, Italy
Ospedale di Desio, ASST Brianza
Desio, Italy
ASL5 Spezzino, La Spezia
La Spezia, Italy
ASST Grande Ospedale Metropolitano Niguarda, Milano
Milan, Italy
Azienda ULSS 2 Marca Trevigiana
Treviso, Italy