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Estimating Reference Intervals From Routine Laboratory Data Using Indirect Reference Interval Methods
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Credits: 1.0
- CME
- P.A.C.E.®
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Course Difficulty: Intermediate
- FLORIDA
Categories:
- For CLS: Supervision/Administration, Quality Control/Quality Assurance, and Safety
- For Doctors: General (AMA Category I)
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Video Length:56m, 46s
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Downloads:
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Application of data analytics to archived laboratory data with the intention of improving patient care and laboratory efficiency is an area of interest that continues to grow among laboratory professionals. The process of applying indirect methods for reference interval (RI) estimation and verification is an example of how laboratorians can use archived data to improve test interpretation and patient care. The aim of this presentation is to describe the principles of indirect RI methods and to provide practical and applicable examples where one can use these analytical tools to characterize RIs for analytes with varied distributions and partitions.
Originally published on December 30, 2024
Lecture Presenter
 | Kelly Doyle, PhD, DABCC, FADLM Associate Professor of Pathology (Clinical) |
Dr. Kelly Doyle is a medical director of special chemistry and endocrinology at ARUP Laboratories and an associate professor at the University of Utah School of Medicine. Dr. Doyle received his doctorate degree in medicinal chemistry from the University of Utah. He then completed a clinical chemistry fellowship at the University of Utah School of Medicine and an oncological sciences fellowship at the Huntsman Cancer Institute. Dr. Doyle is board certified and a member of the American Association of Clinical Chemistry Academy. His research interests include clinical application and implementation of mass spectrometry, emerging biomarkers, endocrinology, and laboratory best practices.
Objectives
After this presentation, participants will be able to:
- Summarize the establishment and use of reference intervals in clinical laboratory practice
- Discuss the application of direct and indirect methods to determine population/sex/age-based reference intervals
- Describe how different estimation methods can overcome analyte specific challenges including skewed, partial, or overlapping distributions
Sponsored by:
University of Utah School of Medicine, Department of Pathology, and ARUP Laboratories