MSACL 2022 Abstract

Free Climb or Free Solo: Guidance and Accounts of Establishing Reference Intervals for LDT Mass Spectrometry Assays

Kelly Doyle, PhD, DABCC (1,2) Elizabeth L. Frank, PhD, DABCC (1,2)
(1) University of Utah Health, Salt Lake City, UT (2) ARUP Laboratories, Inc., Salt Lake City, UT

Elizabeth Frank, PhD (Presenter) University of Utah Health / ARUP Laboratories

Presenter Bio: Elizabeth L. Frank, PhD, is a professor of pathology at the University of Utah School of Medicine and a medical director at ARUP Laboratories, a national reference laboratory operated by the University of Utah, in Salt Lake City. She is certified as a clinical chemist by the American Board of Clinical Chemistry and is a fellow of the AACC Academy. Dr. Frank's clinical and scientific interests are focused on measurement of biogenic amines, porphyrins, and vitamins using HPLC and LC-MS/MS; determination of calculi composition using FTIR; biochemical assessment of nephrolithiasis risk; and use of laboratory test results to evaluate health status.

Relevant Financial Disclosures (within past 24 months, reported on Feb 07, 2023) No relevant financial relationship(s) to disclose. Discussing Products? NO, presenter will NOT present educational content related to products/services of the ineligible company(ies) listed above. Conflict mitigation NOT REQUIRED.

Kelly Doyle, PhD (Presenter) University of Utah Health / ARUP Laboratories

Presenter Bio: I am a board certified clinical chemist (DABCC) with research and clinical interests in mass spectrometry, pediatrics, endocrinology, and toxicology. I am fortunate to be engaged in clinical service and education. I enjoy collaborating on laboratory processes optimization, development of robust methods, and in efforts focused on improving patient care. I received a Ph.D. in Medicinal Chemistry and completed a ComACC accredited fellowship the Department of Pathology at the University of Utah School of Medicine in Clinical Chemistry.

Relevant Financial Disclosures (within past 24 months) Not yet reported.

Objectives
1. Discuss establishment and use of reference intervals in clinical laboratory practice.
2. Summarize regulatory and guidance documents for establishing, transferring, and verifying reference intervals.
3. Describe application of direct and indirect methods to determine population/sex/age-based reference intervals.

Summary
Although assessment of reference intervals (RIs) is an integral component of laboratory medicine practice and so a requirement of LC-MS/MS assay development, establishing RIs using the traditional approach as described in the Clinical and Laboratory Standards Institute (CLSI) guidelines may not be feasible due to limited resources. Direct sampling is complicated by insufficient access to patients within varying interval partitions (e.g., pediatrics, pregnancy), and adoption or transferring of RIs is hindered by lack of assay standardization and unique population demographics. However, indirect sampling techniques using laboratory database results have significant practical advantages compared to direct sampling methods. The use of stored patient data can offer a faster and less costly means to developing RIs, particularly if multiple partitions based on characteristics such as age and sex are required or if selection criteria would cause much of a general healthy population to be excluded from participation in the study.

Numerous techniques designed to identify the distribution of healthy values in an uncharacterized dataset have been proposed. Recent reports have exposed flaws in the application of some techniques and reveal the need for careful evaluation of indirect methods to avoid potential errors. Modernized approaches, based on ready to use R packages, can aid in the development of accurate reference values.

Syllabus/Topics
In this practical training course, we will provide a brief review of the concept of RIs and the fundamentals for establishing, transferring, and verifying RIs. Our experience evaluating RIs for various LDT mass spectrometry assays with differing patient populations will be presented and examples of indirect methods using stored patient data to estimate RIs will be discussed.