Monsurul Hoq1,2, Susan Matthews3,4, Vicky Karlaftis1, Janet Burgess3, Jessica Cowley1,3, Susan Donath1,2, John Carlin1,2, Tina Yen3, Vera Ignjatovic5,2, Paul Monagle1,2. 1. Murdoch Children's Research Institute, Parkville, Australia. 2. The University of Melbourne, Parkville, Australia. 3. The Royal Children's Hospital, Parkville, Australia. 4. International Centre for Point of Care Testing, Flinders University, Bedford Park, Australia. 5. Murdoch Children's Research Institute, Parkville, Australia; vera.ignjatovic@mcri.edu.au.
Abstract
BACKGROUND: Age-specific reference intervals (RIs) have been developed for biochemistry analytes in children. However, the ability to interpret results from multiple laboratories for 1 individual is limited. This study reports a head-to-head comparison of reference values and age-specific RIs for 30 biochemistry analytes for children across 5 analyzer types. METHODS: Blood was collected from healthy newborns and children 30 days to <18 years of age. Serum aliquots from the same individual were analyzed on 5 analyzer types. Differences in the mean reference values of the analytes by the analyzer types were investigated using mixed-effect regression analysis and by comparing maximum variation between analyzers with analyte-specific allowable total error reported in the Westgard QC database. Quantile regression was used to estimate age-specific RIs using power variables in age selected by fractional polynomial regression for the mean, with modification by sex when appropriate. RESULTS: The variations of age-specific mean reference values between analyzer types were within allowable total error (Westgard QC) for most analytes, and common age-specific reference limits were reported as functions of age and/or sex. Analyzer-specific reference limits for all analytes on 5 analyzer types are also reported as functions of age and/or sex. CONCLUSIONS: This study provides quantitative and qualitative measures of the extent to which results for individual children can or cannot be compared across analyzer types, and the feasibility of RI harmonization. The reported equations enable incorporation of age-specific RIs into laboratory information systems for improving evidence-based clinical decisions in children.
BACKGROUND: Age-specific reference intervals (RIs) have been developed for biochemistry analytes in children. However, the ability to interpret results from multiple laboratories for 1 individual is limited. This study reports a head-to-head comparison of reference values and age-specific RIs for 30 biochemistry analytes for children across 5 analyzer types. METHODS: Blood was collected from healthy newborns and children 30 days to <18 years of age. Serum aliquots from the same individual were analyzed on 5 analyzer types. Differences in the mean reference values of the analytes by the analyzer types were investigated using mixed-effect regression analysis and by comparing maximum variation between analyzers with analyte-specific allowable total error reported in the Westgard QC database. Quantile regression was used to estimate age-specific RIs using power variables in age selected by fractional polynomial regression for the mean, with modification by sex when appropriate. RESULTS: The variations of age-specific mean reference values between analyzer types were within allowable total error (Westgard QC) for most analytes, and common age-specific reference limits were reported as functions of age and/or sex. Analyzer-specific reference limits for all analytes on 5 analyzer types are also reported as functions of age and/or sex. CONCLUSIONS: This study provides quantitative and qualitative measures of the extent to which results for individual children can or cannot be compared across analyzer types, and the feasibility of RI harmonization. The reported equations enable incorporation of age-specific RIs into laboratory information systems for improving evidence-based clinical decisions in children.
Authors: Vera Ignjatovic; Philipp E Geyer; Krishnan K Palaniappan; Jessica E Chaaban; Gilbert S Omenn; Mark S Baker; Eric W Deutsch; Jochen M Schwenk Journal: J Proteome Res Date: 2019-10-11 Impact factor: 4.466
Authors: Andre Madsen; Bjørg Almås; Ingvild S Bruserud; Ninnie Helen Bakken Oehme; Christopher Sivert Nielsen; Mathieu Roelants; Thomas Hundhausen; Marie Lindhardt Ljubicic; Robert Bjerknes; Gunnar Mellgren; Jørn V Sagen; Pétur B Juliusson; Kristin Viste Journal: J Clin Endocrinol Metab Date: 2022-06-16 Impact factor: 6.134
Authors: Gorkem Sezgin; Paul Monagle; Tze Ping Loh; Vera Ignjatovic; Monsurul Hoq; Christopher Pearce; Adam McLeod; Johanna Westbrook; Ling Li; Andrew Georgiou Journal: Sci Rep Date: 2020-10-26 Impact factor: 4.379