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A comparison of methods to generate adaptive reference ranges in longitudinal monitoring

In a clinical setting, biomarkers are typically measured and evaluated as biological indicators of a physiological state. Population based reference ranges, known as ‘static’ or ‘normal’ reference ranges, are often used as a tool to classify a biomarker value for an individual as typical or atypical...

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Autores principales: Roshan, Davood, Ferguson, John, Pedlar, Charles R., Simpkin, Andrew, Wyns, William, Sullivan, Frank, Newell, John
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7894906/
https://www.ncbi.nlm.nih.gov/pubmed/33606821
http://dx.doi.org/10.1371/journal.pone.0247338
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author Roshan, Davood
Ferguson, John
Pedlar, Charles R.
Simpkin, Andrew
Wyns, William
Sullivan, Frank
Newell, John
author_facet Roshan, Davood
Ferguson, John
Pedlar, Charles R.
Simpkin, Andrew
Wyns, William
Sullivan, Frank
Newell, John
author_sort Roshan, Davood
collection PubMed
description In a clinical setting, biomarkers are typically measured and evaluated as biological indicators of a physiological state. Population based reference ranges, known as ‘static’ or ‘normal’ reference ranges, are often used as a tool to classify a biomarker value for an individual as typical or atypical. However, these ranges may not be informative to a particular individual when considering changes in a biomarker over time since each observation is assessed in isolation and against the same reference limits. To allow early detection of unusual physiological changes, adaptation of static reference ranges is required that incorporates within-individual variability of biomarkers arising from longitudinal monitoring in addition to between-individual variability. To overcome this issue, methods for generating individualised reference ranges are proposed within a Bayesian framework which adapts successively whenever a new measurement is recorded for the individual. This new Bayesian approach also allows the within-individual variability to differ for each individual, compared to other less flexible approaches. However, the Bayesian approach usually comes with a high computational cost, especially for individuals with a large number of observations, that diminishes its applicability. This difficulty suggests that a computational approximation may be required. Thus, methods for generating individualised adaptive ranges by the use of a time-efficient approximate Expectation-Maximisation (EM) algorithm will be presented which relies only on a few sufficient statistics at the individual level.
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spelling pubmed-78949062021-03-01 A comparison of methods to generate adaptive reference ranges in longitudinal monitoring Roshan, Davood Ferguson, John Pedlar, Charles R. Simpkin, Andrew Wyns, William Sullivan, Frank Newell, John PLoS One Research Article In a clinical setting, biomarkers are typically measured and evaluated as biological indicators of a physiological state. Population based reference ranges, known as ‘static’ or ‘normal’ reference ranges, are often used as a tool to classify a biomarker value for an individual as typical or atypical. However, these ranges may not be informative to a particular individual when considering changes in a biomarker over time since each observation is assessed in isolation and against the same reference limits. To allow early detection of unusual physiological changes, adaptation of static reference ranges is required that incorporates within-individual variability of biomarkers arising from longitudinal monitoring in addition to between-individual variability. To overcome this issue, methods for generating individualised reference ranges are proposed within a Bayesian framework which adapts successively whenever a new measurement is recorded for the individual. This new Bayesian approach also allows the within-individual variability to differ for each individual, compared to other less flexible approaches. However, the Bayesian approach usually comes with a high computational cost, especially for individuals with a large number of observations, that diminishes its applicability. This difficulty suggests that a computational approximation may be required. Thus, methods for generating individualised adaptive ranges by the use of a time-efficient approximate Expectation-Maximisation (EM) algorithm will be presented which relies only on a few sufficient statistics at the individual level. Public Library of Science 2021-02-19 /pmc/articles/PMC7894906/ /pubmed/33606821 http://dx.doi.org/10.1371/journal.pone.0247338 Text en © 2021 Roshan et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Roshan, Davood
Ferguson, John
Pedlar, Charles R.
Simpkin, Andrew
Wyns, William
Sullivan, Frank
Newell, John
A comparison of methods to generate adaptive reference ranges in longitudinal monitoring
title A comparison of methods to generate adaptive reference ranges in longitudinal monitoring
title_full A comparison of methods to generate adaptive reference ranges in longitudinal monitoring
title_fullStr A comparison of methods to generate adaptive reference ranges in longitudinal monitoring
title_full_unstemmed A comparison of methods to generate adaptive reference ranges in longitudinal monitoring
title_short A comparison of methods to generate adaptive reference ranges in longitudinal monitoring
title_sort comparison of methods to generate adaptive reference ranges in longitudinal monitoring
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7894906/
https://www.ncbi.nlm.nih.gov/pubmed/33606821
http://dx.doi.org/10.1371/journal.pone.0247338
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