Biomarkers in osteoarthritis: current status and outlook — the FNIH Biomarkers Consortium PROGRESS OA study

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Abstract

Currently, no disease-modifying therapies are approved for osteoarthritis (OA) use. One obstacle to trial success in this field has been our existing endpoints’ limited validity and responsiveness. To overcome this impasse, the Foundation for the NIH OA Biomarkers Consortium is focused on investigating biomarkers for a prognostic context of use for subsequent qualification through regulatory agencies. This narrative review describes this activity and the work underway, focusing on the PROGRESS OA study.

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Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors.

FRANK E. HARRELL, KERRY L LEE, DANIEL B. MARK (1996)

Multivariable regression models are powerful tools that are used frequently in studies of clinical outcomes. These models can use a mixture of categorical and continuous variables and can handle partially observed (censored) responses. However, uncritical application of modelling techniques can result in models that poorly fit the dataset at hand, or, even more likely, inaccurately predict outcomes on new subjects. One must know how to measure qualities of a model's fit in order to avoid poorly fitted or overfitted models. Measurement of predictive accuracy can be difficult for survival time data in the presence of censoring. We discuss an easily interpretable index of predictive discrimination as well as methods for assessing calibration of predicted survival probabilities. Both types of predictive accuracy should be unbiasedly validated using bootstrapping or cross-validation, before using predictions in a new data series. We discuss some of the hazards of poorly fitted and overfitted regression models and present one modelling strategy that avoids many of the problems discussed. The methods described are applicable to all regression models, but are particularly needed for binary, ordinal, and time-to-event outcomes. Methods are illustrated with a survival analysis in prostate cancer using Cox regression.

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Assessing the performance of prediction models: a framework for traditional and novel measures.

Ewout W. Steyerberg, Andrew J Vickers, Nancy R Cook … (2010)

The performance of prediction models can be assessed using a variety of methods and metrics. Traditional measures for binary and survival outcomes include the Brier score to indicate overall model performance, the concordance (or c) statistic for discriminative ability (or area under the receiver operating characteristic [ROC] curve), and goodness-of-fit statistics for calibration.Several new measures have recently been proposed that can be seen as refinements of discrimination measures, including variants of the c statistic for survival, reclassification tables, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Moreover, decision-analytic measures have been proposed, including decision curves to plot the net benefit achieved by making decisions based on model predictions.We aimed to define the role of these relatively novel approaches in the evaluation of the performance of prediction models. For illustration, we present a case study of predicting the presence of residual tumor versus benign tissue in patients with testicular cancer (n = 544 for model development, n = 273 for external validation).We suggest that reporting discrimination and calibration will always be important for a prediction model. Decision-analytic measures should be reported if the predictive model is to be used for clinical decisions. Other measures of performance may be warranted in specific applications, such as reclassification metrics to gain insight into the value of adding a novel predictor to an established model.

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The individual and socioeconomic impact of osteoarthritis.

Emily Callander, David Hunter, Deborah Schofield (2014)

Osteoarthritis (OA) is a highly prevalent, disabling disease, with a commensurate tremendous individual and socioeconomic burden. This Perspectives article focuses on the burden of OA for the individual, the health-care system and society, to draw attention to the magnitude of the current problem with some reference to projected figures. We have an urgent opportunity to make fundamental changes to the way we care for individuals with OA that will have an effect upon the direct and indirect costs of this disease. By focusing on the burden of this prevalent, disabling, and costly disease, we hope to highlight the opportunity for shifts in health-care policy towards prevention and chronic-disease management.

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Author and article information

Contributors

David J. Hunter:

ORCID: http://orcid.org/0000-0003-3197-752X

David.Hunter@sydney.edu.au

Journal

Journal ID (nlm-ta): Skeletal Radiol

Journal ID (iso-abbrev): Skeletal Radiol

Title: Skeletal Radiology

Publisher: Springer Berlin Heidelberg (Berlin/Heidelberg )

ISSN (Print): 0364-2348

ISSN (Electronic): 1432-2161

Publication date (Electronic): 24 January 2023

Publication date PMC-release: 24 January 2023

Publication date (Print): 2023

Volume: 52

Issue: 11

Pages: 2323-2339

Affiliations

[1 ]GRID grid.412703.3, ISNI 0000 0004 0587 9093, Sydney Musculoskeletal Health, Kolling Institute, Faculty of Medicine, , University of Sydney, Australia and Rheumatology Department, Royal North Shore Hospital, ; St Leonards, NSW 2065 Australia

[2 ]GRID grid.38142.3c, ISNI 000000041936754X, Brigham and Women’s Hospital, , Harvard Medical School, ; Boston, MA USA

[3 ]Foundation for the National Institutes of Health, ( https://ror.org/00k86s890) Bethesda, North, MD USA

[4 ]Duke Molecular Physiology Institute, and Department of Medicine|, Duke University, ( https://ror.org/00py81415) Durham, NC USA

Author information

David J. Hunter http://orcid.org/0000-0003-3197-752X

Article

Publisher ID: 4284

DOI: 10.1007/s00256-023-04284-w

PMC ID: 10509067

PubMed ID: 36692532

SO-VID: 9e80dddd-04fc-4a0b-8f7c-5e6f39b25c06

License:

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

History

Date received : 17 July 2022

Date revision received : 9 January 2023

Date accepted : 12 January 2023

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ScienceOpen disciplines: Radiology & Imaging

Keywords: osteoarthritis,imaging,biomarkers

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ScienceOpen disciplines: Radiology & Imaging

Keywords: osteoarthritis, imaging, biomarkers

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Biomarkers in osteoarthritis: current status and outlook — the FNIH Biomarkers Consortium PROGRESS OA study

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Most cited references 55

Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors.

Assessing the performance of prediction models: a framework for traditional and novel measures.

The individual and socioeconomic impact of osteoarthritis.

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Cited by 2

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