Abstract

IntroductionAnalyses of large clinical datasets from early arthritis cohorts permit the development of algorithms that may be used for outcome prediction in individual patients. The value added by routine use of musculoskeletal ultrasound (MSUS) in an early arthritis setting, as a component of such predictive algorithms, remains to be determined.MethodsThe authors undertook a retrospective analysis of a large, true-to-life, observational inception cohort of early arthritis patients in Newcastle upon Tyne, UK, which included patients with inflammatory arthralgia but no clinically swollen joints. A pragmatic, 10-minute MSUS assessment protocol was developed, and applied to each of these patients at baseline. Logistic regression was used to develop two "risk metrics" that predicted the development of a persistent inflammatory arthritis (PIA), with or without the inclusion of MSUS parameters.ResultsA total of 379 enrolled patients were assigned definitive diagnoses after ≥12 months follow-up (median 28 months), of whom 162 (42%) developed a persistent inflammatory arthritis. A risk metric derived from 12 baseline clinical and serological parameters alone had an excellent discriminatory utility with respect to an outcome of PIA (area under receiver operator characteristic (ROC) curve 0.91; 95% CI 0.88 to 0.94). The discriminatory utility of a similar metric, which incorporated MSUS parameters, was not significantly superior (area under ROC curve 0.91; 95% CI 0.89 to 0.94). Neither did this approach identify an added value of MSUS over the use of routine clinical parameters in an algorithm for discriminating PIA patients whose outcome diagnosis was rheumatoid arthritis (RA).ConclusionsMSUS use as a routine component of assessment in an early arthritis clinic did not add substantial discriminatory value to a risk metric for predicting PIA.

Highlights

  • Analyses of large clinical datasets from early arthritis cohorts permit the development of algorithms that may be used for outcome prediction in individual patients

  • Analyses of large clinical datasets from early arthritis cohorts permit the development of algorithms that may be used for outcome prediction in individuals - an approach which yielded a validated “prediction rule” for use in undifferentiated arthritis (UA) patients, in which a range of baseline clinical and laboratory parameters are weighted and combined to yield a score that relates to rheumatoid arthritis (RA) progression risk [7,8,9]

  • early arthritis (EA) patient cohort and univariate analysis of baseline characteristics A total of 389 eligible patients were recruited between September 2006 and April 2009 inclusive, and were followed up for a minimum of 12 months; 10 had arthritis that remained undifferentiated at the end of the follow-up period, and were excluded from analysis

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Summary

Introduction

Analyses of large clinical datasets from early arthritis cohorts permit the development of algorithms that may be used for outcome prediction in individual patients. Analyses of large clinical datasets from early arthritis cohorts permit the development of algorithms that may be used for outcome prediction in individuals - an approach which yielded a validated “prediction rule” for use in UA patients, in which a range of baseline clinical and laboratory parameters are weighted and combined to yield a score that relates to RA progression risk [7,8,9]. Musculoskeletal ultrasound has shown promise as an evaluation tool in the setting of early arthritis [13,14], but the value it adds to a thorough clinical assessment, for example, as a component of a predictive algorithm, remains to be quantified

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