Damage indices in rheumatic diseases: A systematic review of the literature

Abstract

Objective

To review the current literature, and evaluate the psychometric properties of disease damage indices in rheumatic diseases.

Methods

A search of Medline, EMBASE, and Cochrane Library databases was performed to June 2018 to identify damage indices in all systemic rheumatic diseases. Articles were included in a systematic review if indices were composite (multi-organ) in nature and if adequate detail on methodology was described. Articles pertaining to the validation of these indices were also reviewed in order to assess the psychometric properties of the indices using the Outcome Measures in Rheumatology Arthritis Clinical Trials (OMERACT) filter as a guide.

Results

Of the 2659 articles retrieved through the search, we identified 7 damage indices in five diseases: idiopathic inflammatory myopathy, systemic lupus erythematosus, systemic vasculitis, Sjӧgren's syndrome and antiphospholipid syndrome. A further 48 articles were identified pertaining to the validation of these damage indices. The methodological process for the development of these indices included expert consensus, item reduction and item weighting methods. The level of validation that these indices have achieved is variable, with only 2 damage indices fulfilling all criteria of the OMERACT filter.

Conclusions

To date, there have been 7 composite disease damage indices created in a variety of rheumatic diseases, with the exception of systemic sclerosis (SSc). This review has informed methodology for the development of a disease damage index in SSc.

Introduction

The need for validated outcome measures to assess the status of a disease in an individual patient is an essential part of conducting clinical trials. With the emergence of disease modifying treatments that have the ability to curtail accumulation of damage over time, the need for clinical tools that measure damage in order to guide clinical care, and for use as outcome measures in clinical trials is greater than ever. Well developed instruments to quantify damage in rheumatic diseases are also important for differentiating damage from activity in enrichment of clinical trials for patients who do not have ample pre-existing organ damage and therefore are less likely to report improvement. The methodological process with which these disease-specific outcome measures have been developed varies greatly, resulting in tools with a wide variety of face, content and construct validity.

The purpose of this systematic review was to outline the methodology used to develop composite damage indices in rheumatic diseases, and to evaluate the psychometric properties of these indices, in order to inform the methodology used to develop the Scleroderma Clinical Trials Consortium Damage Index (SCTC-DI). The SCTC-DI will be the first damage index developed to assess organ damage in systemic sclerosis (SSc) in the clinical trial setting, as a primary outcome measure, or to enrich patient selection. Other potential uses of the SCTC-DI include use as a descriptive tool to measure disease burden and to compare cohorts.

The damage indices of rheumatic diseases included in this systematic review are composite in nature, meaning that multiple organ manifestations of the disease are assessed in a single index as opposed to only one or a few disease manifestations. For inclusion in this systematic review, articles also needed to include details of the development and validation of the index.

The Outcome Measures in Rheumatology Arthritis Clinical Trials (OMERACT) is an initiative to develop methodologically sound outcome measures in rheumatology. The OMERACT ‘filter’ is a set of guidelines for the assessment of validity of rheumatology outcome measures. The OMERACT filter is governed by three main concepts; (1) truth – is the measure truthful, and does it measure what is intended? Is the result unbiased and relevant? ‘Truth’ captures issues of face, content, construct, and criterion validity; (2) discrimination – does the measure discriminate between situations of interest? The situations can be states at one time (for classification or prognosis) or states at different times (to measure change). ‘Discrimination’ captures issues of reliability and sensitivity to change and; (3) feasibility – can the measure be applied easily, given constraints of time, money, or interpretability? Feasibility is an essential element in the selection of measures and may be decisive in determining a measure's success when applied in a real life setting [1].