The role of multicriteria decision analysis in the development of candidate classification criteria for antisynthetase syndrome: analysis from the CLASS project

Abstract

Objectives: To develop and evaluate the performance of multicriteria decision analysis (MCDA)-driven candidate classification criteria for antisynthetase syndrome (ASSD).

Methods: A list of variables associated with ASSD was developed using a systematic literature review and then refined into an ASSD key domains and variables list by myositis and interstitial lung disease (ILD) experts. This list was used to create preferences surveys in which experts were presented with pairwise comparisons of clinical vignettes and asked to select the case that was more likely to represent ASSD. Experts' answers were analysed using the Potentially All Pairwise RanKings of all possible Alternatives method to determine the weights of the key variables to formulate the MCDA-based classification criteria. Clinical vignettes scored by the experts as consensus cases or controls and real-world data collected in participating centres were used to test the performance of candidate classification criteria using receiver operating characteristic curves and diagnostic accuracy metrics.

Results: Positivity for antisynthetase antibodies had the highest weight for ASSD classification. The highest-ranked clinical manifestation was ILD, followed by myositis, mechanic's hands, joint involvement, inflammatory rashes, Raynaud phenomenon, fever, and pulmonary hypertension. The candidate classification criteria achieved high areas under the curve when applied to the consensus cases and controls and real-world patient data. Sensitivities, specificities, and positive and negative predictive values were >80%.

Conclusions: The MCDA-driven candidate classification criteria were consistent with published ASSD literature and yielded high accuracy and validity.