Derivation and Validation of a New Disease Activity Assessment Tool With Higher Accuracy for Takayasu Arteritis

ObjectiveTo develop a new disease activity assessment tool with high accuracy for Takayasu arteritis. MethodsIndividual items from National Institute of Health (NIH) criteria and the Indian Takayasu Clinical Activity Score (ITAS2010) were tested as candidate variables to develop a new disease activity assessment tool in a derivation cohort (N = 100). Physician global assessment on disease activity was used as the gold standard. Multivariable logistic regression models were constructed and the model with the highest accuracy was identified. A formula assessing disease activity was generated using simplified beta coefficients (rounded to decimal place). Diagnostic performance was evaluated through estimating the area under the curve (AUC). The new assessment tool was subsequently validated in a validation cohort (N = 46). ResultsThe multivariable model yielding the highest accuracy consisted of a high erythrocyte sedimentation rate (ESR), NIH criteria 1 and 4, and carotidynia. Using simplified beta coefficients, the following disease activity assessment tool was developed: high ESR (3 points), NIH criterion 1 (2 points), NIH criterion 4 (4 points), and carotidynia (3 points) (total score >= 5, active; total score <5, inactive). The new disease activity assessment tool had a higher AUC (89.37) for discriminating active and inactive diseases than NIH criteria (AUC 77.96), ITAS2010 (AUC 66.12), ITAS-ESR (AUC 75.58), and ITAS-C-reactive protein (AUC 71.34). The AUC (85.23) of the new assessment tool was similar in the validation cohort. ConclusionA new disease activity assessment tool that consists of high ESR, NIH criteria 1 and 4, and carotidynia had higher accuracy in discriminating active and inactive disease than currently used clinical assessment tools.

Automated summary

A new disease activity assessment tool comprising high ESR, NIH criteria 1 and 4, and carotidynia demonstrated higher accuracy in discriminating active and inactive disease compared to currently used clinical assessment tools.