A novel risk algorithm predicts interstitial lung disease in systemic sclerosis, suggesting timely HRCTs for better diagnosis.
As a screening tool for the presence of systemic sclerosis-associated interstitial lung disease (SSc-ILD) on high-resolution computed tomography (HRCT) is missing, we aimed to develop the ILD-RISC score, a risk algorithm to guide physicians in ordering HRCTs, with specific focus on follow-up visits. The nominal group technique was used to select items for the multivariable logistic regression with backward selection. The ILD-RISC score was developed from baseline visits of the derivation cohort. After identifying a cut-off favoring sensitivity > 85% from the ROC curve, it was validated in a separate cross-sectional cohort, and then applied longitudinally in a specific SSc cohort with negative baseline HRCT. In the derivation cohort (533 patients), 13 variables associated with the presence of SSc-ILD on HRCT were tested. The ILD-RISC score, including FVC%, DLCO/SB%, digital ulcers ever, age and SSc autoantibodies, showed an area under the curve of 79.1% (75.3-83.0%) for the presence of SSc-ILD on HRCT. An ILD-RISC score≥0.3 had sensitivity 85.6% and specificity 53.6%, as confirmed in the validation cohort (247 patients). Among 819 patients with negative baseline HRCT, 170 developed SSc-ILD: a low ILD-RISC score was detected in almost 50% of the follow-up visits, supporting the sparing of HRCTs. the ILD-RISC score was developed and validated to predict the presence of SSc-ILD Thus, the ILD-RISC score may help to decide when to order HRCTs at follow-up, allowing the reduction of costs and radiation exposure, but also at the time of SSc diagnosis when resources are limited.
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Bruni et al. (2025) studied this question.
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