Development of a multivariable prediction model for progression of systemic sclerosis-associated interstitial lung disease

OBJECTIVE To develop a multivariable model for predicting the progression of systemic sclerosis-associated interstitial lung disease (SSc-ILD) over 52 weeks. METHODS We used logistic regression models to analyse associations between candidate predictors assessed at baseline and progression of SSc-ILD (absolute decline in forced vital capacity (FVC) % predicted >5% or death) over 52 weeks in the placebo group of the SENSCIS trial. Analyses were performed in the overall placebo group and in a subgroup with early and/or inflammatory SSc and/or severe skin fibrosis (<18 months since first non-Raynaud symptom, elevated inflammatory markers, and/or modified Rodnan skin score (mRSS) >18) at baseline. Model performance was assessed using the area under the receiver operating characteristic curve (AUC). RESULTS In the overall placebo group (n=288), the performance of the final multivariable model for predicting SSc-ILD progression was moderate (apparent AUC: 0.63). A stronger model, with an apparent AUC of 0.75, was developed in the subgroup with early and/or inflammatory SSc and/or severe skin fibrosis at baseline (n=155). This model included diffusing capacity of the lung for carbon monoxide (DLco) % predicted, time since first non-Raynaud symptom, mRSS, anti-topoisomerase I antibody status and mycophenolate use. CONCLUSION Prediction of the progression of SSc-ILD may require different approaches in distinct subgroups of patients. Among patients with SSc-ILD and early and/or inflammatory SSc and/or severe skin fibrosis, a nomogram based on a multivariable model may be of value for identifying patients at risk of short-term progression.