Risk prediction modelling in idiopathic inflammatory myositis-associated interstitial lung disease based on seven factors including serum KL-6 and lung ultrasound B-lines.

Objectives: To develop a user-friendly nomogram-based predictive model for interstitial lung disease (ILD) in patients with idiopathic inflammatory myositis (IIM).

Methods: A retrospective study was conducted at Shantou Central Hospital, encompassing 205 IIM patients diagnosed between January 2013 and December 2022. We used the LASSO regression method in the discovery set to select features for model construction, followed by efficacy verification through AUC of ROC. Afterwards, KL-6 values and LUS B-lines number were added into this model to evaluate whether these 2 factors added to the model efficiency. Finally, a web version was constructed to make it more available.

Results: Among the 205 IIM patients, 115 (56.1%) patients were diagnosed with ILD, and 90 (43.9%) did not. The predictive model, derived from the training set, comprised four independent risk factors, including age, presence of respiratory symptoms, anti-melanoma differentiation-associated gene 5 (MDA-5) antibody positivity, and anti-aminoacyl transfer RNA synthetase (anti-ARS) antibodies positivity. Notably, anti-TIF1-γ antibody positivity emerged as a protective factor. The AUC of the ROC based on these 5 factors was 0.876 in the training set and 0.861 in the validation set. The AUC of the ROC based on the 5 factors plus KL-6 was 0.922, 5 factors plus B-line number was 0.949 and 5 factors plus both KL-6 and B-line number was 0.951. Accordingly, a nomogram and a web version were developed.

Conclusions: This predictive model demonstrates robust capability to assess ILD risk in IIM patients, particularly when augmented with serum KL-6 level or/and LUS B-line number.