The prognosis of progressive pulmonary fibrosis (PPF) associated with connective tissue disease (CTD) is dismal; however, the factors predicting its development remain incompletely understood. Here, we aimed to obtain data on possible risk factors for CTD-PPF and create a nomogram prediction model to facilitate timely treatment and prevention.

The prognosis of progressive pulmonary fibrosis (PPF) associated with connective tissue disease (CTD) is dismal; however, the factors predicting its development remain incompletely understood. Here, we aimed to obtain data on possible risk factors for CTD-PPF and create a nomogram prediction model to facilitate timely treatment and prevention.

 We included 108 individuals with CTD-associated interstitial lung disease identified between 2016 and 2023. To identify independent predictors, univariate and multivariate logistic regression analyses were performed using baseline serum markers, imaging characteristics, and pulmonary function parameters. A nomogram known as the serum-imaging-pulmonary function score (SIP-SCORE) was created by combining these variables. The model's performance was validated using the C-index, 1000 bootstrap resampling calibration curves, and five-fold cross-validation, with the area under the curve of the receiver operating characteristic (AUC) assessing discriminative ability.

The prognosis of progressive pulmonary fibrosis (PPF) associated with connective tissue disease (CTD) is dismal; however, the factors predicting its development remain incompletely understood. Here, we aimed to obtain data on possible risk factors for CTD-PPF and create a nomogram prediction model to facilitate timely treatment and prevention.

The prognosis of progressive pulmonary fibrosis (PPF) associated with connective tissue disease (CTD) is dismal; however, the factors predicting its development remain incompletely understood. Here, we aimed to obtain data on possible risk factors for CTD-PPF and create a nomogram prediction model to facilitate timely treatment and prevention.

Independent factors associated with CTD-PPF included elevated serum ferritin (odds ratio [OR]: 1.003, P = 0.027) and haemoglobin (OR: 1.184, P = 0.024), elevated neutrophil count (OR: 2.745, P = 0.016), reduced forced vital capacity (OR: 0.001, P = 0.038), lower vitamin D levels (OR: 0.731, P = 0.018), and the presence of usual interstitial pneumonia (OR: 6.137, P = 0.024). The SIP-SCORE demonstrated strong discriminative performance, with a C-index of 0.895 and an AUC of 0.858.

The prognosis of progressive pulmonary fibrosis (PPF) associated with connective tissue disease (CTD) is dismal; however, the factors predicting its development remain incompletely understood. Here, we aimed to obtain data on possible risk factors for CTD-PPF and create a nomogram prediction model to facilitate timely treatment and prevention.

The SIP-SCORE, a novel nomogram integrating serum biomarkers, imaging features, and pulmonary function was validated in predicting CTD-PPF. This practical tool enables personalized risk assessment, facilitating earlier intervention and improved prevention strategies.