Risk prediction model for critical illness in connective tissue disease-associated interstitial lung disease

This study was to optimize the current methods for identifying and predicting the risk of critical illness in patients with connective tissue disease-associated interstitial lung disease (CTD-ILD).

First, 200 patients diagnosed with CTD-ILD were included, and detailed demographic, serological, and imaging data were collected. Second, a risk identification and prediction framework was constructed based on multivariate logistic regression and machine learning algorithms (random forest (RF) and convolutional neural network (CNN)) to identify significant determinants of critical illness. Finally, the overall performance of each model was evaluated using K-fold cross-validation and external validation procedures. A feature ablation experiment was conducted based on the optimal random forest model to validate the independent contribution of each core predictor.

The results showed that the logistic regression, random forest (RF), and CNN models were all successfully constructed and validated, among which the RF model demonstrated the best overall performance, with an accuracy of 85.7%, an area under the curve (AUC) of 0.88, a sensitivity of 83.5%, and a specificity of 88.2%. The ablation experiment confirmed that each feature had independent predictive value, with the most significant decline in model performance observed after the removal of IL‑6. Among them, the individual AUC value of interleukin-6 (IL-6) reached 0.981. Significant risk factors included patient age, C-reactive protein (CRP) level, presence of honeycomb lung on imaging, and the ratio of arterial oxygen partial pressure to inhaled oxygen concentration (PaO₂/FiO₂).

The model in this study demonstrated satisfactory predictive ability and stability in both internal and external validation phases. The random forest model performed excellently in predicting the likelihood of critical illness in patients with CTD-ILD.