Risk Prediction Model for Critical Illness in Connective Tissue Disease-associated Interstitial Lung Disease

OBJECTIVE: 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). METHODS: 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. CONCLUSION: 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.