Prediction of long-term mortality by using machine learning models in Chinese patients with connective tissue
1Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, The People's Republic of China.
Insights
A new nomogram accurately predicts 3- and 5-year mortality in Chinese patients with connective tissue disease-associated interstitial lung disease (CTD-ILD). This machine learning tool outperforms the existing ILD-GAP model for risk assessment in CTD-ILD management.
Area of Science:
- Pulmonary Medicine
- Medical Informatics
- Rheumatology
Background:
- Accurate risk assessment is vital for managing connective tissue disease-associated interstitial lung disease (CTD-ILD).
- Existing models like ILD-GAP may not be optimal for Chinese CTD-ILD populations.
- Machine learning offers a novel approach to improve mortality prediction.
Purpose of the Study:
- To develop and validate a predictive nomogram for 3- and 5-year mortality in Chinese CTD-ILD patients.
- To compare the performance of the developed nomogram against the ILD-GAP model.
- To enhance risk stratification and clinical decision-making for CTD-ILD.
Main Methods:
- A cohort of 675 Chinese CTD-ILD patients was analyzed.
- Cox regression with LASSO was used for feature selection and nomogram generation.
- Internal validation included bootstrap resampling, likelihood ratio testing, Harrell's C index, IDI, NRI, and decision curve analysis.
Main Results:
- A nomogram incorporating 9 variables (age, rheumatoid arthritis, DLCO, RV diameter, RA area, honeycombing, immunosuppressants, AST, albumin) was developed.
- The nomogram demonstrated superior predictive accuracy (C-index 0.767) compared to ILD-GAP (C-index 0.652).
- The nomogram significantly improved discrimination (IDI, NRI) and clinical utility over the ILD-GAP model.
Conclusions:
- The developed nomogram is a promising tool for predicting mortality in Chinese CTD-ILD patients.
- The ILD-GAP model may be less applicable to this specific population.
- Further external validation is recommended to confirm the nomogram's clinical utility.
Background:
The exact risk assessment is crucial for the management of connective tissue disease-associated interstitial lung disease (CTD-ILD) patients. In the present study, we develop a nomogram to predict 3‑ and 5-year mortality by using machine learning approach and test the ILD-GAP model in Chinese CTD-ILD patients.
Methods:
CTD-ILD patients who were diagnosed and treated at the First Affiliated Hospital of Zhengzhou University were enrolled based on a prior well-designed criterion between February 2011 and July 2018. Cox regression with the least absolute shrinkage and selection operator (LASSO) was used to screen out the predictors and generate a nomogram. Internal validation was performed using bootstrap resampling. Then, the nomogram and ILD-GAP model were assessed via likelihood ratio testing, Harrell's C index, integrated discrimination improvement (IDI), the net reclassification improvement (NRI) and decision curve analysis.
Results:
A total of 675 consecutive CTD-ILD patients were enrolled in this study, during the median follow-up period of 50 (interquartile range, 38-65) months, 158 patients died (mortality rate 23.4%). After feature selection, 9 variables were identified: age, rheumatoid arthritis, lung diffusing capacity for carbon monoxide, right ventricular diameter, right atrial area, honeycombing, immunosuppressive agents, aspartate transaminase and albumin. A predictive nomogram was generated by integrating these variables, which provided better mortality estimates than ILD-GAP model based on the likelihood ratio testing, Harrell's C index (0.767 and 0.652 respectively) and calibration plots. Application of the nomogram resulted in an improved IDI (3- and 5-year, 0.137 and 0.136 respectively) and NRI (3- and 5-year, 0.294 and 0.325 respectively) compared with ILD-GAP model. In addition, the nomogram was more clinically useful revealed by decision curve analysis.
Conclusions:
The results from our study prove that the ILD-GAP model may exhibit an inapplicable role in predicting mortality risk in Chinese CTD-ILD patients. The nomogram we developed performed well in predicting 3‑ and 5-year mortality risk of Chinese CTD-ILD patients, but further studies and external validation will be required to determine the clinical usefulness of the nomogram.
