Mortality Risk Prediction in Patients With Antimelanoma Differentiation–Associated, Gene 5 Antibody–Positive, Dermatomyositis–Associated Interstitial Lung Disease: Algorithm Development and Validation

作者：Hui Li, Ruyi Zou, H. Xin, Ping He, Bin Xi, Yaqiong Tian, Qi Zhao, Xin Yan, Xiaohua Qiu, Yujuan Gao, Liu Yin, Min Cao, Bi Chen, Qian Han, Juan Chen, Guochun Wang, Hourong Cai · 发表于：Journal of Medical Internet Research · 年份：2025 · DOI：10.2196/62836 · 被引用次数：7 · 研究领域：Inflammatory Myopathies and Dermatomyositis、Systemic Sclerosis and Related Diseases、Interstitial Lung Diseases and Idiopathic Pulmonary Fibrosis

BACKGROUND: Patients with antimelanoma differentiation-associated gene 5 antibody-positive dermatomyositis-associated interstitial lung disease (anti-MDA5+DM-ILD) are susceptible to rapidly progressive interstitial lung disease (RP-ILD) and have a high risk of mortality. There is an urgent need for a reliable prediction model, accessible via an easy-to-use web-based tool, to evaluate the risk of death. OBJECTIVE: This study aimed to develop and validate a risk prediction model of 3-month mortality using machine learning (ML) in a large multicenter cohort of patients with anti-MDA5+DM-ILD in China. METHODS: In total, 609 consecutive patients with anti-MDA5+DM-ILD were retrospectively enrolled from 6 hospitals across China. Patient demographics and laboratory and clinical parameters were collected on admission. The primary endpoint was 3-month mortality due to all causes. Six ML algorithms (Extreme Gradient Boosting [XGBoost], logistic regression (LR), Light Gradient Boosting Machine [LightGBM], random forest [RF], support vector machine [SVM], and k-nearest neighbor [KNN]) were applied to construct and evaluate the model. RESULTS: After applying inclusion and exclusion criteria, 509 (83.6%) of the 609 patients were included in our study, divided into a training cohort (n=203, 39.9%), an internal validation cohort (n=51, 10%), and 2 external validation cohorts (n=92, 18.1%, and n=163, 32%). ML identified 8 important variables as critical for model construction: RP-ILD, erythroc...