Related Experiment Video
Updated: May 29, 2025
Imaging Features of Systemic Sclerosis-Associated Interstitial Lung Disease
Published on: June 16, 2020
Mortality Risk Prediction in Patients With Antimelanoma Differentiation-Associated, Gene 5 Antibody-Positive,
Hui Li1,2, Ruyi Zou1, Hongxia Xin3
1Department of Respiratory and Critical Care Medicine, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
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, erythrocyte sedimentation rate (ESR), serum albumin (ALB) level, age, C-reactive protein (CRP) level, aspartate aminotransferase (AST) level, lactate dehydrogenase (LDH) level, and the neutrophil-to-lymphocyte ratio (NLR). LR was chosen as the best algorithm for model construction, and the model demonstrated excellent performance, with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.866, a sensitivity of 84.8%, and a specificity of 84.4% on the validation data set and an AUC of 0.90, a sensitivity of 85.0%, and a specificity of 83.9% on the training data set. Calibration curves and decision curve analysis (DCA) confirmed the model's accuracy and clinical applicability. Moreover, the model showed strong predictive performance in the external validation cohorts (cohort 1: AUC=0.836, 95% CI 0.754-0.916; cohort 2: AUC=0.915, 95% CI 0.871-0.959), indicating good generalizability. This model was integrated into a web-based tool to predict the 3-month mortality for patients with anti-MDA5+DM-ILD.
Conclusions:
We successfully developed a robust clinical prediction model and an accompanying web tool to estimate the 3-month mortality risk for patients with anti-MDA5+DM-ILD.
Insights
Patients with anti-MDA5+DM-ILD face high mortality risk. This study developed a machine learning model to predict 3-month mortality, offering a valuable tool for risk assessment.
Area of Science:
- Medical research
- Machine learning applications in healthcare
- Pulmonology
Background:
- Antimelanoma differentiation-associated gene 5 antibody-positive dermatomyositis-associated interstitial lung disease (anti-MDA5+DM-ILD) is linked to rapidly progressive interstitial lung disease (RP-ILD) and significant mortality.
- A reliable and accessible prediction model is crucial for evaluating mortality risk in these patients.
Purpose of the Study:
- To develop and validate a machine learning-based risk prediction model for 3-month mortality in anti-MDA5+DM-ILD patients.
- To create an easy-to-use web-based tool for assessing this mortality risk.
Main Methods:
- Retrospective enrollment of 509 patients with anti-MDA5+DM-ILD from 6 Chinese hospitals.
- Application of six machine learning algorithms (XGBoost, LR, LightGBM, RF, SVM, KNN) to construct and evaluate the prediction model.
- Identification of key predictive variables including RP-ILD, ESR, ALB, age, CRP, AST, LDH, and NLR.
Main Results:
- Logistic regression (LR) was selected as the optimal algorithm, demonstrating excellent performance with an AUC of 0.866 in validation and 0.90 in training datasets.
- The model showed strong generalizability with high AUC values in external validation cohorts (0.836 and 0.915).
- A web-based tool was developed integrating the prediction model for practical clinical use.
Conclusions:
- A robust clinical prediction model for 3-month mortality in anti-MDA5+DM-ILD patients was successfully developed.
- The accompanying web tool provides an accessible method for risk assessment and clinical decision-making.
