Mortality Risk Prediction in Patients With Antimelanoma Differentiation–Associated, Gene 5 Antibody–Positive, Dermatomyositis–Associated Interstitial Lung Disease: Algorithm Development and Validation
BackgroundPatients 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...
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JMIR Publications
2025-02-01
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| Series: | Journal of Medical Internet Research |
| Online Access: | https://www.jmir.org/2025/1/e62836 |
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| author | Hui Li Ruyi Zou Hongxia Xin Ping He Bin Xi Yaqiong Tian Qi Zhao Xin Yan Xiaohua Qiu Yujuan Gao Yin Liu Min Cao Bi Chen Qian Han Juan Chen Guochun Wang Hourong Cai |
| author_facet | Hui Li Ruyi Zou Hongxia Xin Ping He Bin Xi Yaqiong Tian Qi Zhao Xin Yan Xiaohua Qiu Yujuan Gao Yin Liu Min Cao Bi Chen Qian Han Juan Chen Guochun Wang Hourong Cai |
| author_sort | Hui Li |
| collection | DOAJ |
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BackgroundPatients 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.
ObjectiveThis 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.
MethodsIn 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.
ResultsAfter 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.
ConclusionsWe 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. |
| format | Article |
| id | doaj-art-9a565c98d1c54de49a6762a7299fbe01 |
| institution | Kabale University |
| issn | 1438-8871 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | JMIR Publications |
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| series | Journal of Medical Internet Research |
| spelling | doaj-art-9a565c98d1c54de49a6762a7299fbe012025-02-05T13:15:34ZengJMIR PublicationsJournal of Medical Internet Research1438-88712025-02-0127e6283610.2196/62836Mortality Risk Prediction in Patients With Antimelanoma Differentiation–Associated, Gene 5 Antibody–Positive, Dermatomyositis–Associated Interstitial Lung Disease: Algorithm Development and ValidationHui Lihttps://orcid.org/0000-0002-4611-7808Ruyi Zouhttps://orcid.org/0009-0006-9121-9965Hongxia Xinhttps://orcid.org/0009-0004-2421-4027Ping Hehttps://orcid.org/0009-0005-5566-8993Bin Xihttps://orcid.org/0009-0000-8843-9402Yaqiong Tianhttps://orcid.org/0000-0002-1936-2082Qi Zhaohttps://orcid.org/0000-0002-6749-6835Xin Yanhttps://orcid.org/0000-0002-2829-3277Xiaohua Qiuhttps://orcid.org/0000-0001-6348-1348Yujuan Gaohttps://orcid.org/0000-0003-0650-3218Yin Liuhttps://orcid.org/0000-0002-6891-1543Min Caohttps://orcid.org/0009-0007-3817-6350Bi Chenhttps://orcid.org/0000-0002-6041-6433Qian Hanhttps://orcid.org/0000-0003-4806-222XJuan Chenhttps://orcid.org/0000-0001-5801-9124Guochun Wanghttps://orcid.org/0000-0002-4616-9376Hourong Caihttps://orcid.org/0000-0001-7618-6436 BackgroundPatients 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. ObjectiveThis 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. MethodsIn 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. ResultsAfter 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. ConclusionsWe 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.https://www.jmir.org/2025/1/e62836 |
| spellingShingle | Hui Li Ruyi Zou Hongxia Xin Ping He Bin Xi Yaqiong Tian Qi Zhao Xin Yan Xiaohua Qiu Yujuan Gao Yin Liu Min Cao Bi Chen Qian Han Juan Chen Guochun Wang Hourong Cai Mortality Risk Prediction in Patients With Antimelanoma Differentiation–Associated, Gene 5 Antibody–Positive, Dermatomyositis–Associated Interstitial Lung Disease: Algorithm Development and Validation Journal of Medical Internet Research |
| title | Mortality Risk Prediction in Patients With Antimelanoma Differentiation–Associated, Gene 5 Antibody–Positive, Dermatomyositis–Associated Interstitial Lung Disease: Algorithm Development and Validation |
| title_full | Mortality Risk Prediction in Patients With Antimelanoma Differentiation–Associated, Gene 5 Antibody–Positive, Dermatomyositis–Associated Interstitial Lung Disease: Algorithm Development and Validation |
| title_fullStr | Mortality Risk Prediction in Patients With Antimelanoma Differentiation–Associated, Gene 5 Antibody–Positive, Dermatomyositis–Associated Interstitial Lung Disease: Algorithm Development and Validation |
| title_full_unstemmed | Mortality Risk Prediction in Patients With Antimelanoma Differentiation–Associated, Gene 5 Antibody–Positive, Dermatomyositis–Associated Interstitial Lung Disease: Algorithm Development and Validation |
| title_short | Mortality Risk Prediction in Patients With Antimelanoma Differentiation–Associated, Gene 5 Antibody–Positive, Dermatomyositis–Associated Interstitial Lung Disease: Algorithm Development and Validation |
| title_sort | mortality risk prediction in patients with antimelanoma differentiation associated gene 5 antibody positive dermatomyositis associated interstitial lung disease algorithm development and validation |
| url | https://www.jmir.org/2025/1/e62836 |
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