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Imaging Features of Systemic Sclerosis-Associated Interstitial Lung Disease
Published on: June 16, 2020
Clinical nomogram assisting in discrimination of juvenile dermatomyositis-associated interstitial lung disease
Minfei Hu1, Chencong Shen1, Fei Zheng1
1Department of Rheumatology Immunology & Allergy Medicine, The Children's Hospital, Zhejiang Univesity School of Medicine, National Clinical Research Center for Child Health, Hangzhou, 310003, PR China.
Insights
A new prediction model using non-invasive clinical features can help early identify juvenile dermatomyositis with interstitial lung disease (JDM-ILD). This model aids in clinical evaluation and predicting long-term prognosis for JDM-ILD patients.
Area of Science:
- Pediatric Rheumatology
- Pulmonology
- Medical Informatics
Background:
- Juvenile dermatomyositis (JDM) is an autoimmune disease that can affect the lungs.
- Interstitial lung disease (ILD) is a serious complication of JDM, impacting prognosis.
- Early and accurate discrimination of JDM-ILD is crucial for timely intervention.
Purpose of the Study:
- To develop a prediction model for early discrimination of JDM-ILD using non-invasive clinical features.
- To establish a tool for risk stratification and prognosis prediction in JDM-ILD.
Main Methods:
- Retrospective analysis of clinical data from pediatric JDM patients.
- Application of machine learning techniques to identify predictive factors.
- Development and validation of a nomogram-based risk prediction model.
Main Results:
- The study included 93 pediatric patients with JDM.
- Factors associated with JDM-ILD included elevated ESR, IL-10 levels, and MDA-5 antibodies.
- The developed nomogram showed favorable discrimination in both discovery (AUC=0.736) and validation (AUC=0.792) cohorts.
- Higher nomogram scores correlated with increased risk of disease progression.
Conclusions:
- The ESIM predictive model-based nomogram is a valuable tool for clinical evaluation of JDM-ILD.
- This model aids in predicting the long-term prognosis of JDM-ILD.
- Non-invasive clinical features can effectively predict JDM-ILD development and progression.
Objective:
To establish a prediction model using non-invasive clinical features for early discrimination of DM-ILD in clinical practice.
Method:
Clinical data of pediatric patients with JDM were retrospectively analyzed using machine learning techniques. The early discrimination model for JDM-ILD was established within a patient cohort diagnosed with JDM at a children's hospital between June 2015 and October 2022.
Results:
A total of 93 children were included in the study, with the cohort divided into a discovery cohort (n = 58) and a validation cohort (n = 35). Univariate and multivariate analyses identified factors associated with JDM-ILD, including higher ESR (OR, 3.58; 95% CI 1.21-11.19, P = 0.023), higher IL-10 levels (OR, 1.19; 95% CI, 1.02-1.41, P = 0.038), positivity for MDA-5 antibodies (OR, 5.47; 95% CI, 1.11-33.43, P = 0.045). A nomogram was developed for risk prediction, demonstrating favorable discrimination in both the discovery cohort (AUC, 0.736; 95% CI, 0.582-0.868) and the validation cohort (AUC, 0.792; 95% CI, 0.585-0.930). Higher nomogram scores were significantly associated with an elevated risk of disease progression in both the discovery cohort (P = 0.045) and the validation cohort (P = 0.017).
Conclusion:
The nomogram based on the ESIM predictive model provides valuable guidance for the clinical evaluation and long-term prognosis prediction of JDM-ILD.
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