⚡ Quick Summary
A recent study developed a machine learning-based predictive nomogram for screening children with juvenile idiopathic arthritis (JIA), analyzing data from 223,195 children in the United States. The nomogram demonstrated excellent predictive performance, with an AUC of 0.9130 in the training set, highlighting its potential utility in early diagnosis.
🔍 Key Details
- 📊 Dataset: 223,195 children aged 0 to 17 from the National Survey of Children’s Health (NSCH) database (2016-2021)
- 🧩 Features used: 16 variables selected through LASSO and stepwise logistic regression
- ⚙️ Technology: LASSO logistic regression and stepwise logistic regression
- 🏆 Performance: AUC of 0.9130 in training set, 0.8798 in validation set
🔑 Key Takeaways
- 📊 JIA prevalence ranges from 12.8 to 45 per 100,000 children, making early detection crucial.
- 💡 Machine learning techniques were effectively utilized to create a predictive nomogram.
- 👩🔬 The study analyzed a large dataset, providing robust results for JIA screening.
- 🏆 The nomogram achieved an AUC of 0.9130, indicating strong predictive capabilities.
- 🤖 Sensitivity and specificity were 79.1% and 90.2% in the training set, respectively.
- 🌍 Tools available for parents and health professionals to estimate JIA risk.
- 🆔 Potential biases in selection should be considered when using the nomogram.
📚 Background
Juvenile idiopathic arthritis (JIA) is a common chronic rheumatological condition affecting children, characterized by symptoms such as joint pain and swelling. Diagnosing JIA can be particularly challenging due to symptom overlap with other conditions, necessitating innovative approaches for early detection and management.
🗒️ Study
This study utilized the National Survey of Children’s Health (NSCH) database, which encompasses data from all 50 states and the District of Columbia. By analyzing a substantial cohort of 223,195 children, researchers aimed to develop a predictive nomogram for JIA using advanced statistical techniques, including LASSO logistic regression.
📈 Results
The study identified 555 cases of JIA within the dataset, translating to a prevalence of 248.7 per 100,000 children. The LASSO model exhibited excellent discrimination, achieving an AUC of 0.9002 in the training set and 0.8639 in the validation set. The regression model further improved performance, reaching an AUC of 0.9130 in the training set.
🌍 Impact and Implications
The development of these nomograms represents a significant advancement in the early prediction of JIA in children. By providing accessible tools for parents and healthcare professionals, this study aims to enhance the accuracy of JIA risk estimation, ultimately leading to improved outcomes for affected children. The integration of machine learning in pediatric rheumatology could pave the way for more personalized and timely interventions.
🔮 Conclusion
This study highlights the transformative potential of machine learning in the realm of pediatric healthcare, particularly for conditions like JIA. The predictive nomograms developed offer a promising tool for early detection, which is crucial for effective management. Continued research and refinement of these models could further enhance their applicability and reliability in clinical settings.
💬 Your comments
What are your thoughts on the use of machine learning for early detection of juvenile idiopathic arthritis? We invite you to share your insights and engage in a discussion! 💬 Leave your comments below or connect with us on social media:
Development of a machine learning-based predictive nomogram for screening children with juvenile idiopathic arthritis: a pseudo-longitudinal study of 223,195 children in the United States.
Abstract
BACKGROUND: Juvenile idiopathic arthritis (JIA) is a prevalent chronic rheumatological condition in children, with reported prevalence ranging from 12. 8 to 45 per 100,000 and incidence rates from 7.8 to 8.3 per 100,000 person-years. The diagnosis of JIA can be challenging due to its symptoms, such as joint pain and swelling, which can be similar to other conditions (e.g., joint pain can be associated with growth in children and adolescents).
METHODS: The National Survey of Children’s Health (NSCH) database (2016-2021) of the United States was used in the current study. The NSCH database is funded by the Health Resources and Services Administration and Child Health Bureau and surveyed in all 50 states plus the District of Columbia. A total of 223,195 children aged 0 to 17 were analyzed in this study. A least absolute shrinkage and selection operator (LASSO) logistic regression and stepwise logistic regression were used to select the predictors, which were used to create the nomograms to predict JIA.
RESULTS: A total of 555 (248.7 per 100,000) JIA cases were reported in the NSCH. In the LASSO model, the receiver operating characteristic curve demonstrated excellent discrimination, with an area under the curve (AUC) of 0.9002 in the training set and 0.8639 in the validation set. Of the 16 variables selected by LASSO, 13 overlapped with those from the stepwise model. The regression achieved an AUC of 0.9130 in the training set and 0.8798 in the validation set. Sensitivity, specificity, and accuracy were 79.1%, 90.2%, and 90.2% in the training set, and 69.0%, 90.9%, and 90.8% in the validation set.
DISCUSSION: Using two well-validated predictor models, we developed nomograms for the early prediction of JIA in children based on the NSCH database. The tools are also available for parents and health professionals to utilize these nomograms. Our easy-to-use nomograms are not intended to replace the standard diagnostic methods. Still, they are designed to assist parents, clinicians, and researchers in better-estimating children’s potential risk of JIA. We advise individuals utilizing our nomogram model to be mindful of potential pre-existing selection biases that may affect referrals and diagnoses.
Author: [‘Lee YS’, ‘Gor K’, ‘Sprong ME’, ‘Shrestha J’, ‘Huang X’, ‘Hollender H’]
Journal: Front Public Health
Citation: Lee YS, et al. Development of a machine learning-based predictive nomogram for screening children with juvenile idiopathic arthritis: a pseudo-longitudinal study of 223,195 children in the United States. Development of a machine learning-based predictive nomogram for screening children with juvenile idiopathic arthritis: a pseudo-longitudinal study of 223,195 children in the United States. 2025; 13:1531764. doi: 10.3389/fpubh.2025.1531764