Browsing by Author "Sprong, Matthew Evan"

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    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
    (Frontiers Media, 2025-05-29) Lee, Yu-Sheng; Gor, Kira; Sprong, Matthew Evan; Shrestha, Junu; Huang, Xueli; Hollender, Heaven; Health Sciences, School of Health and Human Sciences
    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.
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    Trajectory Analysis for Identifying Classes of Attention Deficit Hyperactivity Disorder (ADHD) in Children of the United States
    (Bentham Open, 2024-05-21) Lee, Yu-Sheng; Sprong, Matthew Evan; Shrestha, Junu; Smeltzer, Matthew P.; Hollender, Heaven; Health Sciences, School of Health and Human Sciences
    Background: Attention Deficit Hyperactivity Disorder (ADHD) is a mental health disorder that affects attention and behavior. People with ADHD frequently encounter challenges in social interactions, facing issues, like social rejection and difficulties in interpersonal relationships, due to their inattention, impulsivity, and hyperactivity. Methods: A National Longitudinal Survey of Youth (NLSY) database was employed to identify patterns of ADHD symptoms. The children who were born to women in the NLSY study between 1986 and 2014 were included. A total of 1,847 children in the NLSY 1979 cohort whose hyperactivity/inattention score was calculated when they were four years old were eligible for this study. A trajectory modeling method was used to evaluate the trajectory classes. Sex, baseline antisocial score, baseline anxiety score, and baseline depression score were adjusted to build the trajectory model. We used stepwise multivariate logistic regression models to select the risk factors for the identified trajectories. Results: The trajectory analysis identified six classes for ADHD, including (1) no sign class, (2) few signs since preschool being persistent class, (3) few signs in preschool but no signs later class, (4) few signs in preschool that magnified in elementary school class, (5) few signs in preschool that diminished later class, and (6) many signs since preschool being persistent class. The sensitivity analysis resulted in a similar trajectory pattern, except for the few signs since preschool that magnified later class. Children's race, breastfeeding status, headstrong score, immature dependent score, peer conflict score, educational level of the mother, baseline antisocial score, baseline anxious/depressed score, and smoking status 12 months prior to the birth of the child were found to be risk factors in the ADHD trajectory classes. Conclusion: The trajectory classes findings obtained in the current study can (a) assist a researcher in evaluating an intervention (or combination of interventions) that best decreases the long-term impact of ADHD symptoms and (b) allow clinicians to better assess as to which class a child with ADHD belongs so that appropriate intervention can be employed.