Impact of Drug Adherence on Oppositional Defiant Disorder and Conduct Disorder Among Patients With Attention-Deficit/Hyperactivity Disorder

Machine learning-based prediction of persistent oppositional defiant behavior for 5 years

BACKGROUND

Early detection of oppositional defiant behavior is warranted for timely intervention in children at risk. This study aimed to build a predictive model of persistent oppositional defiant behavior based on a machine learning algorithm.

METHODS

With nationwide cohort data collected from 2012 to 2017, a tree-based ensemble model, random forest, was exploited to build a predictive model for persistent oppositional defiant behavior. The persistent oppositional defiant behavior was defined by the presence of oppositional defiant behavior for all the five years. The area under the receiver operating characteristic curve (AUC), overall accuracy, sensitivity, specificity, and Matthew's correlation coefficients (MCC) were measured.

RESULTS

Data of 1,323 children were used for building the machine learning-based predictive model. The baseline mean ± standard deviation month-age of the participants was 51.0 ± 1.2 months. The proportion of persistent oppositional defiant behavior was 0.98% (13/1323). In the hold-out test set, the overall accuracy, AUC, sensitivity, specificity, and MCC were 0.955, 0.982, 1.000, 0.954, and 0.417, respectively.

CONCLUSION

Our study demonstrated that the machine learning-based approach is useful for predicting persistent oppositional defiant behavior in preschool-aged children.

Refill Adherence Measures and Its Association with Economic, Clinical, and Humanistic Outcomes Among Pediatric Patients: A Systematic Review

Although refill adherence measures (RAMs) are widely reviewed on their use among adult patients, existing reviews on adherence among children have only focused on self-report measures and electronic monitoring. Hence, this systematic review aims to examine the use of RAMs and their association with economic, clinical, and humanistic outcomes (ECHO) among pediatric patients. A literature search was conducted in Pubmed, Embase, CINAHL, and PsycINFO. Studies published in English involving subjects aged ≤18 years were included if RAMs were analyzed with ECHO. Of the 35 included studies, the majority (n = 33) were conducted in high-income countries. Asthma was the most common condition (n = 9) studied. Overall, 60.6% of 33 clinical outcomes reported among 22 studies was positive (improved clinical outcomes with improved adherence), while 21.9% of 32 economic outcomes reported among 16 studies was positive (reduced healthcare utilization or cost outcomes with improved adherence). Only four studies evaluated the relationship of adherence with 11 humanistic outcomes, where the majority (72.7%) were considered unclear. RAMs are associated with ECHO and can be considered for use in the pediatric population. Future studies could explore the use of RAMs in low-income countries, and the association of RAMs with quality of life.