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A feasibility study of the childhood depression medication algorithm: the Texas Children's Medication Algorithm
Graham J Emslie1, Carroll W Hughes, M Lynn Crismon
1University of Texas Southwestern Medical Center-Dallas, TX 75390-8589, USA. Graham.Emslie@UTSouthwestern.edu
Insights
Algorithm-driven disease management (ALGO) improved depression and ADHD symptoms in youth. This program showed greater clinical response and function improvements compared to a historical control group.
Area of Science:
- Child and Adolescent Psychiatry
- Mental Health Services Research
- Digital Health Interventions
Background:
- Community mental health centers face challenges in managing pediatric depression and ADHD.
- Standardized treatment approaches are needed to improve clinical outcomes for youth with these conditions.
Purpose of the Study:
- To assess the feasibility and impact of an algorithm-driven disease management program (ALGO) for children and adolescents with depression, with or without ADHD.
- To evaluate ALGO's effect on clinical response and functional outcomes in a community mental health setting.
Main Methods:
- The ALGO program integrated medication algorithms, physician support, outcome documentation, and patient/family psychoeducation.
- A quasi-experimental design compared 39 children treated with ALGO to a historical chart cohort of 114 children.
- Outcomes measured included clinical symptoms (Children's Depression Rating Scale-Revised), functioning (Child Adolescent Functioning Assessment Scale), and global improvement (Clinical Global Impression Scale) over 4 months.
Main Results:
- ALGO treatment significantly reduced depression severity scores (48.2 to 32.5, p <.0005) and improved functioning scores (70.3 to 40.9, p <.0005).
- Global improvement scores showed a greater decrease in the ALGO group (5.7 to 3.7) compared to the control group (5.8 to 4.8, p <.003).
Conclusions:
- Algorithm-driven disease management (ALGO) demonstrated clear improvements in clinical symptoms, functioning, and global response in children and adolescents.
- The ALGO program yielded superior outcomes compared to a historical cohort, supporting its potential for pediatric mental health care.
- Further controlled studies are warranted to examine the effects of ALGO on clinical outcomes in larger pediatric populations.
Objective:
To evaluate the feasibility and impact on clinical response and function associated with the use of an algorithm-driven disease management program (ALGO) for children and adolescents treated for depression with or without attention-deficit/hyperactivity disorder (ADHD) in community mental health centers.
Method:
Interventions included (1). medication algorithms, (2). clinical and technical support for the physician, (3). uniform chart documentation of outcomes, and (4). a patient/family psychoeducation program. Children eligible for entry into the study were referred to the child psychiatrist for initiation or change in medicine. Outcomes of treatment with the ALGO for up to 4 months are presented. Measures of change included clinical symptoms, functioning, and global improvement (Clinical Global Impression Scale). A historical chart cohort from the same clinics was used as a quasi-control.
Results:
Thirty-nine individuals (depression = 24; comorbid depression with ADHD = 15) were enrolled for treatment with ALGO. One hundred fourteen children were in the control cohort (74 depressed, 40 comorbid). For the ALGO groups, Children's Depression Rating Scale-Revised depression severity scores decreased from 48.2 to 32.5 and Child Adolescent Functioning Assessment Scale function scores improved from 70.3 to 40.9 (all p < or =.0005). Clinical Global Impression Scale severity scores decreased from 5.7 to 3.7 in ALGO compared to only 5.8 to 4.8 in the control (p <.003).
Conclusions:
There was clear improvement in clinical symptoms, functioning, and global response with ALGO treatment. The magnitude of the improvement was greater in children and adolescents treated with the ALGO program compared with a historical cohort. These data support the need for controlled studies in larger populations examining the effects of algorithm-driven disease management programs on the clinical outcomes of children with mental illness.
