Identifying the best treatment for young people with depression

Evidence for machine learning guided early prediction of acute outcomes in the treatment of depressed children and adolescents with antidepressants

BACKGROUND

The treatment of depression in children and adolescents is a substantial public health challenge. This study examined artificial intelligence tools for the prediction of early outcomes in depressed children and adolescents treated with fluoxetine, duloxetine, or placebo.

METHODS

The study samples included training datasets (N = 271) from patients with major depressive disorder (MDD) treated with fluoxetine and testing datasets from patients with MDD treated with duloxetine (N = 255) or placebo (N = 265). Treatment trajectories were generated using probabilistic graphical models (PGMs). Unsupervised machine learning identified specific depressive symptom profiles and related thresholds of improvement during acute treatment.

RESULTS

Variation in six depressive symptoms (difficulty having fun, social withdrawal, excessive fatigue, irritability, low self-esteem, and depressed feelings) assessed with the Children's Depression Rating Scale-Revised at 4-6 weeks predicted treatment outcomes with fluoxetine at 10-12 weeks with an average accuracy of 73% in the training dataset. The same six symptoms predicted 10-12 week outcomes at 4-6 weeks in (a) duloxetine testing datasets with an average accuracy of 76% and (b) placebo-treated patients with accuracies of 67%. In placebo-treated patients, the accuracies of predicting response and remission were similar to antidepressants. Accuracies for predicting nonresponse to placebo treatment were significantly lower than antidepressants.

CONCLUSIONS

PGMs provided clinically meaningful predictions in samples of depressed children and adolescents treated with fluoxetine or duloxetine. Future work should augment PGMs with biological data for refined predictions to guide the selection of pharmacological and psychotherapeutic treatment in children and adolescents with depression.

Updates in treatment of depression in children and adolescents

PURPOSE OF REVIEW

To examine recent evidence that informs the treatment of depression in children and adolescents.

RECENT FINDINGS

There are no new leads in the prevention and early intervention of depression in children and adolescents. For acute treatment of major depressive disorder, talking therapies are moving increasingly to internet-based platforms. Family therapy may have a slight edge over individual psychotherapy in the short-term. Patients with severe depression with endogenous features have a more robust response to pharmacotherapy than do patients with mild-to-moderate depression. Findings in relation to reward sensitivity and changes in brain-derived neurotrophic factor levels contradict research conducted in adults, suggesting developmental differences in the mechanisms underlying depression. Ketamine infusion could have a role for adolescents with treatment refractory depression. There was no new evidence concerning relapse prevention.

SUMMARY

Most new findings have been concerned with moderators and mediators of treatment.

The Impact of Failed Antidepressant Trials on Outcomes in Children and Adolescents with Anxiety and Depression: A Systematic Review and Meta-Analysis

Objective: To identify predictors of medication-placebo differences in double-blind placebo-controlled antidepressant trials in children and adolescents with anxiety and depression. Methods: Clinical trials in patients <18 years of age with major depressive disorder or generalized, separation or social anxiety disorders were obtained from PubMed, the Cochrane Database and clinicaltrials.gov searches from inception through 2019. Forty-nine trials (43 published and 6 unpublished) of anxiety (κ = 13) and depression (κ = 36) evaluated 19 antidepressants in 8642 child and adolescent patients; placebo and medication response rates, trial characteristics, disorder, medication class, and funding source were extracted. Antidepressant-placebo differences were examined using Bayesian hierarchical models and estimates of response were determined for trial design, disorder, and medication class variables. Using meta-regression, correlates of antidepressant-placebo difference and placebo response were examined. Results: Funding source differentiated medication-placebo differences regardless of disorder. Industry trials had larger placebo response rates (mean difference: 0.189 ± 0.066, credible interval [CrI]: 0.067 to 0.33, p = 0.0008) and smaller medication-placebo differences (-0.235 ± 0.078, CrI: -0.397 to -0.086, p = 0.005) compared with federally funded trials. However, medication response was similar for industry- and federally-funded studies (-0.046 ± 0.042, CrI: -0.130 to 0.038, p = 0.252). Conclusions: The impact of study sponsorship on trial outcome supports the assertion that industry-funded trials with high placebo response rates and small drug-placebo differences are "failed trials" and should not be described as "negative trials" or used to determine public health estimates of antidepressant efficacy in children and adolescents with anxiety and depression. Identifying the proper role and value of industry-funded trials is critical to establishing the evidence base for antidepressants in youth.