Trajectory and magnitude of response in adults with anxiety disorders: a Bayesian hierarchical modeling meta-analysis of selective serotonin reuptake inhibitors, serotonin norepinephrine reuptake inhibitors, and benzodiazepines.

BACKGROUND: How the trajectory of response to medication (and placebo response) varies among selective serotonin reuptake inhibitors (SSRIs), serotonin norepinephrine reuptake inhibitors (SNRIs), benzodiazepines and across anxiety disorders is unknown. METHODS: We performed a meta-analysis using weekly symptom severity data from randomized, parallel-group, placebo-controlled trials of SSRIs, SNRIs, and benzodiazepines in adults with anxiety disorders. Response was modeled for the standardized change in anxiety using Bayesian hierarchical models. RESULTS: Across 122 trials (N=15,760), SSRIs, SNRIs, and benzodiazepines produced significant improvement in anxiety compared to placebo. Benzodiazepines produced faster improvement by the first week of treatment (p < 0.001). By week 8, the response for benzodiazepines and SSRIs (p = 0.103) and SNRIs (p = 0.911) did not differ nor did SSRIs and SNRIs differ (p = 0.057), although for patients with generalized anxiety disorder (GAD), the benzodiazepines produced greater improvement than SNRIs at week 8 (difference - 12.42, CrI: -25.05 to -0.78, p = 0.037). Medication response was similar across anxiety disorders except for benzodiazepines, which produced greater improvement over the first 4 weeks compared to SSRIs and SNRIs in panic disorder. For SSRIs and SNRIs, women improved more than men, and for benzodiazepines, older patients improved more compared to younger patients. Finally, placebo response plateaued by week 4 of treatment, and, at week 8, social anxiety disorder trials had lower placebo response compared to other anxiety disorders. CONCLUSIONS: Benzodiazepines show early improvement compared to SSRIs and SNRIs. However, by week 8, all treatments yield similar results. Patient characteristics influence the improvement trajectory and magnitude, suggesting potential for personalized medication selection.

What Is the Added Benefit of Combining Cognitive Behavioral Therapy and Selective Serotonin Reuptake Inhibitors in Youth with Obsessive Compulsive Disorder? A Bayesian Hierarchical Modeling Meta-Analysis.

Background: Treatment of obsessive-compulsive disorder (OCD) in children and adolescents frequently involves cognitive behavioral therapy (CBT), selective serotonin reuptake inhibitors (SSRIs), or their combination. However, how adding CBT to SSRIs affects the trajectory and magnitude of improvement has not been evaluated meta-analytically. Methods: We performed a meta-analysis using weekly data from prospective randomized parallel group trials of CBT and SSRIs in pediatric patients with OCD. Response was modeled for the change in the Child Yale-Brown Obsessive-Compulsive Scale (CY-BOCS) using a Bayesian hierarchical model over 12 weeks. Results: Fourteen studies included pharmacotherapy arms, 4 studies included combined pharmacotherapy and psychotherapy, and 10 studies included a placebo or control arm. The studies included 1146 patients (mean age 12.7 ± 1.3 years, mean 42.1% female). In the logarithmic model of response, statistically significant differences in treatment effects for CBT+SSRI and SSRI monotherapy were observed compared with placebo (SSRI ß = -3.59, credible interval [95% CrI]: -4.13 to -3.02, p < 0.001; SSRI+CBT ß = -4.07, 95% CrI: -5.05 to -3.04, p < 0.001). Adding CBT to an SSRI produced numerically (but not statistically significantly) greater improvement over 12 weeks. Greater improvement was observed in studies with more boys (p < 0.001), younger patients (p < 0.001), and in studies with greater baseline symptom severity (p < 0.001). Conclusions: In children and adolescents with OCD, compared with placebo, both SSRIs and SSRI+CBT produced early and sustained improvement over 12 weeks, although the improvement was also related to sample characteristics. Longer term studies are needed to determine when the additive benefit of CBT emerges relative to SSRI monotherapy.

The impact of age on antidepressant response: A mega-analysis of individuals with major depressive disorder.

INTRODUCTION: Understanding how age affects antidepressant response in patients with major depressive disorder has been complicated by small and heterogeneous studies. Yet, understanding how age-across the lifespan-contributes to variation in response could inform treatment selection across the lifespan. This study sought to identify how age impacts antidepressant response using participant-level data from large, NIH-sponsored trials in individuals with MDD aged 12-74 years. MATERIALS AND METHODS: Participant-level data were abstracted from three NIH-sponsored trials of pharmacotherapy (Treatment of SSRI-Resistant Depression in Adolescents (TORDIA) Study, Treatment of Adolescent Depression Study (TADS), and the Combining Medications to Enhance Depression Outcomes Study (COMED)) in patients with MDD. Bayesian Hierarchical Models (BHMs) of individual treatment trajectories were developed using Hamiltonian Monte Carlo No U-Turn Sampling. The individual trajectory of improvement in depressive symptoms (Clinical Global Impression-Severity [CGI-S] and CGI-S equivalent from COMED) was modeled across studies and across individuals with logarithmic trend "random effects" coefficients BHMs. Age and sex (and their interaction) were examined categorically across patients. RESULTS: Study participants (N = 907) were 29.7 ± 17 years of age, 66.3% women, and had a mean baseline CGI-S score of 4.6 ± 0.9. Patients ≤21 years and those >55 years had slower and less response to pharmacotherapy compared to those aged 21-35. Additionally, women improved more than men, and this effect did not differ across ages. DISCUSSION: The patient's age should be considered in predicting antidepressant response, particularly in older and younger individuals who may benefit from other interventions to enhance treatment response.

Socioeconomic Predictors of Treatment Outcomes Among Adults With Major Depressive Disorder.

OBJECTIVE: In this study, the authors sought to examine the impact of socioeconomic variables on outcomes of pharmacotherapy treatments for major depressive disorder in analyses that controlled for treatment access and level of care. METHODS: The authors used data from the Combining Medications to Enhance Depression Outcomes study, a prospective clinical trial conducted from March 2008 to April 2014 with 665 adults who had major depressive disorder and were randomly assigned to three pharmacotherapeutic treatments, to develop Bayesian hierarchical models of treatment trajectories for change in Quick Inventory of Depressive Symptomatology-Self-Report ratings. Posterior tail probabilities were used to evaluate the effects of education, income, race-ethnicity, and employment on treatment outcomes. RESULTS: After sex, age, and treatment type were controlled for in the analyses, not having a college education (<16 years of schooling), being unemployed, or being non-White were each associated with slower and less improvement. At the end of treatment (week 12), not having a college degree reduced treatment responses by 9.6% (p=0.045), being unemployed by 6.6% (p=0.007), and being non-White by 11.3% (p<0.001). Treatment response was significantly related to income; having an income at the 25th percentile of the income distribution decreased improvement by 4.8% compared with having an income at the 75th percentile (p=0.018). CONCLUSIONS: Within a short-term, randomized controlled trial, socioeconomic factors had a critical role in the acute response of patients to pharmacotherapy for major depression.

Combining selective serotonin reuptake inhibitors and cognitive behavioral therapy in youth with depression and anxiety.

BACKGROUND: Treatment studies of children and adolescents with internalizing disorders suggest that the combination of a selective serotonin reuptake inhibitor (SSRI) and cognitive behavioral therapy (CBT) consistently produces greater improvement than either treatment alone. We sought to determine how response to combined treatment varies across disorders (anxiety versus depression), and by specific patient characteristics. METHODS: Three large National Institutes of Health-funded trials of children and adolescents with major depression (n = 2) and anxiety disorders (n = 1) were evaluated, each comparing CBT + SSRI to SSRI only, Bayesian Hierarchical Models (BHMs) were used, for endpoint response, time course of response and predictors of response in participants who received SSRI or SSRI+CBT. RESULTS: SSRI+CBT significantly decreased symptoms by week 4 (p<0.001) across disorders. This improvement continued at week 8 and 12 (p<0.001); however, the additive benefit of CBT over SSRI monotherapy was not statistically significant until week 12 (p<0.001). The fastest response to SSRI+CBT was for patients who were younger, with milder baseline anxiety/depression symptoms and depressive disorders. The slowest response for SSRI+CBT was for boys, adolescents, minoritized children, those with severe symptoms and externalizing disorders. LIMITATIONS: Limitations included inconsistent moderators, variation in the number of observations over time and a lack of genetic or pharmacokinetic variables related to SSRI exposure across studies. CONCLUSIONS: The superiority of SSRI+CBT for youth with depression and anxiety is further supported. For purposes of rapid and greater relief, combination treatment is the superior approach across anxiety and depression and is robust to a range of participant characteristics. However, the added value of CBT (with an SSRI) occurs late in treatment. These findings represent a step towards understanding heterogeneity of treatment response and raise the possibility that interventions could be better tailored or adapted based on patient characteristics.

What next? A Bayesian hierarchical modeling re-examination of treatments for adolescents with selective serotonin reuptake inhibitor-resistant depression.

BACKGROUND: Psychiatrists frequently struggle with how to sequence treatment for depressed adolescents who do not respond to an adequate trial of a selective serotonin reuptake inhibitor (SSRI). This study leveraged recent statistical and computational advances to create Bayesian hierarchal models (BHMs) of response in the treatment of SSRI-resistant depression in adolescents study to inform treatment planning. METHODS: BHMs of individual treatment trajectories were developed and estimated using Hamiltonian Monte Carlo no u-turn sampling. From the Monte Carlo pseudorandom sample, 95% credible intervals, means, posterior tail probabilities, and so forth, were determined. Then, for the random effects model, posterior tail probabilities were used to create Bayesian two-tailed p values to evaluate the null hypotheses: no difference in efficacy between SSRIs and venlafaxine. The robustness of the results was examined using the fixed effects model of treatment comparisons. RESULTS: In patients not receiving cognitive behavioral therapy (CBT; n = 168), SSRIs produced greater and faster improvement in depressive symptoms compared to venlafaxine (p = .015). No differences in response or trajectory of response for symptoms of anxiety were detected between SSRIs and venlafaxine (p = .168). For patients receiving CBT (n = 162), SSRIs and venlafaxine produced similar improvements in symptoms of anxiety and depression. CONCLUSIONS: Findings from this novel computational approach suggest that a second trial of an SSRI is warranted for depressed adolescents who fail to respond to initial SSRI treatment.

Surface topography and hydrophilicity regulate macrophage phenotype in milled microfluidic systems.

Micromilling is an underutilized technique for fabricating microfluidic platforms that is well-suited for the diverse needs of the biologic community. This technique, however, produces culture surfaces that are considerably rougher than in commercially available culture platforms and the hydrophilicity of these surfaces can vary considerably depending on the choice of material. In this study, we evaluated the impact of surface topography and hydrophilicity in milled microfluidic devices on the cellular phenotype and function of primary human macrophages. We found that the rough culture surface within micromilled systems affected the phenotype of macrophages cultured in these devices. However, the presence, type, and magnitude of this effect was dependent on the surface hydrophilicity as well as exposure to chemical polarization signals. These findings confirm that while milled microfluidic systems are an effective platform for culture and analysis of primary macrophages, the topography and hydrophilicity of the culture surface within these systems should be considered in the planning and analysis of any macrophage experiments in which phenotype is relevant.