Only a limited number of patients with major depressive disorder (MDD) respond to a first course of antidepressant medication (ADM). We investigated the feasibility of creating a baseline model to determine which of these would be among patients beginning ADM treatment in the US Veterans Health Administration (VHA).

A 2018–2020 national sample of n = 660 VHA patients receiving ADM treatment for MDD completed an extensive baseline self-report assessment near the beginning of treatment and a 3-month self-report follow-up assessment. Using baseline self-report data along with administrative and geospatial data, an ensemble machine learning method was used to develop a model for 3-month treatment response defined by the Quick Inventory of Depression Symptomatology Self-Report and a modified Sheehan Disability Scale. The model was developed in a 70% training sample and tested in the remaining 30% test sample.

In total, 35.7% of patients responded to treatment. The prediction model had an area under the ROC curve (s.e.) of 0.66 (0.04) in the test sample. A strong gradient in probability (s.e.) of treatment response was found across three subsamples of the test sample using training sample thresholds for high [45.6% (5.5)], intermediate [34.5% (7.6)], and low [11.1% (4.9)] probabilities of response. Baseline symptom severity, comorbidity, treatment characteristics (expectations, history, and aspects of current treatment), and protective/resilience factors were the most important predictors.

Although these results are promising, parallel models to predict response to alternative treatments based on data collected before initiating treatment would be needed for such models to help guide treatment selection.

Fewer than half of patients with major depressive disorder (MDD) respond to psychotherapy. Pre-emptively informing patients of their likelihood of responding could be useful as part of a patient-centered treatment decision-support plan.

This prospective observational study examined a national sample of 807 patients beginning psychotherapy for MDD at the Veterans Health Administration. Patients completed a self-report survey at baseline and 3-months follow-up (data collected 2018–2020). We developed a machine learning (ML) model to predict psychotherapy response at 3 months using baseline survey, administrative, and geospatial variables in a 70% training sample. Model performance was then evaluated in the 30% test sample.

32.0% of patients responded to treatment after 3 months. The best ML model had an AUC (SE) of 0.652 (0.038) in the test sample. Among the one-third of patients ranked by the model as most likely to respond, 50.0% in the test sample responded to psychotherapy. In comparison, among the remaining two-thirds of patients, <25% responded to psychotherapy. The model selected 43 predictors, of which nearly all were self-report variables.

Patients with MDD could pre-emptively be informed of their likelihood of responding to psychotherapy using a prediction tool based on self-report data. This tool could meaningfully help patients and providers in shared decision-making, although parallel information about the likelihood of responding to alternative treatments would be needed to inform decision-making across multiple treatments.

Efficacy of depression treatments, including adjunctive antipsychotic treatment, has not been explored for patients with worsening symptoms after antidepressant therapy (ADT).

This post-hoc analysis utilized pooled data from 3 similarly designed, randomized, double-blind, placebo-controlled trials that assessed the efficacy, safety, and tolerability of adjunctive aripiprazole in patients with major depressive disorder with inadequate response to ADT. The studies had 2 phases: an 8-week prospective ADT phase and 6-week adjunctive (aripiprazole or placebo) treatment phase. This analysis focused on patients whose symptoms worsened during the prospective 8-week ADT phase (worsening defined as >0% increase in Montgomery–Åsberg Depressive Rating Scale [MADRS] Total score). During the 6-week, double-blind, adjunctive phase, response was defined as ≥50% reduction in MADRS Total score and remission as ≥50% reduction in MADRS Total score and MADRS score ≤10.

Of 1065 patients who failed to achieve a response during the prospective phase, 160 exhibited worsening of symptoms (ADT-Worseners), and 905 exhibited no change/reduction in MADRS scores (ADT-Non-worseners). Response rates for ADT-Worseners at endpoint were 36.6% (adjunctive aripiprazole) and 22.5% (placebo). Similarly, response rates at endpoint for ADT-Non-worseners were 37.5% (adjunctive aripiprazole) and 22.5% (placebo). Remission rates at endpoint for ADT-Worseners were 25.4% (adjunctive aripiprazole) and 12.4% (placebo). For ADT-Non-worseners, remission rates were 29.9% (adjunctive aripiprazole) and 17.4% (placebo).

These results suggest that adjunctive aripiprazole is an effective intervention for patients whose symptoms worsen during antidepressant monotherapy. The results challenge the view that benefits of adjunctive therapy with aripiprazole are limited to partial responders to ADT.

While adult populations have been well described in terms of nutritional status, such as the concentration of nutrient biomarkers, little work has been done in healthy paediatric populations.

The primary objective of this analysis was to explore the determinants of plasma micronutrients in a group of healthy infants and children.

The Diabetes Autoimmunity Study in the Young (DAISY) has enrolled 1433 newborns at increased risk for type 1 diabetes in Denver, Colorado. A representative random sample of 257 children from the DAISY cohort between the ages of 9 months and 8 years with a total of 815 clinic visits over time was used in this analysis. Annual dietary intake was assessed over time with Willett food-frequency questionnaires that were validated in this population. Environmental tobacco smoke (ETS) was assessed using a validated survey. Plasma samples were tested for vitamins, carotenoids and total lipids. Predictors of plasma micronutrients were evaluated using mixed models for longitudinal data, while adjusting for age, human leukocyte antigen genotype, type 1 diabetes family history and other potential confounders and covariates.

Increased micronutrient intake was associated with increased levels of their respective plasma nutrient, with the exception of γ-tocopherol. Independent of dietary intake, levels of α- and β-carotene and β-cryptoxanthin were significantly lower, and γ-tocopherol was significantly higher, in children who were exposed to ETS.

Dietary intake predicts plasma micronutrient levels. Exposure to ETS potentially could have negative health effects in this young population.