Dimensions of anxiety in Major depressive disorder and their use in predicting antidepressant treatment outcome: an iSPOT-D report

Identifying latent subtypes of symptom trajectories in major depressive disorder patients and their predictors

This study aimed to identify different symptom trajectories based on the severity of depression symptoms within a 2-month follow-up, and to explore predictive factors for different symptom trajectories. Three hundred and ninety-two adults diagnosed with major depressive disorder (MDD) were recruited from two longitudinal cohorts. Patients received antidepressant treatment as usual, and the depression symptoms were evaluated by the 17-item Hamilton depression rating scale (HAMD-17) at baseline, two weeks, and eight weeks. Based on the HAMD-17 scores, different trajectories of symptom change were distinguished by applying Growth Mixture Modeling (GMM). Furthermore, the baseline sociodemographic, clinical, and cognitive characteristics were compared to identify potential predictors for different trajectories. Through GMM, three unique depressive symptom trajectories of MDD patients were identified: (1) mild-severity class with significant improvement (Mild, n = 255); (2) high-severity class with significant improvement (High, n = 39); (3) moderate-severity class with limited improvement (Limited, n = 98). Among the three trajectories, the Mild class had a relatively low level of anxiety symptoms at baseline, whereas the High class had the lowest education level and the worst cognitive performance. Additionally, participants in the Limited class exhibited an early age of onset and experienced a higher level of emotional abuse. MDD patients could be categorised into three distinct latent subtypes through different symptom trajectories in this study, and the characteristics of these subtype patients may inform identifications for trajectory-specific intervention targets.

Outcomes of a tailored self-care intervention for patients with heart failure and major depression: A secondary analysis of a randomized controlled trial

BACKGROUND

Depression is a recognized barrier to heart failure self-care, but there has been little research on interventions to improve heart failure self-care in depressed patients.

OBJECTIVES

To investigate the outcomes of an individually tailored self-care intervention for patients with heart failure and major depression, and to determine whether the adequacy of self-care at baseline, the severity of depression or anxiety, or other factors affect the outcomes of this intervention.

DESIGN

Secondary analysis of data from a pre-registered randomized controlled trial (NCT02997865).

METHODS

Outpatients with heart failure and comorbid major depression (n = 139) were randomly assigned to cognitive behavior therapy or usual care for depression. In addition, an experienced cardiac nurse provided the tailored self-care intervention to all patients in both arms of the trial starting eight weeks after randomization. Weekly self-care intervention sessions were held between Weeks 8 and 16; the frequency was tapered to biweekly or monthly between Weeks 17 and 32. The Self-Care of Heart Failure Index (v6.2) was used to assess self-care outcomes, with scores ≥70 on each of its three scales (Maintenance, Management, and Confidence) being consistent with adequate self-care. The Week 16 Maintenance scale score was the primary outcome for this analysis.

RESULTS

At baseline, 107 (77%) of the patients scored in the inadequate self-care range on the Maintenance scale. Between Weeks 8 and 16, Maintenance scores improved more in patients with initially inadequate than initially adequate self-care (11.9 vs. 3.2 points, p = .003). Sixty-six (48%) of the patients with initially inadequate Maintenance scores achieved scores in the adequate range by Week 32 (p < .0001). Covariate-adjusted predictors of better Maintenance outcomes included adequate Maintenance at baseline (p < .0001), higher anxiety at baseline (p < .05), and higher dosages of the self-care intervention (p < .0001). Neither treatment with cognitive behavior therapy nor less severe major depression predicted better self-care outcomes.

CONCLUSIONS

Depressed patients with inadequate heart failure self-care are able to achieve clinically significant improvements in self-care with the help of an individually tailored self-care intervention. Further refinement and testing are needed to increase the intervention's potential for clinical implementation.

Predictors of 4-week antidepressant outcome in patients with first-episode major depressive disorder: An ROC curve analysis

BACKGROUND

Pretreatment characteristics of patients, symptom and function could be associated with antidepressant treatment outcome, but its predictive ability is not adequate. Our study aimed to identify predictors of acute antidepressant efficacy in patients with first-episode Major Depressive Disorder (MDD).

METHODS

187 patients with first-episode MDD were included and assessed clinical symptoms, cognitive function and global functioning using the 17-item Hamilton Depression Inventory (HAMD-17), MATRICS Consensus Cognitive Battery (MCCB) and Global Assessment of Functioning (GAF). Participants received treatment with a SSRI (escitalopram or venlafaxine) for 4 weeks. Logistic regression was used to analyze the association between patients' characteristics, symptom profiles, cognitive performance, and global functioning and the antidepressant outcome at the end of 4 weeks, and ROC curve analysis was performed for predictive accuracy with area under the receiver operating curve (AUC).

RESULTS

Antidepressant improvement, response and remission rate at week 4 was 87.7%, 64.7% and 42.8%, respectively. The combination of pretreatment clinical profiles, speed of processing and global functioning showed moderate discrimination of acute improvement, response and remission with AUCs of 0.863, 0.812 and 0.734, respectively.

LIMITATIONS

The major limitation of the present study is the study did not combine pharmacogenomics from the perspective of antidepressant drug metabolism.

CONCLUSION

Aside from the baseline clinical symptoms, cognitive function and global functioning could be predictors of acute treatment outcome in first episode MDD using escitalopram or venlafaxine. This relatively simple application based on clinical symptoms and function seems to be cost-effective method to identify individuals who are more likely to respond to antidepressant treatment.

Substance Use, Mental Health, and Weight-Related Behaviors During the COVID-19 Pandemic Among Metabolic and Bariatric Surgery Patients

Purpose

The impact of the COVID-19 pandemic on behavioral issues among those who have completed bariatric surgery (BS) is not well described in ethnically diverse populations. The aim of this study was to compare the impact of COVID-19 lockdown orders and after lockdown orders were lifted on substance use, mental health, and weight-related behaviors among a sample of post-BS adults.

Materials and Methods

A retrospective medical chart review identified BS patients from one university-based obesity medicine clinic and two BS practices. An online non-anonymous survey was implemented in two phases: during lockdown (April 1–May 31, 2020) and after lockdown orders were lifted (June 1, 2020–September 30, 2020) to obtain information about the COVID-19 pandemic’s impact on BS patients.

Results

A total of 189 (during lockdown=39, post-lockdown=150) participants (90.4% female, mean age 52.4 years, SD 11.1, 49.8% non-Hispanic White, 30.6% non-Hispanic Black, 16.1% Hispanic) participated. Lockdown participants were more likely to have sleep problems (74.3% vs. 56.1%, P=.039) and feel anxious (82.0% vs. 63.0%, P=.024) versus post-lockdown participants. A majority (83.4%) reported depression in both lockdown/post-lockdown. Post-lockdown participants were more than 20 times more likely to report substance use compared those in lockdown (aOR 20.56, 95% CI 2.66–158.4).

Conclusions and Relevance

The COVID-19 pandemic is having a substantial negative impact on substance use, mental health, and weight-related health behaviors in diverse BS patients. These findings have important implications for post-BS patient care teams and may suggest the integration of screening tools to identify those at high risk for behavioral health issues.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s11695-021-05488-6.

Mapping the Presence of Anxiety Symptoms in Adults With Major Depressive Disorder

Background: Patients with major depressive disorder (MDD) often present with co-occurring anxiety symptoms. The network method provides a novel view on understanding the co-occurrence of depressive and anxiety symptoms. Thus, the purpose of our study was to explore it by applying network analysis methods. Methods: We used electronic medical records from West China Hospital in China. In total, 3,424 patients who met the criteria for MDD were included. R-studio 3.6 was used to estimate the network structure. First, we estimated the network structure of depression and anxiety symptoms using the graphic LASSO algorithm. Then, we estimated the centrality indices of nodes to determine which symptoms are more central in the network. We then estimated the bridge centrality indices using the bridge function via the R package networktools. Results: Some strong connections were found like "easy to wake up," "wake up early," and "difficulty falling asleep," "suicidal thoughts," and "hopelessness." "Depressed mood," "somatic anxiety," "hopelessness," "anxiety mood," and "tension" have the higher centrality indices. Results revealed eight bridge symptoms (e.g., concentration/memory difficulty, gastrointestinal symptoms) in the co-occurrence network structure. Conclusions: This research suggests that the described approach in mapping the presence of anxiety symptoms in individuals with major depression might potentially increase diagnostic precision and help choose more targeted interventions and potentially reduce the occurrence of treatment resistance.

Effects of Serial Ketamine Infusions on Corticolimbic Functional Connectivity in Major Depression

BACKGROUND

Ketamine is a highly effective antidepressant for patients with treatment-resistant major depressive disorder (MDD). Resting-state functional magnetic resonance imaging studies show disruptions of functional connectivity (FC) between limbic regions and resting-state networks (RSNs) in MDD, including the default mode network, central executive network (CEN), and salience network (SN). Here, we investigated whether serial ketamine treatments change FC between limbic structures and RSNs.

METHODS

Patients with MDD (n = 44) were scanned at baseline (time 1 [T1]) and 24 hours after the first (T2) and fourth (T3) infusions of ketamine. Healthy control subjects (n = 50) were scanned at baseline, with a subgroup (n = 17) being rescanned at 2 weeks. Limbic regions included the amygdala and hippocampus, and RSNs included the default mode network, CEN, and SN.

RESULTS

Ketamine increased right amygdala FC to the right CEN (p = .05), decreased amygdala FC to the left CEN (p = .005) at T2 versus T1 (p = .015), which then increased at T3 versus T2 (p = .002), and decreased left amygdala FC to the SN (p = .016). Decreased left amygdala to SN FC at T2 predicted improvements in anxiety at T3 (p = .006). Ketamine increased right hippocampus FC to the left CEN (p = .001), and this change at T2 predicted decreased anhedonia at T3 (p = .005).

CONCLUSIONS

Ketamine modulates FC between limbic regions and RSNs implicated in MDD. Increases in FC between limbic regions and the CEN suggest that ketamine may be involved in restoring top-down control of emotion processing. FC decreases between the left amygdala and SN suggest that ketamine may ameliorate MDD-related dysconnectivity in these circuits. Early FC changes between limbic regions and RSNs may be predictive of clinical improvements.

Personalized Psychiatry and Depression: The Role of Sociodemographic and Clinical Variables

Despite several pharmacological options, the clinical outcomes of major depressive disorder (MDD) are often unsatisfactory. Personalized psychiatry attempts to tailor therapeutic interventions according to each patient's unique profile and characteristics. This approach can be a crucial strategy in improving pharmacological outcomes in MDD and overcoming trial-and-error treatment choices. In this narrative review, we evaluate whether sociodemographic (i.e., gender, age, race/ethnicity, and socioeconomic status) and clinical [i.e., body mass index (BMI), severity of depressive symptoms, and symptom profiles] variables that are easily assessable in clinical practice may help clinicians to optimize the selection of antidepressant treatment for each patient with MDD at the early stages of the disorder. We found that several variables were associated with poorer outcomes for all antidepressants. However, only preliminary associations were found between some clinical variables (i.e., BMI, anhedonia, and MDD with melancholic/atypical features) and possible benefits with some specific antidepressants. Finally, in clinical practice, the assessment of sociodemographic and clinical variables considered in our review can be valuable for early identification of depressed individuals at high risk for poor responses to antidepressants, but there are not enough data on which to ground any reliable selection of specific antidepressant class or compounds. Recent advances in computational resources, such as machine learning techniques, which are able to integrate multiple potential predictors, such as individual/ clinical variables, biomarkers, and genetic factors, may offer future reliable tools to guide personalized antidepressant choice for each patient with MDD.