Predicting relapse in major depression after successful initial pharmacological treatment

Depression symptom outcomes and re-engagement among VA patients who discontinue care while symptomatic

OBJECTIVE

Evaluate outcomes of Veterans who discontinued treatment with at least moderate ongoing depressive symptoms.

METHOD

Veterans with elevated depression symptoms from 29 Department of Veterans Affairs facilities completed baseline surveys and follow-up assessments for one year. Analyses examined rates and predictors of treatment discontinuation, treatment re-engagement, and subsequent symptoms among patients who remained out of care.

RESULTS

A total of 242 (17.8%; n = 1359) participants discontinued treatment while symptomatic, with Black participants, participants with less severe depression, and participants receiving only psychotherapy (versus combined psychotherapy and antidepressant medications) discontinuing at higher rates. Among all participants who discontinued treatment (n = 445), 45.8% re-engaged within the following six months with participants receiving combined treatment re-engaging at higher rates. Of participants who discontinued while symptomatic within the first 6 months of the study and did not return to care (n = 112), 68.8% remained symptomatic at 12 months. Lower baseline treatment expectancy and greater depression symptom severity were associated with remaining symptomatic while untreated.

CONCLUSIONS

Black race, lower symptom severity, and treatment modality may help identify patients at higher risk for discontinuing care while symptomatic, whereas patients with lower treatment expectations may be at greater risk for remaining out of care despite continuing symptoms.

Relationship of insight to neurocognitive function and risk of recurrence in depression: A naturalistic follow-up study

Introduction

Major depressive disorder (MDD) is a highly recurrent mental illness accompanied by impairment of neurocognitive function. Lack of insight may affect patients’ motivation to seek treatment, resulting in poor clinical outcomes. This study explores the relationship of insight to neurocognitive function and the risk of recurrence of depressive episodes in patients with MDD.

Methods

Demographic, clinical variables, and neurocognitive function measured with Intra-Extra Dimensional Set Shift (IED) from the Cambridge Neuropsychological Test Automated Battery (CANTAB) were collected from 277 patients with MDD. Among them, 141 participants completed a follow-up visit within 1–5 years. Insight was measured using the 17-item Hamilton Depression Rating Scale (HAM-D). To explore the factors associated with recurrence, binary logistic regression models were used.

Results

Patients with MDD, without insight, had significantly higher total and factor scores (anxiety/somatization, weight, retardation, and sleep) on the HAM-D and worse performance in the neurocognition task, compared to those with insight. Furthermore, binary logistic regression revealed that insight and retardation can predict recurrence.

Conclusion

Lack of insight is associated with recurrence and impaired cognitive flexibility in patients with MDD.

Modeling disease progression and treatment pathways for depression jointly using agent based modeling and system dynamics

Introduction

Depression is a common mental health condition that affects millions of people worldwide. Care pathways for depression are complex and the demand across different parts of the healthcare system is often uncertain and not entirely understood. Clinical progression with depression can be equally complex and relates to whether or not a patient is seeking care, the care pathway they are on, and the ability for timely access to healthcare services. Considering both pathways and progression for depression are however rarely studied together in the literature.

Methods

This paper presents a hybrid simulation modeling framework that is uniquely able to capture both disease progression, using Agent Based Modeling, and related care pathways, using a System Dynamics. The two simulation paradigms within the framework are connected to run synchronously to investigate the impact of depression progression on healthcare services and, conversely, how any limitations in access to services may impact clinical progression. The use of the developed framework is illustrated by parametrising it with published clinical data and local service level data from Wales, UK.

Results and discussion

The framework is able to quantify demand, service capacities and costs across all care pathways for a range of different scenarios. These include those for varying service coverage and provision, such as the cost-effectiveness of treating patients more quickly in community settings to reduce patient progression to more severe states of depression, and thus reducing the costs and utilization of more expensive specialist settings.

Predicting relapse from the time to remission during the acute treatment of depression: A re-analysis of the STAR*D data

BACKGROUND

Predicting relapse during maintenance treatment for depression is challenging. The objective of this analysis was to investigate the association between the time taken to achieve remission in the acute phase, and the subsequent relapse rate or time to relapse using the Sequenced Treatment Alternatives to Relieve Depression dataset.

METHOD

Data of 1296 outpatients with nonpsychotic depression who entered a 12-month naturalistic follow-up period after achieving remission with citalopram for up to 14 weeks were analyzed. One-way analysis of variance and the Jonckheere-Terpstra trend test were performed to compare the relapse rates and days to relapse during the follow-up period among those who achieved remission at weeks 2, 4, 6, 9, 12, and 14. Remission and relapse were defined as scores of ≤5 and ≥11, respectively, on the 16-Item Quick Inventory of Depressive Symptomatology and Self-Report.

RESULTS

The relapse rates were significantly different among those who achieved remission each week (F_{(5, 1087)} = 4.995, p < 0.001). The lowest and highest relapse rates were observed in those who achieved remission at weeks 4 (25.7 %) and 12 (42.4 %), respectively, with a significant difference (p = 0.006). There was also a significant negative trend between the weeks taken to achieve remission and the days to relapse (z = -6.13, p < 0.001).

CONCLUSIONS

Patients with depression who show a faster response to antidepressant treatment are more likely to maintain remission in the long term. This finding suggests that, to prevent relapse, close attention should be paid to patients who require a relatively long time to achieve remission.

A Systematic Review and Individual Patient Data Network Analysis of the Residual Symptom Structure Following Cognitive-Behavioral Therapy and Escitalopram, Mirtazapine and Venlafaxine for Depression

OBJECTIVE

Consistent evidence suggests residual depressive symptomology are the strongest predictors of depression relapse following cognitive-behavioral therapy (CBT) and antidepressant medications (ADM's). Psychometric network models help detecting and understanding central symptoms that remain post-treatment, along with their complex co-occurrences. However, individual psychometric network studies show inconsistent findings. This systematic review and IPD network analysis aimed to estimate and compare the symptom network structures of residual depressive symptoms following CBT, ADM's, and their combination.

METHODS

PsycINFO, PsycArticles, and PubMed were systematically searched through October 2020 for studies that have assessed individuals with major depression at post-treatment receiving either CBT and/or ADM's (venlafaxine, escitalopram, mirtazapine). IPD was requested from eligible samples to estimate and compare residual symptom psychometric network models post-CBT and post-ADM's.

RESULTS

In total, 25 from 663 eligible samples, including 1,389 patients qualified for the IPD. Depressed mood and anhedonia were consistently central residual symptoms post-CBT and post-ADM's. For CBT, fatigue-related and anxiety symptoms were also central post-treatment. A significant difference in network structure across treatments (CBT vs. ADM) was observed for samples measuring depression severity using the MADRS. Specifically, stronger symptom occurrences were present amongst lassitude-suicide post-CBT (vs. ADM's) and amongst lassitude-inability to feel post-ADM's (vs. CBT). No significant difference in global strength was observed across treatments.

CONCLUSIONS

Core major depression symptoms remain central across treatments, strategies to target these symptoms should be considered. Anxiety and fatigue related complaints also remain central post-CBT. Efforts must be made amongst researchers, institutions, and journals to permit sharing of IPD.Systematic Review Registration: A protocol was prospectively registered on PROSPERO (CRD42020141663; https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=141663).

Meta-analysis of association between TPH2 single nucleotide poiymorphism and depression

Tryptophan hydroxylase 2 (TPH2) plays a crucial role in the human brain. Although the association between the TPH2 gene and depression has been suggested in previous meta-analyses, studies based on Chinese subjects are often neglected. Therefore, we included some previous studies based on Chinese subjects to explore the relationship between TPH2 polymorphisms and depression via conducting an extensive meta-analysis. We reviewed 40 research papers that included data on TPH2 gene single nucleotide polymorphisms (SNPs) from 5766 patients with depression and 5988 healthy subjects. The analysis showed an association between polymorphisms in the TPH2 gene and depression, and some results were significant in 24 studies that included Chinese Han study participants. The results of our meta-analysis showed that rs4570625, rs17110747, rs120074175, rs4290270, rs120074175, and rs4290270 may be significantly associated with depression, and that rs11178997 (A/A genotype) may be a significant risk factor for depression in the Chinese subjects. Based on the results of this study, biological experiments should be performed in the future to explore how different SNPs affect depression.

Multidimensional Analysis of Major Depression: Association Between BDNF Methylation, Psychosocial and Cognitive Domains

Major Depression is a complex disorder with a growing incidence worldwide and multiple variables have been associated with its etiology. Nonetheless, its diagnosis is continually changing and the need to understand it from a multidimensional perspective is clear. The purpose of this study was to identify risk factors for depression in a case-control study with 100 depressive inpatients and 87 healthy controls. A multivariate logistic regression analysis was performed including psychosocial factors, cognitive maladaptive schema domains, and specific epigenetic marks (BDNF methylation levels at five CpG sites in promoter IV). A family history of depression, the cognitive schemas of impaired autonomy/performance, impaired limits, other-directedness, and the methylation level of a specific CpG site were identified as predictors. Interestingly, we found a mediating effect of those cognitive schemas in the relationship between childhood maltreatment and depression. Also, we found that depressive patients exhibited hypomethylation in a CpG site of BDNF promoter IV, which adds to the current discussion about the role of methylation in depression. We highlight that determining the methylation of a specific region of a single gene offers the possibility of accessing a highly informative an easily measurable variable, which represents benefits for diagnosis. Following complete replication and validation on larger samples, models like ours could be applicable as additional diagnostic tools in the clinical context.

Predictors of relapse following a stepwise psychopharmacotherapy regime in patients with depressive disorders

BACKGROUND

Real world predictors of relapse following routine treatment for depression remain under-researched. We sought to investigate this in an outpatient clinical sample with depressive disorders receiving stepwise pharmacotherapy based on early clinical decision-making, applying a naturalistic 24-month prospective design.

METHODS

Patients were recruited at a University hospital in South Korea from March 2012 to April 2017. After 3-week antidepressant monotherapy (N = 1262), next treatment steps (1, 2, 3, and 4 or over) with alternative strategies (switching, augmentation, combination, and mixtures of these approaches) were administered based on measurements and patient preference at 3-week points in the acute treatment phase (3, 6, 9, and 12 weeks) (N = 1246). For those who responded [Hamilton Depression Rating Scale (HAMD) score of≤14] (N = 937), relapse (HAMD>14) was identified every 3 months from 6 to 24 months (N = 816). Predictors of relapse were evaluated using multi-variate Cox proportional hazards models.

RESULTS

Four independent relapse predictors were identified: higher number of previous depressive episodes, higher anxiety at baseline, higher number of treatment steps, and poor medication adherence. In particular, treatment Step 4 was significantly associated with relapse compared to treatment Step 1, 2, and 3 after adjustment for relevant covariates.

LIMITATION

Withdrawal syndromes after discontinuing psychotropic drugs, known to confound the determination of relapse, were not evaluated. The study was conducted at a single site, which maximised consistency but may limit generalizability.

CONCLUSIONS

Predictors of relapse reported from more restricted trial or cohort samples were replicated in this long-term naturalistic prospective design.

Assessing recurrence of depression using a zero-inflated negative binomial model: A secondary analysis of lifelog data

When studying recurrence of depression, researchers should pay attention to cases where physicians' assessment corresponds to the patients' perception. However, they should also focus on potential signs of recurrence when the recurrence is suspected by the physicians but not the patients (false-negative zeros). Because false negatives can delay diagnosis and treatment, we aimed to investigate "sitting idly" as a predictor influencing no alert sign of recurrence and estimated the counts of recurrence of depression. A smartphone application and a wearable device were used to collect lifelog data from 89 remitted depressive patients over one year. Recurrent depression was defined using the Japanese version of the Kessler Psychological Distress Scale and Patient Health Questionnaire-9 scores. Estimates of the population-averaged parameters indicated that daily hours of sitting idly increased the chances of recurrent depression occurring two to four weeks later. Exposure to daily ultraviolet light reduced depression relapse. Although long sleep was a determinant of zero outcome of the recurrence of depression after two to four weeks, daily hours of sitting idly can negate it. Thus, daily hours of sitting idly could reduce overdispersion of the recurrence of depression, and we could measure recurrent depression accurately by considering changes in sitting idly.

An individualized treatment rule to optimize probability of remission by continuation, switching, or combining antidepressant medications after failing a first-line antidepressant in a two-stage randomized trial

BACKGROUND

There is growing interest in using composite individualized treatment rules (ITRs) to guide depression treatment selection, but best approaches for doing this are not widely known. We develop an ITR for depression remission based on secondary analysis of a recently published trial for second-line antidepression medication selection using a cutting-edge ensemble machine learning method.

METHODS

Data come from the SUN(^_^)D trial, an open-label, assessor blinded pragmatic trial of previously-untreated patients with major depressive disorder from 48 clinics in Japan. Initial clinic-level randomization assigned patients to 50 or 100 mg/day sertraline. We focus on the 1549 patients who failed to remit within 3 weeks and were then rerandomized at the individual-level to continuation with sertraline, switching to mirtazapine, or combining mirtazapine with sertraline. The outcome was remission 9 weeks post-baseline. Predictors included socio-demographics, clinical characteristics, baseline symptoms, changes in symptoms between baseline and week 3, and week 3 side effects.

RESULTS

Optimized treatment was associated with significantly increased cross-validated week 9 remission rates in both samples [5.3% (2.4%), p = 0.016 50 mg/day sample; 5.1% (2.7%), p = 0.031 100 mg/day sample] compared to randomization (30.1-30.8%). Optimization was also associated with significantly increased remission in both samples compared to continuation [24.7% in both: 11.2% (3.8%), p = 0.002 50 mg/day sample; 11.7% (3.9%), p = 0.001 100 mg/day sample]. Non-significant gains were found for optimization compared to switching or combining.

CONCLUSIONS

An ITR can be developed to improve second-line antidepressant selection, but replication in a larger study with more comprehensive baseline predictors might produce stronger and more stable results.

Short and long-term treatment outcomes of stepwise psychopharmacotherapy based on early clinical decision in patients with depressive disorders

BACKGROUND

To investigate the effects of stepwise pharmacotherapy based on early clinical decision-making on short- and long-term treatment outcomes in outpatients with depressive disorders in a naturalistic one-year prospective design.

METHODS

Patients were recruited at a University hospital in South Korea from March 2012 to April 2017. At baseline, 1262 patients received antidepressant monotherapy. For patients with an insufficient response or uncomfortable side effects, next treatment steps (1, 2, 3, and 4 or over) with alternative strategies (switching, augmentation, combination, and mixtures of these approaches) were administered considering measurements and patient preference at every 3 weeks in the acute treatment phase (3, 6, 9, and 12 weeks) (N=1246), and at every 3 months in the continuation treatment phase (6, 9, and 12 months) (N=1015). Remission was defined as a Hamilton Depression Rating Scale score of ≤ 7.

RESULTS

Remission was more frequently achieved with increasing treatment steps and advanced treatment strategies over the treatment period, while the superior effect of treatment Step 4 or over no longer persisted in the continuation treatment phase. Augmentation + combination strategy was associated with the best outcome, with least benefit associated with a switching strategy compared to monotherapy continuation. Adverse events were more frequent with increasing treatment steps and advanced treatment strategies, while numbers of visits did not statistically differ by treatment steps or strategies.

LIMITATION

The lack of a comparison group without early clinical decision due to the descriptive nature of study design limits to prove directly the study question.

CONCLUSIONS

A stepwise pharmacotherapy approach based on early clinical decision-making in the light of measurements and patient preference could enhance both short- and long-term treatment outcomes in depressive disorders.

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.