The Quick Inventory of Depressive Symptomatology, clinician rated and self-report: a psychometric assessment in Chinese Americans with major depressive disorder

Prediction of individual treatment allocation between electroconvulsive therapy or ketamine using the Personalized Advantage Index

Electroconvulsive therapy (ECT) and ketamine are effective treatments for depression; however, evidence-based guidelines are needed to inform individual treatment selection. We adapted the Personalized Advantage Index (PAI) using machine learning to predict optimal treatment assignment to ECT or ketamine using EHR data on 2506 ECT and 196 ketamine patients. Depressive symptoms were evaluated using the Quick Inventory of Depressive Symptomatology (QIDS) before and during acute treatment. Propensity score matching across treatments was used to address confounding by indication, yielding a sample of 392 patients (n = 196 per treatment). Models predicted differential minimum QIDS scores (min-QIDS) over acute treatment using pretreatment EHR measures and SHAP values identified prescriptive predictors. Patients with large PAI scores who received a predicted optimal had significantly lower min-QIDS compared to the non-optimal treatment group (mean difference = 1.19 [95% CI: 0.32, ∞], t = 2.25, q < 0.05, d = 0.26). Our model identified candidate pretreatment factors to provide actionable, effective antidepressant treatment selection guidelines.

SmartSense-D: A safety, feasibility, and acceptability pilot study of digital phenotyping in young people with major depressive disorder

Background

Digital assessment of behaviours, including physical activity, sleep, and social interactions could be associated with changes in mood and other mental health symptoms. This study assessed the safety, feasibility, acceptability, and potential predictive value of passive and active sensing in young people with major depressive disorder (MDD).

Methods

Over eight weeks, passive (smartphone sensing, actigraphy) and active (ecological momentary assessment; EMA) data were collected from 40 young participants with MDD (aged 16-25 years). We assessed the safety, feasibility, and acceptability of daily active and passive sensing in this population. Additionally, linear mixed models and correlation analysis explored associations between passive and active sensing measures.

Results

Of the 48 young participants, 83% (n = 40) completed the full protocol. No adverse events were reported. Over eight weeks, participants averaged 35.9 days (65.3%) with EMAs and 37.9 days (69%) with actigraphy data. Smartphone sensors recorded communication for 21.1 days (38.4%), location for 43.1 days (78.4%), maximum unlock duration for 43.4 days (79%), social media use for 34.8 days (63.3%), and inter-key delay for 32.8 days (59.6%). Regarding acceptability, 83.1% found the application usable and comfortable. Secondary measures showed significant correlations between sleep and physical activity, and between location and phone use sensors. There was a significant negative association between daily positive mood ratings and QIDS total scores (Beta coefficient [95% CI]: 2.66 [-3.98, -1.34]; p = 0.002).

Conclusion

Passive and active sensing methods were safe, and acceptable among young people with MDD.

Sensory Entrained TMS (seTMS) enhances motor cortex excitability

Transcranial magnetic stimulation (TMS) applied to the motor cortex has revolutionized the study of motor physiology in humans. Despite this, TMS-evoked electrophysiological responses show significant variability, due in part to inconsistencies between TMS pulse timing and ongoing brain oscillations. Variable responses to TMS limit mechanistic insights and clinical efficacy, necessitating the development of methods to precisely coordinate the timing of TMS pulses to the phase of relevant oscillatory activity. We introduce Sensory Entrained TMS (seTMS), a novel approach that uses musical rhythms to synchronize brain oscillations and time TMS pulses to enhance cortical excitability. Focusing on the sensorimotor alpha rhythm, a neural oscillation associated with motor cortical inhibition, we examine whether rhythm-evoked sensorimotor alpha phase alignment affects primary motor cortical (M1) excitability in healthy young adults (n=33). We first confirmed using electroencephalography (EEG) that passive listening to musical rhythms desynchronizes inhibitory sensorimotor brain rhythms (mu oscillations) around 200 ms before auditory rhythmic events (27 participants). We then targeted this optimal time window by delivering single TMS pulses over M1 200 ms before rhythmic auditory events while recording motor-evoked potentials (MEPs; 19 participants), which resulted in significantly larger MEPs compared to standard single pulse TMS and an auditory control condition. Neither EEG measures during passive listening nor seTMS-induced MEP enhancement showed dependence on musical experience or training. These findings demonstrate that seTMS effectively enhances corticomotor excitability and establishes a practical, cost-effective method for optimizing non-invasive brain stimulation outcomes.

Identifying the core residual symptom in patients with major depressive disorder using network analysis and illustrating its association with prognosis: A study based on the national cohorts in China

OBJECTIVE

To identify the core residual symptom of MDD and assess its relationship with patients' long-term outcomes.

METHOD

All patients were administered antidepressants during the acute phase and treated continuously. The 521 patients remitted at month 6 of a multicenter prospective project were included. Remission was defined as a Quick Inventory of Depressive Symptoms-Self-Report total score of ≤5. Functional impairments were measured with the Sheehan Disability Scale, quality of life with the Quality of Life Enjoyment and Satisfaction Questionnaire - short form, and family burden with the Family Burden Scale of Disease. Visits were scheduled at baseline, weeks 2, 8, 12, and month 6.

RESULTS

Difficulty with concentration/decision making was the core residual symptom of MDD, determined with the centrality measure of network analysis. It was positively associated with functional impairments and family burden (r = 0.35, P < 0.01 and r = 0.31, P < 0.01, respectively) and negatively associated with life satisfaction (r = -0.29, P < 0.01). The exhibition of this residual symptom was associated with a family history of psychiatric disorders (OR = 2.610 [1.242-5.485]).

CONCLUSIONS

The core residual symptom of MDD, difficulty with concentration/decision making, is associated with poorer social functioning, heavier family burden, and lower life satisfaction. Early detection and intervention of this symptom may be beneficial.

CLINICAL TRIALS REGISTRATION NUMBER

(Chinese Clinical Trials.gov identifier) ChiCTR-OOC-17012566 and ChiCTR-INR-17012574.

Transdiagnostic risk identification: A validation study of the Clinical High At Risk Mental State (CHARMS) criteria

A set of clinical criteria, the Clinical High At-Risk Mental State (CHARMS) criteria, have been developed to identify symptomatic young people who are at-risk of disorder progression. The current study aimed to validate the CHARMS criteria by testing whether they prospectively identify individuals at-risk of progressing from attenuated symptomatology to a first episode of serious mental disorder, namely first episode psychosis, first episode mania, severe major depression, and borderline personality disorder. 121 young people completed clinical evaluations at baseline, 6- and 12-month follow-up. The Kaplan-Meier method was used to assess transition rates. Cox regression and LASSO were used to examine baseline clinical predictors of transition. Linear mixed effects modelling was used to examine symptom severity. 28 % of CHARMS+ individuals transitioned to a Stage 2 disorder by 12-month follow-up. The CHARMS+ group had more severe symptoms at follow-up than the CHARMS- group. 96 % of Stage 2 transitions were initially to severe depression. Meeting criteria for multiple CHARMS subgroups was associated with higher transition risk: meeting one at-risk group = 24 %; meeting two at-risk groups = 17 %, meeting three at-risk groups = 55 %, meeting four at-risk groups = 50 %. The strongest baseline predictor of transition was severity of depressive symptoms. The CHARMS criteria identified a group of individuals at-risk of imminent onset of severe mental disorder, particularly severe depression. Larger scale studies and longer follow-up periods are required to validate and extend these findings.

Neural effects of TMS trains on the human prefrontal cortex

How does a train of TMS pulses modify neural activity in humans? Despite adoption of repetitive TMS (rTMS) for the treatment of neuropsychiatric disorders, we still do not understand how rTMS changes the human brain. This limited understanding stems in part from a lack of methods for noninvasively measuring the neural effects of a single TMS train-a fundamental building block of treatment-as well as the cumulative effects of consecutive TMS trains. Gaining this understanding would provide foundational knowledge to guide the next generation of treatments. Here, to overcome this limitation, we developed methods to noninvasively measure causal and acute changes in cortical excitability and evaluated this neural response to single and sequential TMS trains. In 16 healthy adults, standard 10 Hz trains were applied to the dorsolateral prefrontal cortex in a randomized, sham-controlled, event-related design and changes were assessed based on the TMS-evoked potential (TEP), a measure of cortical excitability. We hypothesized that single TMS trains would induce changes in the local TEP amplitude and that those changes would accumulate across sequential trains, but primary analyses did not indicate evidence in support of either of these hypotheses. Exploratory analyses demonstrated non-local neural changes in sensor and source space and local neural changes in phase and source space. Together these results suggest that single and sequential TMS trains may not be sufficient to modulate local cortical excitability indexed by typical TEP amplitude metrics but may cause neural changes that can be detected outside the stimulation area or using phase or source space metrics. This work should be contextualized as methods development for the monitoring of transient noninvasive neural changes during rTMS and contributes to a growing understanding of the neural effects of rTMS.

Individual Prediction of Optimal Treatment Allocation Between Electroconvulsive Therapy or Ketamine using the Personalized Advantage Index

Introduction

Electroconvulsive therapy (ECT) and ketamine are two effective treatments for depression with similar efficacy; however, individual patient outcomes may be improved by models that predict optimal treatment assignment. Here, we adapt the Personalized Advantage Index (PAI) algorithm using machine learning to predict optimal treatment assignment between ECT and ketamine using medical record data from a large, naturalistic patient cohort. We hypothesized that patients who received a treatment predicted to be optimal would have significantly better outcomes following treatment compared to those who received a non-optimal treatment.

Methods

Data on 2526 ECT and 235 mixed IV ketamine and esketamine patients from McLean Hospital was aggregated. Depressive symptoms were measured using the Quick Inventory of Depressive Symptomatology (QIDS) before and during acute treatment. Patients were matched between treatments on pretreatment QIDS, age, inpatient status, and psychotic symptoms using a 1:1 ratio yielding a sample of 470 patients (n=235 per treatment). Random forest models were trained and predicted differential patientwise minimum QIDS scores achieved during acute treatment (min-QIDS) scores for ECT and ketamine using pretreatment patient measures. Analysis of Shapley Additive exPlanations (SHAP) values identified predictors of differential outcomes between treatments.

Results

Twenty-seven percent of patients with the largest PAI scores who received a treatment predicted optimal had significantly lower min-QIDS scores compared to those who received a non-optimal treatment (mean difference=1.6, t=2.38, q<0.05, Cohen's D=0.36). Analysis of SHAP values identified prescriptive pretreatment measures.

Conclusions

Patients assigned to a treatment predicted to be optimal had significantly better treatment outcomes. Our model identified pretreatment patient factors captured in medical records that can provide interpretable and actionable guidelines treatment selection.

Neural effects of TMS trains on the human prefrontal cortex

How does a train of TMS pulses modify neural activity in humans? Despite adoption of repetitive TMS (rTMS) for the treatment of neuropsychiatric disorders, we still do not understand how rTMS changes the human brain. This limited understanding stems in part from a lack of methods for noninvasively measuring the neural effects of a single TMS train - a fundamental building block of treatment - as well as the cumulative effects of consecutive TMS trains. Gaining this understanding would provide foundational knowledge to guide the next generation of treatments. Here, to overcome this limitation, we developed methods to noninvasively measure causal and acute changes in cortical excitability and evaluated this neural response to single and sequential TMS trains. In 16 healthy adults, standard 10 Hz trains were applied to the dorsolateral prefrontal cortex (dlPFC) in a randomized, sham-controlled, event-related design and changes were assessed based on the TMS-evoked potential (TEP), a measure of cortical excitability. We hypothesized that single TMS trains would induce changes in the local TEP amplitude and that those changes would accumulate across sequential trains, but primary analyses did not indicate evidence in support of either of these hypotheses. Exploratory analyses demonstrated non-local neural changes in sensor and source space and local neural changes in phase and source space. Together these results suggest that single and sequential TMS trains may not be sufficient to modulate local cortical excitability indexed by typical TEP amplitude metrics but may cause neural changes that can be detected outside the stimulation area or using phase or source space metrics. This work should be contextualized as methods development for the monitoring of transient noninvasive neural changes during rTMS and contributes to a growing understanding of the neural effects of rTMS.

Improved resilience following digital cognitive behavioral therapy for insomnia protects against insomnia and depression one year later

BACKGROUND

While the negative consequences of insomnia are well-documented, a strengths-based understanding of how sleep can increase health promotion is still emerging and much-needed. Correlational evidence has connected sleep and insomnia to resilience; however, this relationship has not yet been experimentally tested. This study examined resilience as a mediator of treatment outcomes in a randomized clinical trial with insomnia patients.

METHODS

Participants were randomized to either digital cognitive behavioral therapy for insomnia (dCBT-I; n = 358) or sleep education control (n = 300), and assessed at pre-treatment, post-treatment, and 1-year follow-up. A structural equation modeling framework was utilized to test resilience as a mediator of insomnia and depression. Risk for insomnia and depression was also tested in the model, operationalized as a latent factor with sleep reactivity, stress, and rumination as indicators (aligned with the 3-P model). Sensitivity analyses tested the impact of change in resilience on the insomnia relapse and incident depression at 1-year follow-up.

RESULTS

dCBT-I resulted in greater improvements in resilience compared to the sleep education control. Furthermore, improved resilience following dCBT-I lowered latent risk, which was further associated with reduced insomnia and depression at 1-year follow-up. Sensitivity analyses indicated that each point improvement in resilience following treatment reduced the odds of insomnia relapse and incident depression 1 year later by 76% and 65%, respectively.

CONCLUSIONS

Improved resilience is likely a contributing mechanism to treatment gains following insomnia therapy, which may then reduce longer-term risk for insomnia relapse and depression.

Assessment of the spectrum of depression and bipolarity in patients with type 1 diabetes

AIMS

The aim of the study was to check the prevalence of unipolarity (depression), bipolarity, as well as the quality of sleep and temperament traits in patients with type 1 diabetes (T1DM) who are provided with optimal conditions of diabetes care and to identify possible risk factors connected with affective traits.

MATERIALS AND METHODS

Out of the 107 T1DM patients, 78 (54 females, 24 males) were included for the analysis (HbA1c [%] 7.11 ± 1.0, BMI [kg/m2 ] 25.3 ± 5.6; Years of disease duration [N] 13.7 ± 8.3). The patients filled in a set of questionnaires during their regular visit to the outpatient clinic. Three patients from the whole group were on intensive insulin therapy with Multiple Daily Injections (MDI) and Self-Monitoring of Blood Glucose (SMBG), all the rest were on various types of personal insulin pumps (years on insulin pump [N] 9.1 ± 4.5). All the patients were on regular diabetologist care, with regular visits in a Centre for Advanced Technologies in Diabetes (at least every 6 months).

RESULTS

In QIDS-S (full explanation and abbreviation 26 patients (33.8%) were screened positive for depression, in PHQ (full explanation and ab 57.7% of the patients (45 patients) had symptoms of depression (age was negatively correlated with PHQ score [r = -0.26; p = 0.023]). In CES-D 16 (20%) of the patients assessed their present affect as depressed. None of the analysed clinical variables correlated with depression scores. In the Mood Disorder Questionnaire (MDQ), 16 patients reported having symptoms of bipolarity (20.5% vs. 79.5%). Hypomania Checklist (HCL) analysis indicated 10 patients with bipolar traits (>14) (14.9% vs. 85.1%). None of the analysed clinical variables correlated with HCL results. 11.5% of patients were indicated to be of morning type. Morningness was more often seen in younger patients (r = 0.39; p = 0.001). As many as 46.6% declared that they had poor sleep quality. The temperament traits analysis correlated with clinical parameters: Cyclothymic temperament trait was negatively correlated with age (r = -0.30; p = 0.007) and positively with HbA1c level (r = 0.30; p = 0.025). Hyperthymic temperament was positively correlated with (BMI r = 0.28; p = 0.016). Quality of sleep was highly correlated with depressive symptoms CESD (r = 0.61, p = 0.001), PHQ Score (r = 0.62; p = 0.001), QISD (r = 0.68; p = 0.001) and bipolarity MDQ (p = 0.50, p = 0.001) and HCL (r = 0.42, p = 0.001). In addition, QIDS was shown to be correlated with the following features of temperament: depressive factor (r = 0.41; p = 0.001), irritable factor (r = 0.53; p = 0.001), cyclothymic factor (r = 0.59; p = 0.001), anxious factor (r = 0.58, p = 0.001).

CONCLUSIONS

The prevalence of affective disorders and poor sleep quality in the examined T1DM patients was much higher than in the general population. Even if the patients have in general good glycaemic control, their mental health condition should not be neglected. Well organised cooperation between patients, diabetologists, psychiatrists and psychotherapists is needed (Clinical Trials Identifier: NCT04616391).

Identification of Endocannabinoid Predictors of Treatment Outcomes in Major Depressive Disorder: A Secondary Analysis of the First Canadian Biomarker Integration Network in Depression (CAN-BIND 1) Study

INTRODUCTION

An increasing number of studies are examining the link between the endocannabinoidome and major depressive disorder (MDD). We conducted an exploratory analysis of this system to identify potential markers of treatment outcomes.

METHODS

The dataset of the Canadian Biomarker Integration Network in Depression-1 study, consisting of 180 patients with MDD treated for eight weeks with escitalopram followed by eight weeks with escitalopram alone or augmented with aripiprazole was analyzed. Association between response Montgomery-Asberg Depression Rating Scale (MADRS; score reduction≥50%) or remission (MADRS score≤10) at weeks 8 and 16 and single nucleotide polymorphisms (SNPs), methylation, and mRNA levels of 33 endocannabinoid markers were examined. A standard genome-wide association studies protocol was used for identifying SNPs, and logistic regression was used to assess methylation and mRNA levels.

RESULTS

Lower methylation of CpG islands of the diacylglycerol lipase alpha gene (DAGLA) was associated with non-remission at week 16 (DAGLA; OR=0.337, p<0.003, q=0.050). Methylation of DAGLA was correlated with improvement in Clinical Global Impression (p=0.026), Quick Inventory of Depressive Symptomatology (p=0.010), and Snaith-Hamilton Pleasure scales (p=0.028). We did not find any association between SNPs or mRNA levels and treatment outcomes.

DISCUSSION

Methylation of DAGLA is a promising candidate as a marker of treatment outcomes for MDD and needs to be explored further.

Optimization of measurement-based care (OMBC) for depression based on all-round and continuous assessment: rationale and protocol for a multicenter randomized control clinical trial

BACKGROUND

Despite the recent findings presenting the benefits of measurement-based care (MBC) compared to treatment as usual (TAU), MBC is still not the standard of care used in clinical settings. The aim of the present study was to achieve the optimization of MBC (OMBC) for major depressive disorder (MDD) by establishing a comprehensive MBC framework based on all-round, continuous assessment for depression.

METHODS

The target recruitment size is 900 patients, and the study is conducted at 8 centers in China. The patients are randomly assigned to the MBC and TAU groups at a 2:1 ratio. The subjects are scheduled to remain for 12 weeks in the acute phase and for 12 months in the maintenance phase. The primary outcomes are the complete remission rate and the proportion of patients with a 16-item Quick Inventory of Depressive Symptomatology-Self-Report (QIDS-SR 16) total score ≤ 5 of the MBC and TAU groups at the acute phase, and the recurrence rate/time between the two groups is measured at the maintenance phase. Secondary outcomes included the changes in the parameters QIDS-SR 16, Patient Health Questionnaire-9 (PHQ-9), and 17-item Hamilton Rating Scale for Depression (HAMD-17) from baseline and the response rate between the two groups at the acute phase as well as the comparison of recurrence rate between the two groups at the end of the study.

TRIAL REGISTRATION

Chinese Clinical Trial Registry, ChiCTR-OOC-17012566 . The registration was performed retrospectively on 4 September 2017.

Evaluating the Clinical Feasibility of an Artificial Intelligence-Powered, Web-Based Clinical Decision Support System for the Treatment of Depression in Adults: Longitudinal Feasibility Study

BACKGROUND

Approximately two-thirds of patients with major depressive disorder do not achieve remission during their first treatment. There has been increasing interest in the use of digital, artificial intelligence-powered clinical decision support systems (CDSSs) to assist physicians in their treatment selection and management, improving the personalization and use of best practices such as measurement-based care. Previous literature shows that for digital mental health tools to be successful, the tool must be easy for patients and physicians to use and feasible within existing clinical workflows.

OBJECTIVE

This study aims to examine the feasibility of an artificial intelligence-powered CDSS, which combines the operationalized 2016 Canadian Network for Mood and Anxiety Treatments guidelines with a neural network-based individualized treatment remission prediction.

METHODS

Owing to the COVID-19 pandemic, the study was adapted to be completed entirely remotely. A total of 7 physicians recruited outpatients diagnosed with major depressive disorder according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition criteria. Patients completed a minimum of one visit without the CDSS (baseline) and 2 subsequent visits where the CDSS was used by the physician (visits 1 and 2). The primary outcome of interest was change in appointment length after the introduction of the CDSS as a proxy for feasibility. Feasibility and acceptability data were collected through self-report questionnaires and semistructured interviews.

RESULTS

Data were collected between January and November 2020. A total of 17 patients were enrolled in the study; of the 17 patients, 14 (82%) completed the study. There was no significant difference in appointment length between visits (introduction of the tool did not increase appointment length; F2,24=0.805; mean squared error 58.08; P=.46). In total, 92% (12/13) of patients and 71% (5/7) of physicians felt that the tool was easy to use; 62% (8/13) of patients and 71% (5/7) of physicians rated that they trusted the CDSS. Of the 13 patients, 6 (46%) felt that the patient-clinician relationship significantly or somewhat improved, whereas 7 (54%) felt that it did not change.

CONCLUSIONS

Our findings confirm that the integration of the tool does not significantly increase appointment length and suggest that the CDSS is easy to use and may have positive effects on the patient-physician relationship for some patients. The CDSS is feasible and ready for effectiveness studies.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04061642; http://clinicaltrials.gov/ct2/show/NCT04061642.

Depression prevention in digital cognitive behavioral therapy for insomnia: Is rumination a mediator?

Background There has been growing support for digital Cognitive Behavioral Therapy (dCBT-I) as a scalable intervention that both reduces insomnia and prevents depression. However, the mechanisms by which dCBT-I reduces and prevents depression is less clear. Methods This was a randomized controlled trial with two parallel arms: dCBT-I (N=358), or online sleep education as the control condition (N=300). Outcome variables were measured at pre-treatment, post-treatment, and one-year follow-up, and included the Insomnia Severity Index (ISI), the Quick Inventory of Depressive Symptomatology (QIDS-SR₁₆), and the Perseverative Thinking Questionnaire (PTQ). The analyses tested change in PTQ scores as a mediator for post-treatment insomnia, post-treatment depression, and incident depression at one-year follow-up. Results Reductions in rumination (PTQ) were significantly larger in the dCBT-I condition compared to control. Results also showed that reductions in rumination significantly mediated the improvement in post-treatment insomnia severity (proportional effect = 11%) and post-treatment depression severity (proportional effect = 19%) associated with the dCBT-I condition. Finally, reductions in rumination also significantly mediated the prevention of clinically significant depression via dCBT-I (proportional effect = 42%). Limitations Depression was measured with a validated self-report instrument instead of clinical interviews. Durability of results beyond one-year follow-up should also be tested in future research. Conclusions Results provide evidence that rumination is an important mechanism in how dCBT-I reduces and prevents depression.

The effects of intramuscular administration of scopolamine augmentation in moderate to severe major depressive disorder: a randomized, double-blind, placebo-controlled trial

INTRODUCTION

Major depressive disorder (MDD) is a common affective disorder. Currently established pharmacotherapies lack rapid clinical response, thereby limiting their ability to bring instant relief to patients. A series of clinical trials has demonstrated the antidepressant effects of scopolamine, yet few have studied the effects of add-on scopolamine to currently available antidepressants. It is not known whether conventional antidepressant treatment with a 3-day scopolamine injection could speed up oral antidepressant efficacy. The main focus of this study is to detect the capacity of the rapid-onset efficacy of such a treatment option.

METHODS AND ANALYSIS

This study consisted of a single-centre, double-blind, three-arm randomized trial with a 4-week follow-up period. Sixty-six participants meeting entry criteria were randomly allocated to three treatment groups: a high-dose group, a low-dose group and a placebo control group. Psychiatric rating scales were administered at baseline and seven viewing points following the administration of intramuscular injections. The primary outcome measure was length of time from randomization (baseline) to early improvement.

RESULTS

Both primary and secondary outcome measures consistently showed no differences among the three groups. The cumulative response rate and the remission rate were 72.7% (48/66) and 47.0% (31/66). Intramuscular scopolamine treatment was relatively well tolerated. Two subjects with high-dose injections dropped out because of a drug-related side effect.

CONCLUSION

Contrary to our prediction, we found that, compared to placebo (0.9% saline i.m.), scopolamine was not associated with a significantly faster antidepressant response rate.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT03131050. Registered on 18 April 2017.

Depression prevention via digital cognitive behavioral therapy for insomnia: a randomized controlled trial

STUDY OBJECTIVES

Insomnia is a common precursor to depression; yet, the potential for insomnia treatment to prevent depression has not been demonstrated. Cognitive behavioral therapy for insomnia (CBT-I) effectively reduces concurrent symptoms of insomnia and depression and can be delivered digitally (dCBT-I); however, it remains unclear whether treating insomnia leads to sustained reduction and prevention of depression. This randomized controlled trial examined the efficacy of dCBT-I in reducing and preventing depression over a 1-year follow-up period.

METHODS

Patients with Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) insomnia disorder were randomly assigned to receive dCBT-I or an attentional control. The follow-up sample included 358 patients in the dCBT-I condition and 300 patients in the online sleep education condition. The primary outcome measure was relative rate ratios for depression at 1-year follow-up. Insomnia responses to treatment were also tested as predictors of incident depression at the 1-year follow-up.

RESULTS

At 1-year follow-up, depression severity continued to be significantly lower in the dCBT-I condition relative to control. In addition, the number of individuals who reported no depression at 1-year follow-up was 51% higher in the dCBT-I condition relative to control. In those with minimal to no depression at baseline, the incident rate of moderate-to-severe depression at 1-year follow-up was reduced by half in the dCBT-I condition relative to the control condition.

CONCLUSION

dCBT-I showed robust effects as an intervention that prevents depression. Future research should examine dose-response requirements and further characterize mechanisms of action of dCBT-I for depression prevention.

CLINICAL TRIAL

Sleep to Prevent Evolving Affective Disorders; NCT02988375.

Modified Cognitive Behavioral Therapy for Insomnia in Depressed Adolescents: A Pilot Study

Objective/Background: The purpose of the study was to pilot a five-week insomnia treatment in adolescents with major depressive disorder (MDD) and insomnia. This was an open-label trial of a modified-group cognitive behavioral therapy for insomnia (CBTI). Participants: Adolescents with MDD (n = 16; mean age = 17.3 +/- 1.7), characterized by the Children's Depression Rating Scale-Revised T-score ≥ 55 and insomnia, characterized by > 30 min to fall or return to sleep and an Insomnia Severity Index (ISI) score of ≥ 7 participated. Methods: Sleep diaries, actigraphy, weekly ISI, Quick Inventory of Depressive Symptomatology (QIDS), and Multidimensional Fatigue Inventory (MFI) were completed. Results: Paired t-tests comparing pre- and posttreatment revealed a decrease in sleep onset latency from 41 min +/- 14 min to 18 min +/- 8.9 min (t = 5.9, p = .004). Linear mixed modeling across sessions revealed that ISI (B = 11.0, SE = 0.94, p < .001), QIDS (B = 11.3, SE = 0.96, p < .001), and MFI (B = 30.0, SE = 4.4, p < .001) improved across treatment. Daily sleep diaries showed decreased wake during the night (B = 22.8, SE = 7.19, p = .008), increased sleep time (B = 382.4, SE = 71.89, p < .001), and increased quality of sleep (B = 3.7, SE = 0.37, p < .001). When asked whether group members would recommend this group, 27% responded "yes" and 73% responded "definitely yes." Conclusions: Additional controlled studies utilizing sleep-focused therapy in depressed adolescents with insomnia are warranted.

Efficacy of digital CBT for insomnia to reduce depression across demographic groups: a randomized trial

BACKGROUND

Insomnia and depression are highly comorbid and mutually exacerbate clinical trajectories and outcomes. Cognitive behavioral therapy for insomnia (CBT-I) effectively reduces both insomnia and depression severity, and can be delivered digitally. This could substantially increase the accessibility to CBT-I, which could reduce the health disparities related to insomnia; however, the efficacy of digital CBT-I (dCBT-I) across a range of demographic groups has not yet been adequately examined. This randomized placebo-controlled trial examined the efficacy of dCBT-I in reducing both insomnia and depression across a wide range of demographic groups.

METHODS

Of 1358 individuals with insomnia randomized, a final sample of 358 were retained in the dCBT-I condition and 300 in the online sleep education condition. Severity of insomnia and depression was examined as a dependent variable. Race, socioeconomic status (SES; household income and education), gender, and age were also tested as independent moderators of treatment effects.

RESULTS

The dCBT-I condition yielded greater reductions in both insomnia and depression severity than sleep education, with significantly higher rates of remission following treatment. Demographic variables (i.e. income, race, sex, age, education) were not significant moderators of the treatment effects, suggesting that dCBT-I is comparably efficacious across a wide range of demographic groups. Furthermore, while differences in attrition were found based on SES, attrition did not differ between white and black participants.

CONCLUSIONS

Results provide evidence that the wide dissemination of dCBT-I may effectively target both insomnia and comorbid depression across a wide spectrum of the population.

Scopolamine augmentation of a newly initiated escitalopram treatment for major depressive disorder: study protocol for a randomized controlled trial

Background

Major depressive disorder (MDD) is a prevalent and disabling disorder that can lead to heavy individual, familial, and societal burdens. Although pharmaceutical interventions still play an essential role in therapeutic measures, limitations, including effects that are delayed for weeks, are noteworthy. Antidepressants with rapid efficacy and acceptable tolerance have been investigated for many years; rapid antidepressant effects and promising clinical applications have been obtained with intravenous and oral scopolamine. This study aims to evaluate the efficacy of repeated intramuscular scopolamine as an add-on treatment to escitalopram.

Methods

This is a single-center, saline-controlled, double-blind, three-armed, randomized trial. Sixty-six participants diagnosed with MDD will be recruited at Beijing Anding Hospital and randomly assigned to one of three groups: a high-dose intramuscular scopolamine augmentation group; a low-dose intramuscular scopolamine augmentation group; and a placebo control group. Our primary endpoint is improvement in the 17-Item Hamilton Rating Scale for Depression (HRSD17) score from the baseline (at least a 20% reduction). Prespecified secondary endpoints include response rates and remission rates as well as changes in the total or subscale scores between the baseline and week 4.

Discussion

This study will provide the first insight regarding the rapid antidepressant efficacy and tolerability of an intramuscular scopolamine add-on to the usual treatment in Chinese MDD patients. The first discussion concerns whether augmentation can accelerate early antidepressant efficacy. A pilot study of intramuscular scopolamine is performed.

The limitations of this study include its small sample size and it being a single-center study, suggesting the need for further confirmation with trials enrolling larger populations.

Ethics and dissemination

The study protocol and all related materials have been approved by the Institutional Ethics Committee of the Beijing Anding Hospital (No. 2016–106, Beijing, China). The findings will be disseminated through peer-reviewed journals and at national and international conferences.

Trial registration

ClinicalTrials.gov, NCT03131050. Registered on 18 April 2017.

Electronic supplementary material

The online version of this article (10.1186/s13063-018-3132-3) contains supplementary material, which is available to authorized users.

Translating the HAM-D into the MADRS and vice versa with equipercentile linking

BACKGROUND

The Hamilton Depression Rating Scale (HAM-D) and the Montgomery Asberg Depression Rating Scale (MADRS) are scales used frequently to rate the symptoms of depression. There are many situations in which it is important to know what a given total score or a percent reduction from baseline score of one scale means in relation to the other scale.

METHOD

We used the equipercentile linking method to identify corresponding scores of simultaneous HAM-D and MADRS ratings in 4388 patients from 31 mirtazapine trials in major depressive disorder. Data were collected at baseline and at weeks 1, 2 and 4.

RESULTS

HAM-D scores of 10, 20, 30 and 40 roughly corresponded to MADRS scores of 13, 26, 39 and 52-53, respectively. An absolute HAM-D improvement of 10, 20, 25 points corresponded to a MADRS improvement of 12, 26, and 34. A percentage improvement from baseline of the HAM-D was approximately the same as a percentage improvement on the MADRS.

CONCLUSION

These results are important for the comparison of trials that used the HAM-D and MADRS. We present conversion tables for future research.

Psychometric properties of the 16-item Quick Inventory of Depressive Symptomatology: a systematic review and meta-analysis

Effective management of depression is predicated upon reliable assessment. The Quick Inventory of Depressive Symptomatology (QIDS) is a depression severity scale with both self-rated (QIDS-SR16) and clinician-rated (QIDS-C16) versions. Although widely used in research, the psychometric properties of the QIDS16 have not been systematically reviewed. We performed a systematic review of studies of the psychometric properties (factor structure, internal consistency, convergent validity, discriminant validity, test-retest reliability and responsiveness to change) of the QIDS-SR16 or QIDS-C16. Six databases were searched: MEDLINE, EMBASE, PsycINFO, CinAHL, Web of Science and the Cochrane Central Register of Controlled Trials. Findings were summarised, bias assessed and correlations with reference standards were pooled. 37 studies (17,118 participants) were included in the review. Both versions of the QIDS16 were unidimensional. Cronbach's alpha ranged from 0.69 to 0.89 for the QIDS-SR16 and 0.65 to 0.87 for the QIDS-C16. The QIDS-SR16 correlated moderately to highly with several depression severity scales. Seven studies were pooled where QIDS-SR16 was correlated with the HRSD-17 (r = 0.76, CI 0.69, 0.81) in patients diagnosed with depression. Four studies examined convergent validity with the QIDS-C16. Four studies examined discriminant validity, for the QIDS-SR16 alone. Eighteen studies had at least one author who was a co-author of the original QIDS16 study. Most studies were conducted in the USA (n = 26). The QIDS-SR16 and the QIDS-C16 are unidimensional rating scales with acceptable internal consistency. To justify the use of the QIDS16 scale in clinical practice, more research is needed on convergent and discriminant validity, and in populations outside the USA.

The Spanish version of the Quick Inventory of Depressive Symptomatology-Self-Report (QIDS-SR16): a psychometric analysis in a clinical sample

BACKGROUND

Psychometrically robust and easy-to-administer scales for depressive symptoms are necessary for research and clinical assessment. This is a psychometric study of the Spanish version of the Quick Inventory of Depressive Symptomatology-Self-Report (QIDS-SR16) in a clinical sample.

METHOD

One-hundred and seventy-three patients (65% women) with a psychiatric disorder including depressive symptoms were recruited. Such symptoms were assessed by means of the QIDS-SR16 and two interviewer-rated instruments: the 17-item Hamilton Depression Rating Scale (HDRS17) and the Clinical Global Impression-Severity (CGI-S) scale. Self-rated measures of health-related quality of life, subjective happiness and perceived social support were also obtained. Dimensionality, internal consistency, construct validity, criterion validity, and responsiveness to change of the QIDS-SR16 were examined.

RESULTS

Exploratory and confirmatory factor analyses replicated the original one-factor structure. The Spanish version of the QIDS-SR16 showed good to excellent internal consistency (α=0.88), convergent validity [HDRS17 (r=0.77), CGI-S (r=0.78)], and divergent validity [EuroQol-5D Visual Analogue Scale (r=-0.78), Subjective Happiness Scale (r=-0.72)]. The QIDS-SR16 was excellent in discriminating clinically significant from non-significant depressive symptomatology (area under ROC curve=0.93). It also showed a high sensitivity to treatment-related changes: patients with greater clinical improvement showed a greater decrease in QIDS-SR16 scores (p<0.001).

LIMITATIONS

The study was conducted in a single center, which may limit the generalizability of the findings.

CONCLUSIONS

The Spanish version of the QIDS-SR16 retains the soundness of metric characteristics of the original version which makes the scale an invaluable instrument to assess depressive symptoms.