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BACKGROUND: The Canadian Network for Mood and Anxiety Treatments (CANMAT) last published clinical guidelines for the management of major depressive disorder (MDD) in 2016. Owing to advances in the field, an update was needed to incorporate new evidence and provide new and revised recommendations for the assessment and management of MDD in adults. METHODS: CANMAT convened a guidelines editorial group comprised of academic clinicians and patient partners. A systematic literature review was conducted, focusing on systematic reviews and meta-analyses published since the 2016 guidelines. Recommendations were organized by lines of treatment, which were informed by CANMAT-defined levels of evidence and supplemented by clinical support (consisting of expert consensus on safety, tolerability, and feasibility). Drafts were revised based on review by patient partners, expert peer review, and a defined expert consensus process. RESULTS: The updated guidelines comprise eight primary topics, in a question-and-answer format, that map a patient care journey from assessment to selection of evidence-based treatments, prevention of recurrence, and strategies for inadequate response. The guidelines adopt a personalized care approach that emphasizes shared decision-making that reflects the values, preferences, and treatment history of the patient with MDD. Tables provide new and updated recommendations for psychological, pharmacological, lifestyle, complementary and alternative medicine, digital health, and neuromodulation treatments. Caveats and limitations of the evidence are highlighted. CONCLUSIONS: The CANMAT 2023 updated guidelines provide evidence-informed recommendations for the management of MDD, in a clinician-friendly format. These updated guidelines emphasize a collaborative, personalized, and systematic management approach that will help optimize outcomes for adults with MDD.
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BACKGROUND: Patients with major depressive disorder (MDD) can present with altered brain structure and deficits in cognitive function similar to aging. Yet, the interaction between age-related brain changes and brain development in MDD remains understudied. In a cohort of adolescents and adults with and without MDD, we assessed brain aging differences and associations through a newly developed tool quantifying normative neurodevelopmental trajectories. METHODS: 304 MDD participants and 236 non-depressed controls were recruited and scanned from three studies under the Canadian Biomarker Integration Network for Depression. Volumetric data were used to generate brain centile scores, which were examined for: a) differences in MDD relative to controls; b) differences in individuals with versus without severe childhood maltreatment; and c) correlations with depressive symptom severity, neurocognitive assessment domains, or escitalopram treatment response. RESULTS: Brain centiles were significantly lower in the MDD group compared to controls. It was also significantly correlated with working memory in controls, but not the MDD group. No significant associations were observed in depression severity or antidepressant treatment response with brain centiles. Likewise, childhood maltreatment history did not significantly affect brain centiles. CONCLUSIONS: Consistent with prior work on machine learning models that predict "brain age", brain centile scores differed in people diagnosed with MDD, and MDD was associated with differential relationships between centile scores and working memory. The results support the notion of atypical development and aging in MDD, with implications on neurocognitive deficits associated with aging-related cognitive function.
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Introduction: Pain management in patients with chronic pain and comorbid depression is challenging and understudied. There is interest in intermittent theta-burst stimulation (iTBS), a new modality of repetitive transcranial magnetic stimulation (rTMS). This retrospective review describes changes in pain, anxiety and depression throughout iTBS treatment at the dorsolateral prefrontal cortex (DLPFC). Methods: A retrospective chart review was conducted of patients who underwent their first acute series of iTBS treatments at the DLPFC for depression at a single institution between 2020 and 2023. Data on depression, anxiety, and pain were collected throughout iTBS treatment using the Beck Depression Inventory-II (BDI-II; higher scores indicate worse depression) and visual analogue scale (VAS; 0-100, higher scores indicate worse pain, anxiety, and depression). Nonparametric tests were used for all analyses. Results: Of 104 patients, 52 reported moderate pain at baseline (50.0%). Median BDI-II scores decreased from 38.0 (interquartile range [IQR] = 29.0-44.0) to 24.0 (IQR = 9.0-36.0) from pre- to posttreatment (P < 0.001). Of the 32 patients with both pre- and posttreatment pain scores, there was a significant decrease from 40.0 (IQR = 5.5-71.8) to 15.0 (IQR = 3.5-53.8; P = 0.037). In patients with at least moderate pain at baseline, pain scores decreased from 71.0 (IQR = 55.0-80.0) to 20.0 (IQR = 11.0-71.0; P = 0.004). Ten of 32 patients with available pre- and posttreatment scores reported ≥30% reduction in pain scores (31.2%). Conclusion: These preliminary results, suggesting decreases in pain following iTBS treatment, provide a rationale for future rigorous investigations to evaluate this intervention for depression and comorbid chronic pain.
Introduction: La prise en charge de la douleur chez les patients souffrant de douleur chronique et de dépression comorbide est difficile et sous-étudiée. Il existe un intérêt pour la stimulation thêta-burst intermittente (STBi), une nouvelle modalité de stimulation magnétique transcrânienne répétitive (SMTr). Cette revue rétrospective décrit les changements dans la douleur, l'anxiété et la dépression tout au long du traitement par STBi au niveau de la région du cortex préfrontal dorsolatéral.Un examen rétrospectif des dossiers a été mené pour les patients ayant reçu leur première série intensive de traitements par STBi pour la dépression dans la région du cortex préfrontal dorsolatéral dans un seul établissement entre 2020 et 2023. Les données sur la dépression, l'anxiété et la douleur ont été collectées tout au long des traitements par STBi à l'aide de l'Inventaire de dépression de Beck-II (IDB-II; des scores plus élevés indiquent une dépression plus grave) et de l'échelle visuelle analogique (EVA; 0-100, des scores plus élevés indiquent une aggravation de la douleur, de l'anxiété et de la dépression). Des tests non paramétriques ont été utilisés pour toutes les analyses.Résultats: Parmi les 104 patients inclus dans l'étude, 52 ont déclaré une douleur modérée au départ (50,0 %). Les scores médians de l'IDB-II ont diminué de 38,0 (intervalle interquartile [IQR] = 29,0-44,0) à 24,0 (IQR = 9,0-36,0) avant et après le traitement (P < 0,001). Parmi les 32 patients pour lesquels des scores de douleur avant et après le traitement étaient disponibles, une diminution significative a été constatée, passant de 40,0 (IQR = 5,5-71,8) à 15,0 (IQR = 3,5-53,8 ; P = 0,037). Chez les patients hospitalisés avec une douleur au moins modérée au départ, les scores de douleur ont diminué de 71,0 (IQR = 55,0-80,0) à 20,0 (IQR = 11,0-71,0 ; P = 0,004). Dix des 32 patients pour lesquels des scores avant et après le traitement étaient disponibles ont rapporté une réduction ≥30 % des scores de douleur (31,2 %).Conclusion: Ces résultats préliminaires, qui indiquent une diminution de la douleur après le traitement par STBi, offrent une justification pour la réalisation de futures études rigoureuses afin d'évaluer cette intervention pour la dépression et les douleurs chroniques comorbides.
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BACKGROUND: Identifying neuroimaging biomarkers of antidepressant response may help guide treatment decisions and advance precision medicine. AIMS: To examine the relationship between anhedonia and functional neurocircuitry in key reward processing brain regions in people with major depressive disorder receiving aripiprazole adjunct therapy with escitalopram. METHOD: Data were collected as part of the CAN-BIND-1 study. Participants experiencing a current major depressive episode received escitalopram for 8 weeks; escitalopram non-responders received adjunct aripiprazole for an additional 8 weeks. Functional magnetic resonance imaging (on weeks 0 and 8) and clinical assessment of anhedonia (on weeks 0, 8 and 16) were completed. Seed-based correlational analysis was employed to examine the relationship between baseline resting-state functional connectivity (rsFC), using the nucleus accumbens (NAc) and anterior cingulate cortex (ACC) as key regions of interest, and change in anhedonia severity after adjunct aripiprazole. RESULTS: Anhedonia severity significantly improved after treatment with adjunct aripiprazole.There was a positive correlation between anhedonia improvement and rsFC between the ACC and posterior cingulate cortex, ACC and posterior praecuneus, and NAc and posterior praecuneus. There was a negative correlation between anhedonia improvement and rsFC between the ACC and anterior praecuneus and NAc and anterior praecuneus. CONCLUSIONS: Eight weeks of aripiprazole, adjunct to escitalopram, was associated with improved anhedonia symptoms. Changes in functional connectivity between key reward regions were associated with anhedonia improvement, suggesting aripiprazole may be an effective treatment for individuals experiencing reward-related deficits. Future studies are required to replicate our findings and explore their generalisability, using other agents with partial dopamine (D2) agonism and/or serotonin (5-HT2A) antagonism.
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Neural network-level changes underlying symptom remission in major depressive disorder (MDD) are often studied from a single perspective. Multimodal approaches to assess neuropsychiatric disorders are evolving, as they offer richer information about brain networks. A FATCAT-awFC pipeline was developed to integrate a computationally intense data fusion method with a toolbox, to produce a faster and more intuitive pipeline for combining functional connectivity with structural connectivity (denoted as anatomically weighted functional connectivity (awFC)). Ninety-three participants from the Canadian Biomarker Integration Network for Depression study (CAN-BIND-1) were included. Patients with MDD were treated with 8 weeks of escitalopram and adjunctive aripiprazole for another 8 weeks. Between-group connectivity (SC, FC, awFC) comparisons contrasted remitters (REM) with non-remitters (NREM) at baseline and 8 weeks. Additionally, a longitudinal study analysis was performed to compare connectivity changes across time for REM, from baseline to week-8. Association between cognitive variables and connectivity were also assessed. REM were distinguished from NREM by lower awFC within the default mode, frontoparietal, and ventral attention networks. Compared to REM at baseline, REM at week-8 revealed increased awFC within the dorsal attention network and decreased awFC within the frontoparietal network. A medium effect size was observed for most results. AwFC in the frontoparietal network was associated with neurocognitive index and cognitive flexibility for the NREM group at week-8. In conclusion, the FATCAT-awFC pipeline has the benefit of providing insight on the 'full picture' of connectivity changes for REMs and NREMs while making for an easy intuitive approach.
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ABSTRACT: We compared a combination of the nonsedating antioxidant, alpha-lipoic acid (ALA), with the sedating anticonvulsant, pregabalin, vs each monotherapy to treat neuropathic pain due to peripheral neuropathies. In this randomized, double-blind, 3-period crossover trial, participants received oral ALA, pregabalin, and their combination-each for 6 weeks. The primary outcome was mean daily pain intensity at maximal tolerated doses (MTD); secondary outcomes included quality of life (SF-36), sleep (Medical Outcomes Study-Sleep Scale), adverse effects, drug doses, and other measures. Of 55 participants randomized (20-diabetic neuropathy, 19-small fiber neuropathy, and 16-other neuropathies), 46 completed 2 periods, and 44 completed 3. At MTD, the primary outcome of mean pain intensity (0-10) was 5.32 (standard error, SE = 0.18), 3.96 (0.25), 3.25 (0.25), and 3.16 (0.25) at baseline, ALA, pregabalin, and combination, respectively ( P < 0.01 for ALA vs combination and pregabalin). Treatment differences were similar in subgroups with diabetic neuropathy and with other neuropathies. SF-36 total scores (higher number indicates better quality of life) were 66.6 (1.88), 70.1 (1.88), and 69.4 (1.87) with ALA, pregabalin, and combination ( P < 0.05 for ALA vs combination and pregabalin). At MTD, there were no statistically significant treatment differences in adverse effects or drug doses. This trial demonstrates superiority of pregabalin vs ALA but provides no evidence to suggest added benefit of combining ALA with pregabalin to treat neuropathic pain.
Subject(s)
Diabetic Neuropathies , Neuralgia , Thioctic Acid , Humans , Pregabalin/therapeutic use , Thioctic Acid/therapeutic use , Diabetic Neuropathies/drug therapy , Analgesics/therapeutic use , Quality of Life , gamma-Aminobutyric Acid/therapeutic use , Treatment Outcome , Neuralgia/drug therapy , Neuralgia/chemically induced , Double-Blind MethodABSTRACT
OBJECTIVES: Treatment-emergent sexual dysfunction is frequently reported by individuals with major depressive disorder (MDD) on antidepressants, which negatively impacts treatment adherence and efficacy. We investigated the association of polymorphisms in pharmacokinetic genes encoding cytochrome-P450 drug-metabolizing enzymes, CYP2C19 and CYP2D6, and the transmembrane efflux pump, P-glycoprotein (i.e., ABCB1), on treatment-emergent changes in sexual function (SF) and sexual satisfaction (SS) in the Canadian Biomarker Integration Network in Depression 1 (CAN-BIND-1) sample. METHODS: A total of 178 adults with MDD received treatment with escitalopram (ESC) from weeks 0-8 (Phase I). At week 8, nonresponders were augmented with aripiprazole (ARI) (i.e., ESC + ARI, n = 91), while responders continued ESC (i.e., ESC-Only, n = 80) from weeks 8-16 (Phase II). SF and SS were evaluated using the sex effects (SexFX) scale at weeks 0, 8, and 16. We assessed the primary outcomes, SF and SS change for weeks 0-8 and 8-16, using repeated measures mixed-effects models. RESULTS: In ESC-Only, CYP2C19 intermediate metabolizer (IM) + poor metabolizers (PMs) showed treatment-related improvements in sexual arousal, a subdomain of SF, from weeks 8-16, relative to CYP2C19 normal metabolizers (NMs) who showed a decline, F(2,54) = 8.00, p < 0.001, q = 0.048. Specifically, CYP2C19 IM + PMs reported less difficulty with having and sustaining vaginal lubrication in females and erection in males, compared to NMs. Furthermore, ESC-Only females with higher concentrations of ESC metabolite, S-desmethylcitalopram (S-DCT), and S-DCT/ESC ratio in serum demonstrated more decline in SF (r = -0.42, p = 0.004, q = 0.034) and SS (r = -0.43, p = 0.003, q = 0.034), respectively, which was not observed in males. ESC-Only females also demonstrated a trend for a correlation between S-DCT and sexual arousal change in the same direction (r = -0.39, p = 0.009, q = 0.052). CONCLUSIONS: CYP2C19 metabolizer phenotypes may be influencing changes in sexual arousal related to ESC monotherapy. Thus, preemptive genotyping of CYP2C19 may help to guide selection of treatment that circumvents selective serotonin reuptake inhibitor-related sexual dysfunction thereby improving outcomes for patients. Additionally, further research is warranted to clarify the role of S-DCT in the mechanisms underlying ESC-related changes in SF and SS. This CAN-BIND-1 study was registered on clinicaltrials.gov (Identifier: NCT01655706) on 27 July 2012.
Subject(s)
Cytochrome P-450 CYP2D6 , Depressive Disorder, Major , Adult , Male , Female , Humans , Cytochrome P-450 CYP2D6/genetics , Cytochrome P-450 CYP2D6/metabolism , Aripiprazole/adverse effects , Escitalopram , Citalopram/adverse effects , Depressive Disorder, Major/drug therapy , Depressive Disorder, Major/genetics , Cytochrome P-450 CYP2C19/genetics , Cytochrome P-450 CYP2C19/metabolism , Depression , Canada , Biomarkers , ATP Binding Cassette Transporter, Subfamily BABSTRACT
Preclinical research implicates stress-induced upregulation of the enzyme, serum- and glucocorticoid-regulated kinase 1 (SGK1), in reduced hippocampal volume. In the current study, we tested the hypothesis that greater SGK1 mRNA expression in humans would be associated with lower hippocampal volume, but only among those with a history of prolonged stress exposure, operationalized as childhood maltreatment (physical, sexual, and/or emotional abuse). Further, we examined whether baseline levels of SGK1 and hippocampal volume, or changes in these markers over the course of antidepressant treatment, would predict treatment outcomes in adults with major depression [MDD]. We assessed SGK1 mRNA expression from peripheral blood, and left and right hippocampal volume at baseline, as well as change in these markers over the first 8 weeks of a 16-week open-label trial of escitalopram as part of the Canadian Biomarker Integration Network in Depression program (MDD [n = 161] and healthy comparison participants [n = 91]). Childhood maltreatment was assessed via contextual interview with standardized ratings. In the full sample at baseline, greater SGK1 expression was associated with lower hippocampal volume, but only among those with more severe childhood maltreatment. In individuals with MDD, decreases in SGK1 expression predicted lower remission rates at week 16, again only among those with more severe maltreatment. Decreases in hippocampal volume predicted lower week 16 remission for those with low childhood maltreatment. These results suggest that both glucocorticoid-related neurobiological mechanisms of the stress response and history of childhood stress exposure may be critical to understanding differential treatment outcomes in MDD. ClinicalTrials.gov: NCT01655706 Canadian Biomarker Integration Network for Depression Study.
Subject(s)
Child Abuse , Depressive Disorder, Major , Adult , Child , Humans , Biomarkers , Canada , Depression , Depressive Disorder, Major/drug therapy , Depressive Disorder, Major/genetics , Gene Expression , Glucocorticoids/metabolism , Hippocampus/diagnostic imaging , Magnetic Resonance Imaging/methods , RNA, MessengerABSTRACT
Background: Quality of life (QoL) is an important patient-centric outcome to evaluate in treatment of major depressive disorder (MDD). This work sought to investigate the performance of several machine learning methods to predict a return to normative QoL in patients with MDD after antidepressant treatment.Methods: Several binary classification algorithms were trained on data from the first 2 weeks of the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study (n = 651, conducted from 2001 to 2006) to predict week 9 normative QoL (score ≥ 67, based on a community normative sample, on the Quality of Life Enjoyment and Satisfaction Questionnaire-Short Form [Q-LES-Q-SF]) after treatment with citalopram. Internal validation was performed using a STAR*D holdout dataset, and external validation was performed using the Canadian Biomarker Integration Network in Depression-1 (CAN-BIND-1) dataset (n = 175, study conducted from 2012 to 2017) after treatment with escitalopram. Feature importance was calculated using SHapley Additive exPlanations (SHAP).Results: Random Forest performed most consistently on internal and external validation, with balanced accuracy (area under the receiver operator curve) of 71% (0.81) on the STAR*D dataset and 69% (0.75) on the CAN-BIND-1 dataset. Random Forest Classifiers trained on Q-LES-Q-SF and Quick Inventory of Depressive Symptomatology-Self-Rated variables had similar performance on both internal and external validation. Important predictive variables came from psychological, physical, and socioeconomic domains.Conclusions: Machine learning can predict normative QoL after antidepressant treatment with similar performance to that of prior work predicting depressive symptom response and remission. These results suggest that QoL outcomes in MDD patients can be predicted with simple patient-rated measures and provide a foundation to further improve performance and demonstrate clinical utility.Trial Registration: ClinicalTrials.gov identifiers NCT00021528 and NCT01655706.
Subject(s)
Depressive Disorder, Major , Quality of Life , Humans , Antidepressive Agents/therapeutic use , Biomarkers , Canada , Citalopram/therapeutic use , Depressive Disorder, Major/diagnosis , Depressive Disorder, Major/drug therapy , Depressive Disorder, Major/psychology , Quality of Life/psychology , Treatment Outcome , Clinical Studies as TopicABSTRACT
Identifying clinically relevant predictors of depressive recurrence following treatment for Major Depressive Disorder (MDD) is critical for relapse prevention. Implicit self-depressed associations (SDAs), defined as implicit cognitive associations between elements of depression (e.g., sad, miserable) and oneself, often persist following depressive episodes and may represent a cognitive biomarker for future recurrences. Thus, we examined whether SDAs, and changes in SDAs over time, prospectively predict depressive recurrence among treatment responders in the CAN-BIND Wellness Monitoring for MDD Study, a prospective cohort study conducted across 5 clinical centres. A total of 96 patients with MDD responding to various treatments were followed an average of 1.01 years. Participants completed the Depression Implicit Association Test (DIAT) - a computer-based measure of SDAs - every 8 weeks on a tablet device. Survival analyses indicated that greater SDAs at baseline and increases in SDAs over time predicted shorter time to MDD recurrence, even after accounting for depressive symptom severity. The findings show that SDAs are a robust prognostic indicator of risk for MDD recurrence, and that the DIAT may be a feasible and low-cost clinical screening tool. SDAs also represent a potential mechanism underlying the course of recurrent depression and are a promising target for relapse prevention interventions.
Subject(s)
Depressive Disorder, Major , Humans , Depressive Disorder, Major/psychology , Depression/psychology , Prospective Studies , Canada , Biomarkers , RecurrenceABSTRACT
Childhood maltreatment (CM) is a strong transdiagnostic risk factor for future psychopathology. This risk is theorized to emerge partly because of glucocorticoid-mediated atrophy in the hippocampus, which leaves this area sensitive to further volume loss even through adulthood in the face of future stress and the emergence of psychopathology. This proof-of-principle study examines which specific dimensions of internalizing psychopathology in the context of a CM history are associated with decreases in hippocampal volume over a 6-month period. This study included 80 community-recruited adults (ages 18-66 years, 61.3% women) oversampled for a lifetime history of internalizing psychopathology. At baseline and a naturalistic 6-month follow-up, the symptom dimensions of the tripartite model (anxious arousal, anhedonic depression, and general distress) were assessed by self-report. Hippocampal volume was derived through T1-weighted magnetic resonance imaging scanning segmented via the volBrain HIPS pipeline. CM severity was determined via a semistructured, contextual interview with independent ratings. We found that higher levels of anxious arousal predicted decreases in hippocampal volume over time in those with greater severity of CM but were associated at a trend with increases in hippocampal volume over time in those with lower severity of maltreatment. Findings were specific to anxious arousal and the CA1 subregion of the hippocampus. These novel results suggest that for individuals with a history of CM, transdiagnostic interventions that target and reduce psychological and physiological arousal may result in the preservation of hippocampal structure and, thus, improvements in cognitive and emotional regulation in the face of stress. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Subject(s)
Adult Survivors of Child Abuse , Hippocampus , Humans , Adult , Female , Male , Hippocampus/diagnostic imaging , Hippocampus/pathology , Anxiety , Psychopathology , Adult Survivors of Child Abuse/psychology , ArousalABSTRACT
Importance: Untreated depression is a growing public health concern, with patients often facing a prolonged trial-and-error process in search of effective treatment. Developing a predictive model for treatment response in clinical practice remains challenging. Objective: To establish a model based on electroencephalography (EEG) to predict response to 2 distinct selective serotonin reuptake inhibitor (SSRI) medications. Design, Setting, and Participants: This prognostic study developed a predictive model using EEG data collected between 2011 and 2017 from 2 independent cohorts of participants with depression: 1 from the first Canadian Biomarker Integration Network in Depression (CAN-BIND) group and the other from the Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care (EMBARC) consortium. Eligible participants included those aged 18 to 65 years who had a diagnosis of major depressive disorder. Data were analyzed from January to December 2022. Exposures: In an open-label trial, CAN-BIND participants received an 8-week treatment regimen of escitalopram treatment (10-20 mg), and EMBARC participants were randomized in a double-blind trial to receive an 8-week sertraline (50-200 mg) treatment or placebo treatment. Main Outcomes and Measures: The model's performance was estimated using balanced accuracy, specificity, and sensitivity metrics. The model used data from the CAN-BIND cohort for internal validation, and data from the treatment group of the EMBARC cohort for external validation. At week 8, response to treatment was defined as a 50% or greater reduction in the primary, clinician-rated scale of depression severity. Results: The CAN-BIND cohort included 125 participants (mean [SD] age, 36.4 [13.0] years; 78 [62.4%] women), and the EMBARC sertraline treatment group included 105 participants (mean [SD] age, 38.4 [13.8] years; 72 [68.6%] women). The model achieved a balanced accuracy of 64.2% (95% CI, 55.8%-72.6%), sensitivity of 66.1% (95% CI, 53.7%-78.5%), and specificity of 62.3% (95% CI, 50.1%-73.8%) during internal validation with CAN-BIND. During external validation with EMBARC, the model achieved a balanced accuracy of 63.7% (95% CI, 54.5%-72.8%), sensitivity of 58.8% (95% CI, 45.3%-72.3%), and specificity of 68.5% (95% CI, 56.1%-80.9%). Additionally, the balanced accuracy for the EMBARC placebo group (118 participants) was 48.7% (95% CI, 39.3%-58.0%), the sensitivity was 50.0% (95% CI, 35.2%-64.8%), and the specificity was 47.3% (95% CI, 35.9%-58.7%), suggesting the model's specificity in predicting SSRIs treatment response. Conclusions and Relevance: In this prognostic study, an EEG-based model was developed and validated in 2 independent cohorts. The model showed promising accuracy in predicting treatment response to 2 distinct SSRIs, suggesting potential applications for personalized depression treatment.
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Monitoring sleep and activity through wearable devices such as wrist-worn actigraphs has the potential for long-term measurement in the individual's own environment. Long periods of data collection require a complex approach, including standardized pre-processing and data trimming, and robust algorithms to address non-wear and missing data. In this study, we used a data-driven approach to quality control, pre-processing and analysis of longitudinal actigraphy data collected over the course of 1 year in a sample of 95 participants. We implemented a data processing pipeline using open-source packages for longitudinal data thereby providing a framework for treating missing data patterns, non-wear scoring, sleep/wake scoring, and conducted a sensitivity analysis to demonstrate the impact of non-wear and missing data on the relationship between sleep variables and depressive symptoms. Compliance with actigraph wear decreased over time, with missing data proportion increasing from a mean of 4.8% in the first week to 23.6% at the end of the 12 months of data collection. Sensitivity analyses demonstrated the importance of defining a pre-processing threshold, as it substantially impacts the predictive value of variables on sleep-related outcomes. We developed a novel non-wear algorithm which outperformed several other algorithms and a capacitive wear sensor in quality control. These findings provide essential insight informing study design in digital health research.
Subject(s)
Actigraphy , Algorithms , Humans , Workflow , Polysomnography , Data CollectionABSTRACT
BACKGROUND: Antidepressants are used to treat acute depression in patients with bipolar I disorder, but their effect as maintenance treatment after the remission of depression has not been well studied. METHODS: We conducted a multisite, double-blind, randomized, placebo-controlled trial of maintenance of treatment with adjunctive escitalopram or bupropion XL as compared with discontinuation of antidepressant therapy in patients with bipolar I disorder who had recently had remission of a depressive episode. Patients were randomly assigned in a 1:1 ratio to continue treatment with antidepressants for 52 weeks after remission or to switch to placebo at 8 weeks. The primary outcome, assessed in a time-to-event analysis, was any mood episode, as defined by scores on scales measuring symptoms of hypomania or mania, depression, suicidality, and mood-episode severity; additional treatment or hospitalization for mood symptoms; or attempted or completed suicide. Key secondary outcomes included the time to an episode of mania or hypomania or depression. RESULTS: Of 209 patients with bipolar I disorder who participated in an open-label treatment phase, 150 who had remission of depression were enrolled in the double-blind phase in addition to 27 patients who were enrolled directly. A total of 90 patients were assigned to continue treatment with the prescribed antidepressant for 52 weeks (52-week group) and 87 were assigned to switch to placebo at 8 weeks (8-week group). The trial was stopped before full recruitment was reached owing to slow recruitment and funding limitations. At 52 weeks, 28 of the patients in the 52-week group (31%) and 40 in the 8-week group (46%) had a primary-outcome event. The hazard ratio for time to any mood episode in the 52-week group relative to the 8-week group was 0.68 (95% confidence interval [CI], 0.43 to 1.10; P = 0.12 by log-rank test). A total of 11 patients in the 52-week group (12%) as compared with 5 patients in the 8-week group (6%) had mania or hypomania (hazard ratio, 2.28; 95% CI, 0.86 to 6.08), and 15 patients (17%) as compared with 35 patients (40%) had recurrence of depression (hazard ratio, 0.43; 95% CI, 0.25 to 0.75). The incidence of adverse events was similar in the two groups. CONCLUSIONS: In a trial involving patients with bipolar I disorder and a recently remitted depressive episode, adjunctive treatment with escitalopram or bupropion XL that continued for 52 weeks did not show a significant benefit as compared with treatment for 8 weeks in preventing relapse of any mood episode. The trial was stopped early owing to slow recruitment and funding limitations. (Funded by the Canadian Institutes of Health Research; ClinicalTrials.gov number, NCT00958633.).
Subject(s)
Bipolar Disorder , Humans , Bipolar Disorder/drug therapy , Bipolar Disorder/diagnosis , Mania , Bupropion/adverse effects , Depression , Escitalopram , Canada , Neoplasm Recurrence, Local/drug therapy , Antidepressive Agents/adverse effects , Double-Blind Method , Treatment OutcomeABSTRACT
OBJECTIVE: There is limited data about the impact of mood disorders treatment guidelines on clinical outcomes. The objective of this study was to investigate the impact of prescribers' adherence to the 2018 Canadian Network for Mood and Anxiety Treatments (CANMAT) and International Society for Bipolar Disorders (ISBD) treatment guidelines recommendations on the readmission rates of patients hospitalized for mania. METHOD: A retrospective cohort of all individuals admitted due to acute mania to Kingston General Hospital, Kingston, ON, from January 2018 to July 2021 was included in this study. Patient variables and data regarding index admission and subsequent hospitalizations were extracted from medical records up to December 31, 2021. Treatment regimens were classified as first-line, second-line, noncompliant, or no treatment. We explored the associations between treatment regimens and the risk of readmissions using univariate, multivariate, and survival analysis. RESULTS: We identified 211 hospitalizations related to 165 patients. The mean time-to-readmission was 211.8 days (standard deviation [SD] = 247.1); the 30-day rehospitalization rate was 13.7%, and any rehospitalization rate was 40.3%. Compared to no treatment, only first-line treatments were associated with a statistically significant decreased risk of 30-day readmission (odds ratio [OR] = 0.209; 95% CI, 0.058 to 0.670). The risk of any readmission was reduced by first-line (OR = 0.387; 95% CI, 0.173 to 0.848) and noncompliant regimens (OR = 0.414; 95% CI, 0.174 to 0.982) compared to no treatment. On survival analysis, no treatment group was associated with shorter time-to-readmission (log-rank test, p = 0.014) and increased risk of readmission (hazard ratio = 2.27; 95% CI, 1.30 to 3.96) when compared to first-line medications. CONCLUSIONS: Treatment with first-line medications was associated with lower 30-day rehospitalization rates and longer time-to-readmission. Physicians' adherence to treatments with higher-ranked evidence for efficacy, safety, and tolerability may improve bipolar disorder outcomes.
Subject(s)
Bipolar Disorder , Humans , Bipolar Disorder/therapy , Bipolar Disorder/drug therapy , Patient Readmission , Retrospective Studies , Mania , Canada/epidemiology , AnxietyABSTRACT
BACKGROUND: Lithium remains the gold-standard medication for acute and prophylactic treatment of bipolar disorder. Understanding clinicians' practices and patients' experiences, knowledge and attitudes about lithium may improve its clinical use. METHODS: Online anonymous surveys collected information about clinician's practices and level of confidence in managing lithium and patients' experiences with lithium treatment and information received about benefits and side effects. Knowledge and attitudes regarding lithium were assessed with the Lithium Knowledge Test (LKT) and the Lithium Attitudes Questionnaire (LAQ). RESULTS: Among 201 clinicians, 64.2% endorsed often treating patients with lithium and reported high levels of confidence in assessing and managing lithium. Practices concerning clinical indications, drug titration, and serum levels were guideline-concordant, but compliance with monitoring recommendations was less frequent. Practitioners were interested in receiving more education about lithium. The patients' survey recruited 219 participants with 70.3% being current lithium users. Most patients (68%) found lithium helpful and 71% reported experiencing any kind of side effect. Most responders did not receive information about side effects or other benefits of lithium. Patients with higher scores on the LKT were more likely to have positive attitudes about lithium. LIMITATIONS: Cross-sectional design with predominantly English-speaking participants from Brazil and North America. CONCLUSIONS: There is a discrepancy between guidelines, clinician confidence and knowledge of lithium use and practice. A deeper understanding of how to monitor, prevent and manage long-term side effects and which patients are most likely to benefit from lithium may narrow the gap between knowledge and use.
Subject(s)
Attitude , Lithium , Humans , Lithium/therapeutic use , Cross-Sectional Studies , Surveys and Questionnaires , Lithium Compounds , Health Knowledge, Attitudes, PracticeABSTRACT
Depression is a leading global cause of disability, yet about half of patients do not respond to initial antidepressant treatment. This treatment difficulty may be in part due to the heterogeneity of depression and corresponding response to treatment. Unsupervised machine learning allows underlying patterns to be uncovered, and can be used to understand this heterogeneity by finding groups of patients with similar response trajectories. Prior studies attempting this have clustered patients using a narrow range of data primarily from depression scales. In this work, we used unsupervised machine learning to cluster patients receiving escitalopram therapy using a wide variety of subjective and objective clinical features from the first eight weeks of the Canadian Biomarker Integration Network in Depression-1 trial. We investigated how these clusters responded to treatment by comparing changes in symptoms and symptom categories, and by using Principal Component Analysis (PCA). Our algorithm found three clusters, which broadly represented non-responders, responders, and remitters. Most categories of features followed this response pattern except for objective cognitive features. Using PCA with our clusters, we found that subjective mood state/anhedonia is the core feature of response with escitalopram, but there exists other distinct patterns of response around neurovegetative symptoms, activation, and cognition.
Subject(s)
Depressive Disorder, Major , Humans , Canada , Depressive Disorder, Major/psychology , Escitalopram , Treatment OutcomeABSTRACT
Pharmacotherapies for the treatment of major depressive disorder were serendipitously discovered almost seven decades ago. From this discovery, scientists pinpointed the monoaminergic system as the primary target associated with symptom alleviation. As a result, most antidepressants have been engineered to act on the monoaminergic system more selectively, primarily on serotonin, in an effort to increase treatment response and reduce unfavorable side effects. However, slow and inconsistent clinical responses continue to be observed with these available treatments. Recent findings point to the glutamatergic system as a target for rapid acting antidepressants. Investigating different cohorts of depressed individuals treated with serotonergic and other monoaminergic antidepressants, we found that the expression of a small nucleolar RNA, SNORD90, was elevated following treatment response. When we increased Snord90 levels in the mouse anterior cingulate cortex (ACC), a brain region regulating mood responses, we observed antidepressive-like behaviors. We identified neuregulin 3 (NRG3) as one of the targets of SNORD90, which we show is regulated through the accumulation of N6-methyladenosine modifications leading to YTHDF2-mediated RNA decay. We further demonstrate that a decrease in NRG3 expression resulted in increased glutamatergic release in the mouse ACC. These findings support a molecular link between monoaminergic antidepressant treatment and glutamatergic neurotransmission.
Subject(s)
Depressive Disorder, Major , Animals , Mice , Affect , Antidepressive Agents/pharmacology , Depressive Disorder, Major/drug therapy , Signal Transduction , Synaptic TransmissionABSTRACT
Background: Symptoms of depression are present in neurodegenerative disorders (ND). It is important that depression-related symptoms be adequately screened and monitored in persons living with ND. The Quick Inventory of Depressive Symptomatology Self-Report (QIDS-SR) is a widely-used self-report measure to assess and monitor depressive severity across different patient populations. However, the measurement properties of the QIDS-SR have not been assessed in ND. Aim: To use Rasch Measurement Theory to assess the measurement properties of the Quick Inventory of Depressive Symptomatology Self-Report (QIDS-SR) in ND and in comparison to major depressive disorder (MDD). Methods: De-identified data from the Ontario Neurodegenerative Disease Research Initiative (NCT04104373) and Canadian Biomarker Integration Network in Depression (NCT01655706) were used in the analyses. Five hundred and twenty participants with ND (Alzheimer's disease or mild cognitive impairment, amyotrophic lateral sclerosis, cerebrovascular disease, frontotemporal dementia and Parkinson's disease) and 117 participants with major depressive disorder (MDD) were administered the QIDS-SR. Rasch Measurement Theory was used to assess measurement properties of the QIDS-SR, including unidimensionality and item-level fit, category ordering, item targeting, person separation index and reliability and differential item functioning. Results: The QIDS-SR fit well to the Rasch model in ND and MDD, including unidimensionality, satisfactory category ordering and goodness-of-fit. Item-person measures (Wright maps) showed gaps in item difficulties, suggesting poor precision for persons falling between those severity levels. Differences between mean person and item measures in the ND cohort logits suggest that QIDS-SR items target more severe depression than experienced by the ND cohort. Some items showed differential item functioning between cohorts. Conclusion: The present study supports the use of the QIDS-SR in MDD and suggest that the QIDS-SR can be also used to screen for depressive symptoms in persons with ND. However, gaps in item targeting were noted that suggests that the QIDS-SR cannot differentiate participants falling within certain severity levels. Future studies would benefit from examination in a more severely depressed ND cohort, including those with diagnosed clinical depression.
ABSTRACT
Despite considerable efforts to study the relationship between insomnia and depression, there is minimal research investigating whether insomnia symptoms change over time during a course of antidepressant pharmacotherapy. This study investigated the course of insomnia symptoms during the acute treatment of major depressive disorder (MDD) using a secondary analysis of data from MDD patients (N = 180) who were treated with open-label escitalopram (10-20 mg/day) for 8-weeks. Montgomery-Asberg Depression Rating Scale without sleep item (modified-MADRS) assessed depression and Self-reported Quick Inventory Depressive Scale (QIDS-SR) measured subjective sleep-onset, mid-nocturnal, and early-morning insomnia throughout 8-weeks of treatment. Pittsburgh Sleep Quality Index (PSQI) was used to assess subjective sleep quality, duration, onset latency, and efficiency throughout 8-weeks of treatment. Remission of depression was defined as modified-MADRS ≤10 at week-8. Mixed model repeated measures (MMRMs) were conducted with remission status as an independent variable and each sleep variable as a dependent variable. MMRMs demonstrated that remitters had significantly lower QIDS-SR sleep-onset and mid-nocturnal insomnia scores as well as a significantly lower PSQI sleep quality score than non-remitters throughout 8-weeks of treatment. Monitoring subjective sleep-onset and mid-nocturnal insomnia during the course of treatment with serotonergic antidepressants may be useful for predicting acute remission of depression.
