Treatment Of Depression In Persons Research Articles

Unlocking the Potential of Effective Connectivity as a Neuroimaging Biomarker: A Path Toward Personalized Depression Treatment in Youth

Unlocking the Potential of Effective Connectivity as a Neuroimaging Biomarker: A Path Toward Personalized Depression Treatment in Youth

Melancholic features and treatment outcome to selective serotonin reuptake inhibitors in major depressive disorder: A re-analysis of the STAR*D trial

BackgroundMelancholia has been positioned as a qualitatively different form of Major Depressive Disorder (MDD). Some studies have suggested that melancholic MDD patients may show lower remission when receiving treatment with Selective Serotonin Reuptake Inhibitors, but this has not yet been explored in large, representative samples of MDD. MethodsWe used data from the STAR*D, a multisite randomized controlled trial (n = 4041). We defined melancholia status through the BA Melancholia Empirical Index, constructed using items from the Inventory of Depressive Symptomatology (IDSC). The main outcome of interest was symptomatic remission defined as a Quick Inventory of Depressive Symptoms (Clinician version) (QIDS-C) below or equal to 5. Inverse probability weighting was used to control for confounding. Results3827 patients were eligible for this study. Melancholic patients were more likely to be unemployed, never married, to self-report an African American race, and to have a higher depressive severity. The adjusted 4-month probability of remission was 26.9 % (22.0, 45.5) for melancholic and 53.8 % (53.2, 58.5), for nonmelancholic patients. Compared with nonmelancholic, the difference in 4-month probability of remission was −26.9 % (−37.0, −15.6). Results were consistent across sensitivity analyses. LimitationsItems from IDSC were used as a surrogate measure of the BA Melancholia Index, and extrapolation of the results to agents other than citalopram and to psychotic MDD patients requires caution. ConclusionsMelancholic MDD patients showed lower probabilities of remission at 4-months receiving treatment with citalopram. The results of this study show how validly subtyping episodes could contribute to the personalized treatment of depression.

Developing an Electroencephalography-Based Model for Predicting Response to Antidepressant Medication

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. To establish a model based on electroencephalography (EEG) to predict response to 2 distinct selective serotonin reuptake inhibitor (SSRI) medications. 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. 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. 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. 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. 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.

Toward Personalized Treatment of Depression: An Affordable Citalopram Test based on a Solid-Contact Potentiometric Electrode for at-Home Monitoring of the Antidepressant Dosage.

Citalopram (CTLP) is one of the most common antidepressants prescribed worldwide. It has a narrow therapeutic window and can cause severe toxicity and mortality if the dosage exceeds the safe level. Reports indicated that at-home monitoring of citalopram dosage considerably benefits the patients, yet there are no devices capable of such measurement of citalopram in biofluids. This work presents an affordable citalopram test for at-home and point-of-care monitoring of citalopram levels in urine, ensuring a safe and effective drug compliance. Our platform consists of a citalopram-selective yarn-based electrode (CTLP-SYE) that uses polymeric sensing membranes to provide valuable information about drug concentration in urine. CTLP-SYE is noninvasive and has a response time of fewer than 10 s. The fabricated electrode showed near-Nernstian behavior with a 52.3 mV/decade slope in citalopram hydrobromide solutions ranging from 0.5 μM to 1.0 mM, with a detection limit of 0.2 μM. Results also indicated that neither interfering ions nor pH affects electrode performance. We showed that CTLP-SYE could accurately and reproducibly measure citalopram in human urine (RSD 2.0 to 3.2%, error <12%) at clinically relevant concentrations. This work paves the way for the personalized treatment of depression and accessible companion diagnostics to improve treatment efficacy and safety.

Differential symptom cluster responses to repetitive transcranial magnetic stimulation treatment in depression.

Repetitive transcranial magnetic stimulation (rTMS) can target specific neural circuits, which may allow for personalized treatment of depression. Treatment outcome is typically determined using sum scores from validated measurement scales; however, this may obscure differential improvements within distinct symptom domains. The objectives for this work were to determine: (1) whether a standard depression measure can be represented using a four symptom cluster model and (2) whether these symptom clusters had a differential response to rTMS treatment. Data were obtained from two multi-centre randomized controlled trials of rTMS delivered to the left dorsolateral prefrontal cortex (DLPFC) for participants with treatment-resistant depression (TRD) conducted in Canada (THREE-D [Conducted between Sept 2013, and Oct 2016] and CARTBIND [Conducted between Apr 2016 and Feb 2018]). The first objective used confirmatory factor analytic techniques, and the second objective used a linear mixed effects model. Trial Registration: NCT01887782, NCT02729792. In the total sample of 596 participants with TRD, we found a model consisting of four symptom clusters adequately fit the data. The primary analysis using the THREE-D treatment trial found that symptom clusters demonstrated a differential response to rTMS treatment (F(3,5984)=31.92, p<0.001). The anxiety symptom cluster was significantly less responsive to treatment than other symptom clusters (t(6001)=-8.02, p<0.001). These findings were replicated using data from the CARTBIND trial. There are distinct symptom clusters experienced by individuals with TRD that have a differential response to rTMS. Future work will determine whether differing rTMS treatment targets have distinct patterns of symptom cluster responses with the eventual goal of personalizing rTMS protocols based on an individual's clinical presentation. Canadian Institutes of Health Research, Brain Canada.

Personalized symptom clusters that predict depression treatment outcomes: A replication of machine learning methods

: The purpose of this study is to use independent datasets to externally validate the three symptom clusters of unipolar depression identified by Chekroud, to evaluate personalized treatment trajectories and outcomes based on these symptom clusters, and to verify predictors. : The Quick Inventory of Depressive Symptomatology-Self Report (QIDS-SR16)1 and Hamilton Rating Scale for Depression (Ham-D)2 data from two placebo controlled, double-blind clinical trials (Dual Therapy and Duloxetine) were used for external validation. Machine learning methods were applied to replicate the three symptom clusters and to produce treatment trajectories. Penalized logistic regressions were conducted to identify top baseline variables that best predicted treatment outcomes. : The variables Chekroud identified as comprising sleep, atypical and core emotional clusters are replicated. Treatment trajectories demonstrate that dual treatment (escitalopram and bupropion) performed best across all symptom clusters but did not outperform escitalopram monotherapy over time. For each symptom cluster, there were differences in treatment efficacy among antidepressants. : By using different treatment trajectories based on a patient's symptom cluster profile, clinicians could potentially select best fit antidepressants to achieve the biggest benefit. Our results showed that total baseline QIDS, Ham-D score, anxiety disorder diagnosis and course of depressive illness were the best baseline predictors. Results could enhance personalized depression treatment plans and help to improve outcomes. : NCT00519428, NCT00360724.

Gut microbiota mediates the pharmacokinetics of Zhi-zi-chi decoction for the personalized treatment of depression

Ethnopharmacological relevanceZhi-zi-chi decoction (ZZCD), from “Treatise on Febrile Diseases”, is a typical traditional Chinese medicine herb pair, which consists of Gardeniae Fructus (GF) and Semen Sojae Praeparatu (SSP). In clinical research, ZZCD was widely used to fight depression, remove annoyance. Many studies have reported that gut microbiota is critical target for the influence of depress through gut-brain axis, and our previously studies have found that ZZCD exhibiting antidepressant effect was through the gut-brain axis. However, the specific mechanism by which gut microbiota mediates the pharmacokinetics parameters of active compounds from ZZCD during the process of depression treatment has not yet been studied. Aim of the studyTo explore the differences in pharmacokinetics characters of bioactive iridoids from ZZCD and study the changes of gut microbiota at different stages of depression with the personalized medicine of ZZCD. Materials and methodsA new strategy exploring the relationship among disease phenotypes (D), intestinal microbiota (I), enzymes (E) and traits of metabolism (T) named as “DIET” was established. Firstly, a fast, selective and sensitive ultra-performance liquid chromatography coupled with tandem mass spectrometer (UPLC-MS/MS) was established and validated to quality the main bioactive compounds from ZZCD and compare the pharmacokinetics and bioavailability of different iridoids prototypes and metabolites from ZZCD between normal and chronic unpredictable mild stress rats. Subsequently, the activity of corresponding metabolic enzymes of anti-depressive compounds, β-glucosidases and sulfotransferases, were analyzed by ρ-nitrophenyl-β -D-glucopyranoside and sulfotransferases ELISA kits, respectively. Finally, 16S rRNA gene sequencing was adopt to analyze intestinal bacteria composition for the treatment of depression by ZZCD. ResultsThe antidepressant effect of ZZCD was promoted due to the increased exposures and reduced eliminations of anti-depressive compounds, especially geniposide and genipin 1-gentiobioside, under the depression state. With the ZZCD treatment, the depression was improved, but the exposures of anti-depressive compounds from ZZCD gradually decreased. Meanwhile, there were the corresponding decreased trends on the activity of β-glucosidases and sulfotransferases. With the consumption of ZZDC and the improvement of depression, the exposures of anti-depressive iridoid glycosides decreased and the activity of metabolism enzymes restored. Meanwhile, the dysbiosis of pathogenic bacteria (Bacteroidota) induced by depression was ameliorated and the probiotics (Firmicutes) at the phylum and genus level raised, the two phyla are closely related to the production of β-glucosidase and sulfotransferases. ConclusionsIt is the first proposed that ZZCD could personalized to treat depression at different stages targeting gut microbiota and gut microbiome could emerged as a potential diagnostic and therapeutic biomarker in depression.

Patterns of Pretreatment Reward Task Brain Activation Predict Individual Antidepressant Response: Key Results From the EMBARC Randomized Clinical Trial

BackgroundThe lack of biomarkers to inform antidepressant selection is a key challenge in personalized depression treatment. This work identifies candidate biomarkers by building deep learning predictors of individual treatment outcomes using reward processing measures from functional magnetic resonance imaging, clinical assessments, and demographics. MethodsParticipants in the EMBARC (Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care) study (n = 222) underwent reward processing task-based functional magnetic resonance imaging at baseline and were randomized to 8 weeks of sertraline (n = 106) or placebo (n = 116). Subsequently, sertraline nonresponders (n = 37) switched to 8 weeks of bupropion. The change in Hamilton Depression Rating Scale was measured after treatment. Reward processing, clinical measurements, and demographics were used to train treatment-specific deep learning models. ResultsThe predictive model for sertraline achieved R2 of 48% (95% CI, 33%–61%; p < 10−3) in predicting the change in Hamilton Depression Rating Scale and number-needed-to-treat (NNT) of 4.86 participants in predicting response. The placebo model achieved R2 of 28% (95% CI, 15%–42%; p < 10−3) and NNT of 2.95 in predicting response. The bupropion model achieved R2 of 34% (95% CI, 10%–59%, p < 10−3) and NNT of 1.68 in predicting response. Brain regions where reward processing activity was predictive included the prefrontal cortex and cerebellar crus 1 for sertraline and the cingulate cortex, caudate, orbitofrontal cortex, and crus 1 for bupropion. ConclusionsThese findings demonstrate the utility of reward processing measurements and deep learning to predict antidepressant outcomes and to form multimodal treatment biomarkers.

Acceptance and commitment therapy for the treatment of depression in persons with physical disability: a randomized controlled trial

Objective: To evaluate the effectiveness of acceptance and commitment therapy (ACT) on depressive symptoms in physically disabled persons. Design: Randomized controlled trial. Setting: State welfare organization in Kamyaran, Kurdistan, Iran. Participants: Fifty-two physically disabled participants with a primary diagnosis of depression were randomly assigned to either ACT or control groups. Interventions: Participants in the ACT group (n = 23) received eight weekly 90-minute group sessions based on standard ACT protocol for depression. Participants in the control group (n = 29) received psychoeducation regarding depression. Main measures: Measures were recorded at baseline, eight weeks (end of treatment), and 16 weeks (follow-up). The outcomes were the change in the depressive symptoms, measured by Beck Depression Inventory-II (BDI-II), psychological flexibility, emotion regulation, and psychological well-being measured by Acceptance and Action Questionnaire-II (AAQ-II), Emotion Regulation Questionnaire (ERQ), and Scales of Psychological Well-Being (SPWB), respectively. Results: After eight weeks, significant changes in depressive symptoms was observed in the experimental group (ACT –10.39 ± 0.79 vs control 0.66 ± 0.68, P < 0.001). Compared to the control group, the experimental group also showed significant improvement in psychological flexibility (ACT 8.13 ± 0.52 vs control –0.03 ± 0.51, P < 0.001), adaptive emotion regulation strategies (ACT 10.74 ± 0.62 vs control 0.03 ± 1.03, P < 0.001), and psychological well-being (ACT 66.95 ± 4.01 vs control –1.90 ± 1.04, P < 0.001). Conclusion: Compared with control group, ACT significantly reduced the participants’ depression, and changed psychological flexibility, emotion regulation, and psychological well-being in persons with physical disability.

Differentiating clinically depressed adolescents with and without active suicidality: An examination of neurophysiological and self‐report measures of reward responsiveness

Suicidality in youth is a major public health problem and objective methods for identifying those at greatest risk are critically needed. Suicidality has been associated with alterations in reward-related decision-making, but the extent to which measures of reward responsiveness (RR) can differentiate youth with and without suicidality in clinical samples remains unclear. We examined reliable and accessible neurophysiological (i.e., reward positivity [RewP] event-related potential) and self-report (Behavioral Activation System subscales) measures of RR in relation to active suicidality in 58 clinically depressed adolescents (14- to 18-year-olds). Logistic regression analysis indicated that active suicidality in depressed adolescents was associated with heightened RR at both the self-report and neurophysiological levels. A relatively more positive RewP to win and a more negative RewP to loss uniquely predicted active suicidality beyond demographic, clinical, and self-report measures. Results support the utility of neurophysiological measures in differentiating clinically depressed adolescents with and without suicidality. Although depression is commonly characterized by reduced RR, depressed adolescents with active suicidality exhibited relatively enhanced neural responses to reward and loss feedback. Results highlight the need for consideration of heterogeneity in RR in depression and research on personalized depression treatment.

An electroencephalographic signature predicts antidepressant response in major depression.

Antidepressants are widely prescribed, but their efficacy relative to placebo is modest, in part because the clinical diagnosis of major depression encompasses biologically heterogeneous conditions. Here, we sought to identify a neurobiological signature of response to antidepressant treatment as compared to placebo. We designed a latent-space machine learning algorithm tailored for resting-state electroencephalography (rsEEG) and applied it to data from the largest imaging-coupled, placebo-controlled antidepressant study (n=309). Symptom improvement was robustly predicted in a manner both specific for the antidepressant sertraline (versus placebo) and generalizable across different study sites and EEG equipment. This sertraline-predictive EEG signature generalized to two depression samples, wherein it reflected general antidepressant medication responsivity, and related differentially to repetitive transcranial magnetic stimulation (rTMS) treatment outcome. Furthermore, we found that the sertraline rsEEG signature indexed prefrontal neural responsivity, as measured by concurrent TMS/EEG. Our findings advance the neurobiological understanding of antidepressant treatment through an EEG-tailored computational model and provide a clinical avenue for personalized treatment of depression.

Grant Report on SCH: Personalized Depression Treatment Supported by Mobile Sensor Analytics.

We report on the newly started project “SCH: Personalized Depression Treatment Supported by Mobile Sensor Analytics”. The current best practice guidelines for treating depression call for close monitoring of patients, and periodically adjusting treatment as needed. This project will advance personalized depression treatment by developing a system, DepWatch, that leverages mobile health technologies and machine learning tools. The objective of DepWatch is to assist clinicians with their decision making process in the management of depression. The project comprises two studies. Phase I collects sensory data and other data, e.g., clinical data, ecological momentary assessments (EMA), tolerability and safety data from 250 adult participants with unstable depression symptomatology initiating depression treatment. The data thus collected will be used to develop and validate assessment and prediction models, which will be incorporated into DepWatch system. In Phase II, three clinicians will use DepWatch to support their clinical decision making process. A total of 128 participants under treatment by the three participating clinicians will be recruited for the study. A number of new machine learning techniques will be developed.

Disability worsening among persons with multiple sclerosis and depression: A Swedish cohort study.

ObjectiveDepression is common in multiple sclerosis (MS), but its impact on disability worsening has not yet been determined. We explored the risk of disability worsening associated with depression in a nationwide longitudinal cohort.MethodsThis retrospective cohort study used linked data from 3 Swedish nationwide registries: the MS Register, National Patient Register, and Prescribed Drug Register. Two incident cohorts were developed: cohort 1 included all registered cases of MS in the MS Registry (2001–2014) with depression defined as ≥1 ICD-10 code for depression; and cohort 2 comprised all cases of MS in the MS Registry (2005–2014) with depression defined as ≥1 prescription filled for an antidepressant. Cox regression models were used to compare the risk of reaching sustained disability milestone scores of 3.0, 4.0, and 6.0 on the Expanded Disability Status Scale (EDSS) between persons with MS with and without depression.ResultsCohort 1 included 5,875 cases; 502 (8.5%) had depression. Cohort 2 had 3,817 cases; 1,289 (33.8%) were prescribed an antidepressant. Persons with depression were at a significantly higher risk of reaching sustained EDSS scores of 3.0, 4.0, and 6.0, with hazard ratios of 1.50 (95% confidence interval [CI] 1.20–1.87), 1.79 (95% CI 1.40–2.29), and 1.89 (95% CI 1.38–2.57), respectively. A similar increased risk among persons exposed to antidepressants was observed, with hazard ratios of 1.37 (95% CI 1.18–1.60), 1.93 (95% CI 1.61–2.31), and 1.86 (95% CI 1.45–2.40) for sustained EDSS scores of 3.0, 4.0, and 6.0, respectively.ConclusionPersons with MS and comorbid depression had a significantly increased risk of disability worsening. This finding highlights the need for early recognition and appropriate treatment of depression in persons with MS.

A Feasibility Study of an AI-Powered Clinical Decision Aid for Personalized Depression Treatment Selection

A Feasibility Study of an AI-Powered Clinical Decision Aid for Personalized Depression Treatment Selection

Economic evaluation of an experience sampling method intervention in depression compared with treatment as usual using data from a randomized controlled trial

BackgroundExperience sampling, a method for real-time self-monitoring of affective experiences, holds opportunities for person-tailored treatment. By focussing on dynamic patterns of positive affect, experience sampling method interventions (ESM-I) accommodate strategies to enhance personalized treatment of depression―at potentially low-costs. This study aimed to investigate the cost-effectiveness of an experience sampling method intervention in patients with depression, from a societal perspective.MethodsParticipants were recruited between January 2010 and February 2012 from out-patient mental health care facilities in or near the Dutch cities of Eindhoven and Maastricht, and through local advertisements. Out-patients diagnosed with major depression (n = 101) receiving pharmacotherapy were randomized into: (i) ESM-I consisting of six weeks of ESM combined with weekly feedback regarding the individual’s positive affective experiences, (ii) six weeks of ESM without feedback, or (iii) treatment as usual only. Alongside this randomised controlled trial, an economic evaluation was conducted consisting of a cost-effectiveness and a cost-utility analysis, using Hamilton Depression Rating Scale (HDRS) and quality adjusted life years (QALYs) as outcome, with willingness-to-pay threshold for a QALY set at €50,000 (based on Dutch guidelines for moderate severe to severe illnesses).ResultsThe economic evaluation showed that ESM-I is an optimal strategy only when willingness to pay is around €3000 per unit HDRS and around €40,500 per QALY. ESM-I was the least favourable treatment when willingness to pay was lower than €30,000 per QALY. However, at the €50,000 willingness-to-pay threshold, ESM-I was, with a 46% probability, the most favourable treatment (base-case analysis). Sensitivity analyses confirmed the robustness of these results.ConclusionsWe may tentatively conclude that ESM-I is a cost-effective add-on intervention to pharmacotherapy in outpatients with major depression.Trial registrationNetherlands Trial register, NTR1974.

Association between CYP2D6 Genotypes and the Risk of Antidepressant Discontinuation, Dosage Modification and the Occurrence of Maternal Depression during Pregnancy.

Importance: Polymorphic expression of drug metabolizing enzymes affects the metabolism of antidepressants, and thus can contribute to drug response and/or adverse events. Pregnancy itself can affect CYP2D6 activity with profound variations determined by CYP2D6 genotype.Objective: To investigate the association between CYP2D6 genotype and the risk of antidepressant discontinuation, dosage modification, and the occurrence of maternal CYP2D6, Antidepressants, Depression during pregnancy.Setting: Data from the Organization of Teratology Information Specialists (OTIS) Antidepressants in Pregnancy Cohort, 2006–2010, were used. Women were eligible if they were within 14 completed weeks of pregnancy at recruitment and exposed to an antidepressant or having any exposures considered non-teratogenic.Main Outcomes and Measures: Gestational antidepressant usage was self-reported and defined as continuous/discontinued use, and non-use; dosage modification was further documented. Maternal depression and anxiety were measured every trimester using the telephone interviewer-administered Edinburgh Postnatal Depression Scale and the Beck Anxiety Inventory, respectively. Saliva samples were collected and used for CYP2D6 genotype analyses. Logistic regression models were used to calculate crude and adjusted odds ratios (OR) with 95% confidence intervals.Results: A total of 246 pregnant women were included in the study. The majority were normal metabolizers (NM, n = 204, 83%); 3.3% (n = 8) were ultrarapid metabolizers (UM), 5.7% (n = 14) poor metabolizers (PM), and 8.1% (n = 20) intermediate metabolizers (IM). Among study subjects, 139 women were treated with antidepressants at the beginning of pregnancy, and 21 antidepressant users (15%) discontinued therapy during pregnancy. Adjusting for depressive symptoms, and other potential confounders, the risk of discontinuing antidepressants during pregnancy was nearly four times higher in slow metabolizers (poor or intermediate metabolizers) compared to those with a faster metabolism rate (normal or ultrarapid metabolizers), aOR = 3.57 (95% CI: 1.15-11.11). Predicted CYP2D6 metabolizer status did not impact dosage modifications. Compared with slow metabolizers, significantly higher proportion of women in the fast metabolizer group had depressive symptom in the first trimester (19.81 vs. 5.88%, P = 0.049). Almost 21% of treated women remained depressed during pregnancy (14.4% NM-UM; 6.1% PM-IM).Conclusions and Relevance: Prior knowledge of CYP2D6 genotype may help to identify pregnant women at greater risk of antidepressant discontinuation. Twenty percent of women exposed to antidepressants during pregnancy remained depressed, indicating an urgent need for personalized treatment of depression during pregnancy.

Digital technology and clinical decision making in depression treatment: Current findings and future opportunities.

Clinical decision making encompasses a broad set of processes that contribute to the effectiveness of depression treatments. There is emerging interest in using digital technologies to support effective and efficient clinical decision making. In this paper, we provide "snapshots" of research and current directions on ways that digital technologies can support clinical decision making in depression treatment. Practical facets of clinical decision making are reviewed, then research, design, and implementation opportunities where technology can potentially enhance clinical decision making are outlined. Discussions of these opportunities are organized around three established movements designed to enhance clinical decision making for depression treatment, including measurement-based care, integrated care, and personalized medicine. Research, design, and implementation efforts may support clinical decision making for depression by (1) improving tools to incorporate depression symptom data into existing electronic health record systems, (2) enhancing measurement of treatment fidelity and treatment processes, (3) harnessing smartphone and biosensor data to inform clinical decision making, (4) enhancing tools that support communication and care coordination between patients and providers and within provider teams, and (5) leveraging treatment and outcome data from electronic health record systems to support personalized depression treatment. The current climate of rapid changes in both healthcare and digital technologies facilitates an urgent need for research, design, and implementation of digital technologies that explicitly support clinical decision making. Ensuring that such tools are efficient, effective, and usable in frontline treatment settings will be essential for their success and will require engagement of stakeholders from multiple domains.

Systematic review of interventions for depression and anxiety in persons with inflammatory bowel disease.

BackgroundDepression and anxiety are common in inflammatory bowel disease (IBD) and can affect disease outcomes, including quality of life and success of disease treatment. Successful management of psychiatric comorbidities may improve outcomes, though the effectiveness of existing treatments in IBD is unknown.MethodsWe searched multiple online databases from inception until March 25, 2015, without restrictions on language, date, or location of publication. We included controlled clinical trials conducted in persons with IBD and depression or anxiety. Two independent reviewers reviewed all abstracts and full-text articles and extracted information including trial and participant characteristics. We also assessed the risk of bias.ResultsOf 768 unique abstracts, we included one trial of pharmacological anxiety treatment in IBD (48 participants), which found an improvement in anxiety symptoms (p < 0.001). There was a high risk of bias in this trial. We found no controlled clinical trials on the treatment of depression in persons with IBD and depression and no controlled clinical trials reporting on psychological interventions for anxiety or depression in IBD.ConclusionsOnly one trial examined an intervention for anxiety in adults with IBD and no trials studied depression in adults with IBD. The level of evidence is low because of the risk of bias and limited evidence.Electronic supplementary materialThe online version of this article (doi:10.1186/s13104-016-2204-2) contains supplementary material, which is available to authorized users.

Evidence for two distinct immune phenotypes in subjects with depression and comorbid kidney disease

Evidence suggests that inflammation may characterize a biologically distinct subgroup of patients with Major Depressive Disorder (MDD) and comorbid medical illness. In Chronic Kidney Disease (CKD) depression is common and increases morbidity and mortality. We examined baseline immune function in 27 adults with non-dialysis, stage 3–5 CKD and MDD, enrolled in the CKD Antidepressant Sertraline Trial (CAST), using RNA Sequencing of whole blood. Subjects completed the Quick Inventory of Depression Symptomatology (QIDS) to quantify depression severity. We analyzed co-expression of hyper-variable genes, performing hierarchical cluster analysis according to correlation with QIDS score. Subjects empirically divided into two clusters: (1) 18 subjects in whom depression severity inversely correlated with expression of interferon signaling genes (correlation coefficient 0.95), and (2) 9 subjects without correlation to the interferon cluster but whose depression was significantly correlated with ribosomal gene expression (cc = 0.83) (both p 0.001). Using a significance threshold of p 0.001, we analyzed genes within each group for correlation with clinical parameters. In group 2, STAT3 pathway expression was associated with fatigue and expression of interferon and insulin like growth factor signaling genes were correlated with mental and physical function, respectively. Our results suggest the existence of different depression immune phenotypes; further exploration of these types will provide an opportunity to develop personalized depression treatment in patients with comorbid depression and CKD.

Patterns and frequency of the treatment of depression in persons with epilepsy

ObjectiveThough depression is common in persons with epilepsy, it often remains undiagnosed and/or untreated. The current study aimed to determine the proportion of persons with epilepsy receiving depression-related treatment and to characterize the type of treatment received. MethodsPersons with epilepsy (n=185) from the only epilepsy clinic in a city of 1.2 million people completed questionnaires and the gold-standard Structured Clinical Interview for DSM Disorders (SCID) to assess current and past depression. Treatment for depression (pharmacological and nonpharmacological) was ascertained through patient self-report and chart review. ResultsOf those with current depression (n=27), the majority (70.3%) were not on any depression-related treatment. In persons with current depression, nonpharmacological management was the most common treatment method, followed by treatment with psychotropic medications such as selective serotonin reuptake inhibitors. More individuals with a past history of depression but without a current episode (n=43) were treated (37.2%); it was more common for these individuals to be treated with pharmacological measures. After using an algorithm that adjusts the treated prevalence for those who are successfully treated, the adjusted proportion of depression treatment was 53.1%. ConclusionsThe proportion of people treated for current depression in this cohort was very low. Future studies should investigate barriers to treatment and how depression treatment can be optimized for those with epilepsy.