Antidepressant Medication Treatment Research Articles

Efficacy of non-invasive brain stimulation combined with antidepressant medications for depression: a systematic review and meta-analysis of randomized controlled trials.

Antidepressants, noninvasive brain stimulation (NIBS), and their combination are commonly used in routine clinical practice. Nevertheless, there is a continuous dispute regarding whether the effectiveness of NIBS in combination with antidepressants exceeds that of antidepressants alone. This meta-analysis aimed to evaluate the existing evidence and drawa definitive conclusion on this issue. We conducted a comprehensive search of five databases: Embase, PubMed, Web of Science, SinoMed, and the Cochrane Database of Randomized Controlled Trials. The search was conducted until October 6, 2023. The primary outcomes were the pre- and post-intervention depression and anxiety scores. Secondary outcomes included dropout rates, response rates, and certain levels of neurotransmitters [ 5-hydroxytryptamine (5-HT), dopamine (DA), and gamma-aminobutyric acid (GABA)] at the end of the intervention. Subgroup, meta-regression, and sensitivity analyses were performed to explore the sources of heterogeneity. The data were analysed using R 4.2.2. We included 18 RCTs [1357 participants; 11 studies used repetitive transcranial magnetic stimulation (rTMS) and 7 studies used transcranial direct current stimulation (tDCS)]. The follow-up duration varied from two weeks to three months. Overall, whether in combination with rTMS or tDCS, antidepressants proved more effective in alleviating depressive symptoms compared to when used as monotherapy. However, this advantage was not evident during the follow-up period. (p > 0.05). And the combination's efficacy in improving anxiety was found to be lacking. Post-treatment serum levels of 5-HT, DA, and GABA were higher in the rTMS group were higher than antidepressant medication group (p < 0.05). Furthermore, subgroup analysis results indicated that only the rTMS + antidepressant medication treatment significantly improved remission and remission rates. The meta-regression results showed that the type of antidepressant and the sex of the participants had a significant association with the depression score. Combination treatment with NIBS was significantly more effective in improving depression symptoms than medication alone. rTMS combined with antidepressants appears to be more effective in improving response and remission rates. However, efficacy may be influenced by the type of medicine used in combination, and long-term efficacy data is lacking. PROSPERO CRD42023388259.

Patient Characteristics and Adherence to Antidepressant Medication Treatment at PsychiatricOutpatient Department of Mardan Medical Complex, Pakistan

Background: Adherence to antidepressant treatment is an important factor in the treatment and effective management of depressed patients. Characteristics of patients can help in determining compliance to treatment protocols. In order to find link between compliance and demographic characteristics of patients, insight can be obtained through exploration of these variables.Objectives: To determine the association between patient characteristics and antidepressant medication adherence. To identify characteristics of patients that may improve or worsen compliance to antidepressant medication. Material and Methods: This study was a descriptive, cross-sectional study aiming to find the link between personal characteristics and adherence to anti-depressant medications. The study was conducted at Psychiatry outpatient department of a tertiary care hospital, Mardan Medical Complex, Mardan Pakistan, from 05th September 2018 to 08th February 2021. Data was collected from 660 patients who presented with depressive symptoms to Psychiatry OPD. The main objective was to identify characteristics of patients leading to poor compliance to antidepressant medication. Results: Total study population was 660. Majority of the study population 82.9 % (n-549) were female. Uneducated were, 56.6 % (375), 81.1 % (n-537) were unemployed, and 65.9 % (n-436) were married. Follow up interviews were conducted in 316 (47.7 %) patients at 3rd week, 186 (28.1 %) patients at 6th week, 130 (19.6 %) patients at 9th week, 96 (14.5 %) patients at 12th week, and 31 (4.7 %) patients at 6 months. There was no significant difference found on the basis of patient characteristics and adherence. However, compliance was better among patients who had longer duration of illness. Conclusion: Medication adherence is a common problem among depressed patients and leads to impaired daily life functioning. Our study could not found any significant difference regarding patient characteristics among compliant and non compliant patients. Educating patients regarding the use of antidepressant medication treatment should be added to routine practice. Keywords: Anti depressants, Depression, Non-adherence, Poor compliance.

Predicting Outcomes of Antidepressant Treatment in Community Practice Settings.

The authors examined whether machine-learning models could be used to analyze data from electronic health records (EHRs) to predict patients' responses to antidepressant medications. EHR data from a Washington State health system identified patients ages ≥13 years who started an antidepressant medication in 2016 in a community practice setting and had a baseline Patient Health Questionnaire-9 (PHQ-9) score of ≥10 and at least one PHQ-9 score recorded 14-180 days later. Potential predictors of a response to antidepressants were extracted from the EHR and included demographic characteristics, psychiatric and substance use diagnoses, past psychiatric medication use, mental health service use, and past PHQ-9 scores. Random-forest and penalized regression analyses were used to build models predicting follow-up PHQ-9 score and a favorable treatment response (≥50% improvement in score). Among 2,469 patients starting antidepressant medication treatment, the mean±SD baseline PHQ-9 score was 17.3±4.5, and the mean lowest follow-up score was 9.2±5.9. Outcome data were available for 72% of the patients. About 48% of the patients had a favorable treatment response. The best-fitting random-forest models yielded a correlation between predicted and observed follow-up scores of 0.38 (95% CI=0.32-0.45) and an area under the receiver operating characteristic curve for a favorable response of 0.57 (95% CI=0.52-0.61). Results were similar for penalized regression models and for models predicting last PHQ-9 score during follow-up. Prediction models using EHR data were not accurate enough to inform recommendations for or against starting antidepressant medication. Personalization of depression treatment should instead rely on systematic assessment of early outcomes.

A marginal structural model to estimate the effect of antidepressant medication treatment on major cardiovascular events among people with post-traumatic stress disorder.

Previous evidence on antidepressant medication and cardiovascular disease (CVD) among patients with posttraumatic stress disorder (PTSD) has been inconclusive. We estimated the association between antidepressant medication and CVD by applying a marginal structural model. We analyzed medical utilization records of 27 170 people with PTSD without prior major cardiovascular events in the Korean National Health Insurance Database (NHID). PTSD and CVD were defined in accordance with the recorded ICD-10 diagnostic codes. We acquired information on antidepressant use from the NHID and categorized them by medication type. A composite major adverse cardiovascular events (MACE) outcome was defined as coronary artery disease with revascularization, ischaemic stroke, and/or haemorrhagic stroke. We used inverse probability of treatment weighting to estimate the parameters of a marginal structural discrete-time survival analysis regression model, comparing the resulting estimates to those derived from traditional time-fixed and time-varying Cox proportional hazards regression. We calculated cumulative daily defined doses to test for a dose-response relationship. People exposed to antidepressants showed a higher hazard of MACE [hazard ratio (HR) 1.34, 95% confidence interval (CI) 1.18-1.53]. The estimated effects were strongest for selective serotonin reuptake inhibitors (HR 1.24, 95% CI 1.08-1.44) and TCAs (HR 1.33, 95% CI 1.13-1.56). Exposure to serotonin-norepinephrine reuptake inhibitors did not appear to increase the risk of MACE. People exposed to higher doses of antidepressants showed higher risk of MACE. In a national cohort of people with PTSD, exposure to antidepressant medications increased the risk of MACE in a dose-response fashion.

Use of Non-Psychiatric Medications With Potential Depressive Symptom Side Effects and Level of Depressive Symptoms in Major Depressive Disorder

Objective: To determine whether use of medications with potential depressive symptom side effects is associated with a higher level of depressive symptoms in adults with antidepressant-treated major depressive disorder (MDD).Methods: The study was based on the 2013-2014, 2015-2016, and 2017-2018 National Health and Nutrition Examination Survey (NHANES)-a nationally representative cross-sectional survey of the US general population. In 885 adult participants from these NHANES cycles who reported receiving antidepressants for treatment of International Classification of Diseases, Tenth Revision, Clinical Modification MDD, the association between the number of medications with potential depressive symptom side effects and the level of depressive symptoms was assessed.Results: A majority (66.7%, n = 618) of the participants with antidepressant-treated MDD used at least 1 non-psychiatric medication with potential depressive symptom side effects, and 37.3% (n = 370) used more than 1 such medication. The number of medications with depressive symptom side effects was significantly associated with lower odds of no to minimal depressive symptoms, defined as a Patient Health Questionnaire-9 (PHQ-9) score < 5 (adjusted odds ratio [AOR] = 0.75, 95% confidence interval [CI] = 0.64-0.87, P < .001), and higher odds of moderate to severe symptoms, defined as a PHQ-9 score ≥ 10 (AOR = 1.14, 95% CI = 1.004-1.29, P = .044). No such associations were found for medications without potential depressive symptom side effects.Conclusions: Individuals treated for MDD frequently use non-psychiatric medications for comorbid medical conditions that are associated with an increased risk of depressive symptoms. In evaluating the response to antidepressant medication treatment, side effects of concomitantly used medications should be considered.

Heartbeat-Evoked Potential in Major Depressive Disorder: A Biomarker for Differential Treatment Prediction between Venlafaxine and rTMS?

Introduction: Currently, major depressive disorder (MDD) treatment plans are based on trial-and-error, and remission rates remain low. A strategy to replace trial-and-error and increase remission rates could be treatment stratification. We explored the heartbeat-evoked potential (HEP) as a biomarker for treatment stratification to either antidepressant medication or rTMS treatment. Methods: Two datasets were analyzed: (1) the International Study to Predict Optimized Treatment in Depression (iSPOT-D; n = 1,008 MDD patients, randomized to escitalopram, sertraline, or venlafaxine, and n = 336 healthy controls) and (2) a multi-site, open-label rTMS study (n = 196). The primary outcome measure was remission. Cardiac field artifacts were removed from the baseline EEG using independent component analysis (ICA). The HEP-peak was detected in a bandwidth of 20 ms around 8 ms and 270 ms (N8, N270) after the R-peak of the electrocardiogram signal. Differences between remitters and non-remitters were statistically assessed by repeated-measures ANOVAs for electrodes Fp1, Cz, and Oz. Results: In the venlafaxine subgroup, remitters showed a lower HEP around the N8 peak than non-remitters on electrode site Cz (p = 0.004; d = 0.497). The rTMS group showed a non-significant difference in the opposite direction (d = −0.051). Retrospective stratification to one of the treatments based on the HEP resulted in enhanced treatment outcome prediction for venlafaxine (+22.98%) and rTMS (+10.66%). Conclusion: These data suggest that the HEP could be used as a stratification biomarker between venlafaxine and rTMS; however, future out-of-sample replication is warranted.

Development of a model to predict combined antidepressant medication and psychotherapy treatment response for depression among veterans

BackgroundAlthough research shows that more depressed patients respond to combined antidepressants (ADM) and psychotherapy than either alone, many patients do not respond even to combined treatment. A reliable prediction model for this could help treatment decision-making. We attempted to create such a model using machine learning methods among patients in the US Veterans Health Administration (VHA). MethodsA 2018–2020 national sample of VHA patients beginning combined depression treatment completed self-report assessments at baseline and 3 months (n = 658). A learning model was developed using baseline self-report, administrative, and geospatial data to predict 3-month treatment response defined by reductions in the Quick Inventory of Depression Symptomatology Self-Report and/or in the Sheehan Disability Scale. The model was developed in a 70 % training sample and tested in the remaining 30 % test sample. Results30.0 % of patients responded to treatment. The prediction model had a test sample AUC-ROC of 0.657. A strong gradient was found in probability of treatment response from 52.7 % in the highest predicted quintile to 14.4 % in the lowest predicted quintile. The most important predictors were episode characteristics (symptoms, comorbidities, history), personality/psychological resilience, recent stressors, and treatment characteristics. LimitationsRestrictions in sample definition, a low recruitment rate, and reliance on patient self-report rather than clinician assessments to determine treatment response limited the generalizability of results. ConclusionsA machine learning model could help depressed patients and providers predict likely response to combined ADM-psychotherapy. Parallel information about potential harms and costs of alternative treatments would be needed, though, to inform optimal treatment selection.

The Precision in Psychiatry (PIP) study: Testing an internet-based methodology for accelerating research in treatment prediction and personalisation

BackgroundEvidence-based treatments for depression exist but not all patients benefit from them. Efforts to develop predictive models that can assist clinicians in allocating treatments are ongoing, but there are major issues with acquiring the volume and breadth of data needed to train these models. We examined the feasibility, tolerability, patient characteristics, and data quality of a novel protocol for internet-based treatment research in psychiatry that may help advance this field.MethodsA fully internet-based protocol was used to gather repeated observational data from patient cohorts receiving internet-based cognitive behavioural therapy (iCBT) (N = 600) or antidepressant medication treatment (N = 110). At baseline, participants provided > 600 data points of self-report data, spanning socio-demographics, lifestyle, physical health, clinical and other psychological variables and completed 4 cognitive tests. They were followed weekly and completed another detailed clinical and cognitive assessment at week 4. In this paper, we describe our study design, the demographic and clinical characteristics of participants, their treatment adherence, study retention and compliance, the quality of the data gathered, and qualitative feedback from patients on study design and implementation.ResultsParticipant retention was 92% at week 3 and 84% for the final assessment. The relatively short study duration of 4 weeks was sufficient to reveal early treatment effects; there were significant reductions in 11 transdiagnostic psychiatric symptoms assessed, with the largest improvement seen for depression. Most participants (66%) reported being distracted at some point during the study, 11% failed 1 or more attention checks and 3% consumed an intoxicating substance. Data quality was nonetheless high, with near perfect 4-week test retest reliability for self-reported height (ICC = 0.97).ConclusionsAn internet-based methodology can be used efficiently to gather large amounts of detailed patient data during iCBT and antidepressant treatment. Recruitment was rapid, retention was relatively high and data quality was good. This paper provides a template methodology for future internet-based treatment studies, showing that such an approach facilitates data collection at a scale required for machine learning and other data-intensive methods that hope to deliver algorithmic tools that can aid clinical decision-making in psychiatry.

Achievement of glycemic control and antidepressant medication use in comorbid depression and type 2 diabetes

BackgroundExisting studies designed to determine if depression treatment in patients with type 2 diabetes (T2D) is associated with improved glycemic control have produced inconsistent results. The present study investigated the link between acute phase antidepressant medication treatment and achievement of glycemic control in patients with T2D using nationally distributed electronic health record data. MethodsA retrospective cohort study (n = 7332) was conducted using nationally distributed Optum® de-identified electronic health record data from 2010 to 2018. Eligible patients were 18–64 years old and had T2D, depression, and poor glycemic control. Antidepressant medication treatment was categorized into acute phase treatment (≥12 weeks), less than acute phase (<12 weeks) or no treatment. Glycemic control was defined as HbA1c < 7.0 % (53 mmol/mol). Propensity scores (PS) and inverse probability of treatment weighting (IPTW) controlled for confounding. Extended Cox models measured the association between duration of antidepressant medication treatment and glycemic control at 0 to 36 months, 36 to 72 months and ≥72 months. ResultsAfter controlling for confounding, compared to no treatment, acute phase treatment was significantly associated with achieving glycemic control within 36 months (HR 1.17, 95 % CI 1.02–1.34). No association was observed beyond 36 months. There was no association between acute vs. less than acute phase treatment and glycemic control. LimitationsWe were unable to measure decreased depression severity which could contribute to glycemic control. ConclusionsFor patients with T2D and hyperglycemia, acute phase antidepressant medication may enable glycemic control. Further research is needed to establish mechanisms for this association.

Effect of early-life stress or fluoxetine exposure on later-life conditioned taste aversion learning in Sprague-Dawley rats

In rodents, early-life exposure to environmental stress or antidepressant medication treatment has been shown to induce similar long-term consequences on memory- and depression-related behavior in adulthood. To expand on this line of work, we evaluated how juvenile exposure to chronic variable stress (CVS) or the selective serotonin reuptake inhibitor fluoxetine (FLX) influences conditioned taste aversion (CTA) learning in adulthood. To do this, in Experiment 1, we examined how adolescent CVS alone (postnatal day [PND] 35–48), or with prenatal stress (PNS) history (PNS + CVS), influenced the acquisition and extinction of CTA in adult male Sprague Dawley rats. Specifically, at PND70+ (adulthood), rats were presented with 0.15 % saccharin followed by an intraperitoneal (i.p.) injection of lithium chloride (LiCl) to induce visceral malaise. A total of four saccharin (conditioned stimulus) and LiCl (unconditioned stimulus) pairings occurred across the CTA acquisition phase. Next, saccharin was presented without aversive consequences, and intake was measured across consecutive days of the extinction phase. No differences in body weight gain across the experimental days, rate of CTA acquisition, or extinction of CTA, were observed among the experimental groups (control, n = 7; CVS, n = 12; PNS + CVS, n = 9). In Experiment 2, we evaluated if early-life FLX exposure alters CTA learning in adulthood. Specifically, adolescent stress naïve male and female rats received FLX (0 or 20 mg/kg/i.p) once daily for 15 consecutive days (PND35-49). During antidepressant exposure, FLX decreased body weight gain in both male (n = 7) and female rats (n = 7), when compared to respective controls (male control, n = 8; female control, n = 8). However, juvenile FLX exposure decreased body weight-gain in adult male, but not female, rats. Lastly, adolescent FLX history had no effect on CTA acquisition or extinction in adulthood (PND70), in neither male nor female rats. Together, the data indicate that juvenile FLX exposure results in a long-term decrease of body weight-gain in a male-specific manner. Yet, independent of sex, neither early-life stress nor FLX exposure alters CTA learning in adulthood.

Development of a model to predict antidepressant treatment response for depression among Veterans.

Only a limited number of patients with major depressive disorder (MDD) respond to a first course of antidepressant medication (ADM). We investigated the feasibility of creating a baseline model to determine which of these would be among patients beginning ADM treatment in the US Veterans Health Administration (VHA). A 2018-2020 national sample of n = 660 VHA patients receiving ADM treatment for MDD completed an extensive baseline self-report assessment near the beginning of treatment and a 3-month self-report follow-up assessment. Using baseline self-report data along with administrative and geospatial data, an ensemble machine learning method was used to develop a model for 3-month treatment response defined by the Quick Inventory of Depression Symptomatology Self-Report and a modified Sheehan Disability Scale. The model was developed in a 70% training sample and tested in the remaining 30% test sample. In total, 35.7% of patients responded to treatment. The prediction model had an area under the ROC curve (s.e.) of 0.66 (0.04) in the test sample. A strong gradient in probability (s.e.) of treatment response was found across three subsamples of the test sample using training sample thresholds for high [45.6% (5.5)], intermediate [34.5% (7.6)], and low [11.1% (4.9)] probabilities of response. Baseline symptom severity, comorbidity, treatment characteristics (expectations, history, and aspects of current treatment), and protective/resilience factors were the most important predictors. Although these results are promising, parallel models to predict response to alternative treatments based on data collected before initiating treatment would be needed for such models to help guide treatment selection.

The Appalachia Mind Health Initiative (AMHI): a pragmatic randomized clinical trial of adjunctive internet-based cognitive behavior therapy for treating major depressive disorder among primary care patients

BackgroundMajor depressive disorder (MDD) is a leading cause of disease morbidity. Combined treatment with antidepressant medication (ADM) plus psychotherapy yields a much higher MDD remission rate than ADM only. But 77% of US MDD patients are nonetheless treated with ADM only despite strong patient preferences for psychotherapy. This mismatch is due at least in part to a combination of cost considerations and limited availability of psychotherapists, although stigma and reluctance of PCPs to refer patients for psychotherapy are also involved. Internet-based cognitive behaviorial therapy (i-CBT) addresses all of these problems.MethodsEnrolled patients (n = 3360) will be those who are beginning ADM-only treatment of MDD in primary care facilities throughout West Virginia, one of the poorest and most rural states in the country. Participating treatment providers and study staff at West Virginia University School of Medicine (WVU) will recruit patients and, after obtaining informed consent, administer a baseline self-report questionnaire (SRQ) and then randomize patients to 1 of 3 treatment arms with equal allocation: ADM only, ADM + self-guided i-CBT, and ADM + guided i-CBT. Follow-up SRQs will be administered 2, 4, 8, 13, 16, 26, 39, and 52 weeks after randomization. The trial has two primary objectives: to evaluate aggregate comparative treatment effects across the 3 arms and to estimate heterogeneity of treatment effects (HTE). The primary outcome will be episode remission based on a modified version of the patient-centered Remission from Depression Questionnaire (RDQ). The sample was powered to detect predictors of HTE that would increase the proportional remission rate by 20% by optimally assigning individuals as opposed to randomly assigning them into three treatment groups of equal size.Aggregate comparative treatment effects will be estimated using intent-to-treat analysis methods. Cumulative inverse probability weights will be used to deal with loss to follow-up. A wide range of self-report predictors of MDD heterogeneity of treatment effects based on previous studies will be included in the baseline SRQ. A state-of-the-art ensemble machine learning method will be used to estimate HTE.DiscussionThe study is innovative in using a rich baseline assessment and in having a sample large enough to carry out a well-powered analysis of heterogeneity of treatment effects. We anticipate finding that self-guided and guided i-CBT will both improve outcomes compared to ADM only. We also anticipate finding that the comparative advantages of adding i-CBT to ADM will vary significantly across patients. We hope to develop a stable individualized treatment rule that will allow patients and treatment providers to improve aggregate treatment outcomes by deciding collaboratively when ADM treatment should be augmented with i-CBT.Trial registrationClinicalTrials.gov NCT04120285. Registered on October 19, 2019.

0470 Pre-treatment Objective Short Sleep is Associated with Poor Treatment Response in Patients with Insomnia and Major Depression: A Report from the TRIAD Study

Abstract Introduction Several studies have shown that patients with short sleep duration show a poor response to cognitive behavioral insomnia therapy insomnia (CBT-I) but such studies have not included patients with comorbid conditions. This study was conducted to determine whether pre-treatment sleep duration moderates the response of patients with major depression (MDD) and insomnia (ID) disorders to a combined CBT-I and antidepressant medication treatment. Methods This study involved a secondary analysis of data from the TRIAD trial that tested combined CBT-I/antidepressant medication treatment of patients with MDD and ID. Participants (N=99; 70 women; Mage = 47.7 +/-12.4 yrs.) completed pre-treatment polysomnography (PSG) and were randomly assigned to a 12-week treatment comprised of antidepressant medication combined with CBT-I or a sham quasi-desensitization therapy for insomnia (DTI). Short and longer sleepers were defined using PSG total sleep time cutoffs of &amp;lt;5, &amp;lt;6 and &amp;lt;7 hours for short sleep. Insomnia and depression remission determined respectively from the Insomnia Severity Index and Hamilton Rating Scale for Depression were used to compare treatment responses of short and longer sleepers defined by the cutoffs mentioned. Results Logistic regression analyses showed that statistically significant results were obtained only when the cutoff of &amp;lt;5 hours of sleep was used to define “short sleep.” CBT-I recipients with &amp;gt; 5 hours of sleep were significantly more likely to achieve insomnia remission than were either the DTI recipients with &amp;gt; 5 hours of sleep (OR = 6.72; 95% CI = 2.03 – 22.26) or the CBT-I recipients with short sleep (OR = 18.92; 95% CI = 2.03 – 178.69). The longer sleeping CBT-I group was also more likely to achieve insomnia and/or depression remission than was either the longer sleeping DTI group (OR = 3.12 95% CI = 1.11 – 9.53) or the shorter sleeping CBT-I group (OR = 8.464; 95% CI = 1.40 – 51.12). Conclusion Sleeping &amp;lt;5 hours may dispose patients with comorbid MDD/ID to a poor response to combined CBT-I/antidepressant medication treatments for their insomnia and depression. Future studies to replicate these findings and explore mechanisms of treatment response seem warranted. Support (If Any) National Institutes of Health, Grant Numbers MH078924, MH078961, MH079256 and HL096492.

Antidepressant medication use by patients accessing a national digital mental health service

BackgroundDigital mental health services (DMHSs) provide psychological treatments via the internet or phone and are increasingly being offered as part of routine care. This study describes antidepressant (AD) medication use and treatment outcomes in a large sample of routine care patients accessing a DMHS. MethodsPatients completing an assessment with an Australia-wide DMHS (MindSpot Clinic) from 1st January to 31st December 2020 (n = 17,409) were asked about psychotropic medication use. Demographic characteristics and treatment outcomes on the PHQ-9 (depression), GAD-7 (anxiety), and K-10+ (general distress) were compared for patients taking an AD versus no AD. Treatment outcomes were also analyzed for a subgroup of patients reporting recent commencement of AD medication. ResultsAlmost one quarter of patients (4141/17409; 23.8%) reported taking an AD, mainly selective serotonin reuptake inhibitors (SSRIs). Patients taking ADs had more severe symptoms however effect sizes were large (Cohen's d's > 1.0). Patients recently commencing ADs had the highest baseline symptoms but showed greater symptom improvement at post-treatment and 3-month follow-up. LimitationsTreatment trajectory was measured weekly using standardized scales that are sensitive to change, however they did not allow formal clinical diagnoses of depression and were subject to the effects of missing data. The observational design did not control for spontaneous recovery or for comorbid conditions that might influence recovery. ConclusionsDespite these limitations, online treatment provided by a DMHS as part of routine care is acceptable and effective for patients reporting concurrent AD medication use.

Achievement of glycemic control and antidepressant medication use in comorbid depression and type II diabetes.

Limited previous work has suggested that treatment of co-morbid patients with anti-depressant medication (ADM) is associated with improved glycemic control. To determine the association of ADM treatment on glycemic control at 3 time periods after diagnosis of diabetes. Retrospective cohort study. Propensity scores (PS) and inverse probability of treatment weighting (IPTW) controlled for confounding. Extended Cox models measured the association between adequate, inadequate vs. no treatment and glycemic control at 0 to 36 months, 36 to 72 months and ≥72 months. Optum®, a nationally distributed source dataset with a random sample of 5 million patients ≥18 years of age, from which we used de-identified data for 2011-2017. Eligible patients aged 18-64 had type 2 diabetes (T2DM) with poor glycemic control, and diagnosis of depression at least one year prior to diagnosis of T2DM. Exclusion criteria included steroid use, HIV, cancer, and inadequate data. 7,332 patients meeting inclusion criteria were identified. ADM treatment (identified by patient prescriptions within the EHR) was defined as adequately treated (≥12 weeks of antidepressants), inadequately treated (<12 weeks) or untreated. Glycemic control was defined as A1c<7.0%. Outcome was achievement of glycemic control at 0 to 36 months, 36 to 72 months and ≥72 months. After controlling for confounding, compared to no ADM treatment, adequate ADM treatment was significantly associated with achieving glycemic control within 36 months (HR 1.17, 95% CI 1.02-1.34). No association was observed beyond 36 months. There was no association between inadequate vs. no treatment and glycemic control. Receipt of adequate ADM therapy is associated with achieving glycemic control in the first 3 years after a T2DM diagnoses with uncontrolled A1c.

Examining the Use of Antidepressants for Adolescents with Depression/Anxiety Who Regularly Use Cannabis: A Narrative Review.

Depression and anxiety disorders are two of the most common and growing mental health concerns in adolescents. Consequently, antidepressant medication (AD) use has increased widely during the last decades. Several classes of antidepressants are used mainly to treat depression, anxiety, and obsessive-compulsive disorders by targeting relevant brain neurochemical pathways. Almost all randomized clinical trials of antidepressants examined patients with no concomitant medications or drugs. This does not address the expected course of therapy and outcome in cannabis users. Cannabis is the most commonly used illicit substance globally. Substantial changes in its regulation are recently taking place. Many countries and US states are becoming more permissive towards its medical and recreational use. The psychological and physiological effects of cannabis (mainly of its major components, tetrahydrocannabinol (THC) and cannabidiol (CBD)) have been extensively characterized. Cannabis use can be a risk factor for depressive and anxiety symptoms, but some constituents or mixtures may have antidepressant and/or anxiolytic potential. The aim of this literature review is to explore whether simultaneous use of AD and cannabis in adolescence can affect AD treatment outcomes. Based on the current literature, it is reasonable to assume that antidepressants are less effective for adolescents with depression/anxiety who frequently use cannabis. The mechanisms of action of antidepressants and cannabis point to several similarities and conjunctions that merit future investigation regarding the potential effectiveness of antidepressants among adolescents who consume cannabis regularly.

Antidepressant treatment and blood pressure control in patients with comorbid depression and treatment resistant hypertension

BackgroundThough inconsistent, the majority of studies support an association between depression and incident hypertension and poor blood pressure control. However, none have investigated whether antidepressant medication (ADM) therapy is associated with blood pressure control in patients with comorbid depression and treatment resistant hypertension. MethodsOptum® de-identified Electronic Health Record data (2010–2018) were used to create a retrospective cohort of patients (≥18 years of age) with comorbid depression and treatment resistant hypertension. Patients were categorized into adequate ADM, inadequate ADM and no ADM treatment. A modified Poisson regression approach with robust error variance was used to estimate the association between ADM status and blood pressure control before and after adjusting for covariates. ResultsPatients were, on average, 55.7 (SD ± 9.9) years of age, 63.9% were female, 76.2% were white and 19.2% Black race. In crude models, inadequate ADM (RR = 1.06; 95%CI:1.01–1.11) and adequate ADM (RR = 1.08; 95%CI:1.03–1.14), compared to no ADM treatment, were associated with blood pressure control. After adjusting for covariates this relationship was attenuated and no longer significant. ConclusionsThe modest association between ADM therapy and blood pressure control in patients with treatment resistant hypertension is largely explained by traditional risk factors for hypertension such as obesity and older age. Treating depression is not a robust factor in blood pressure control among those with treatment resistant hypertension.

Frailty and Depression in Late Life: A High-Risk Comorbidity With Distinctive Clinical Presentation and Poor Antidepressant Response.

To investigate the longitudinal relationship between physical frailty, the clinical representation of accelerated biological aging, and antidepressant medication response in older adults with depressive illness. An 8-week randomized placebo-controlled trial (escitalopram or duloxetine) followed by 10 months of open antidepressant medication treatment (augmentation, switch strategies) was conducted in an outpatient research clinic. 121 adults aged 60 years or older with major depressive disorder (MDD) or persistent depressive disorder and a 24-item Hamilton Rating Scale for Depression (HRSD) ≥16 were enrolled. Primary measures assessed serially over 12 months include response (50% reduction from baseline HRSD score), remission (HRSD score <10), and frailty (non/intermediate frail [0-2 deficits] vs frail [≥3 deficits]); latent class analysis was used to classify longitudinal frailty trajectories. A 2-class model best fit the data, identifying a consistently low frailty risk (63% of the sample) and consistently high frailty risk (37% of the sample) trajectory. Response and remission rates (ps ≤ .002) for adults in the high-risk frailty class were at least 21 percentage points worse than those in the low-risk class over 12 months. Furthermore, subsequent frailty was associated with previous frailty (ps ≤ .01) but not previous response or remission (ps ≥ .10). Antidepressant medication is poorly effective for MDD occurring in the context of frailty in older adults. Furthermore, even when an antidepressant response is achieved, this response does little to improve their frailty. These data suggest that standard psychiatric assessment of depressed older adults should include frailty measures and that novel therapeutic strategies to address comorbid frailty and depression are needed.

Repetitive transcranial magnetic stimulation treatment of major depressive disorder and comorbid chronic pain: response rates and neurophysiologic biomarkers.

Major depressive disorder (MDD) and chronic pain are highly comorbid, and pain symptoms are associated with a poorer response to antidepressant medication treatment. It is unclear whether comorbid pain also is associated with a poorer response to treatment with repetitive transcranial magnetic stimulation (rTMS). 162 MDD subjects received 30 sessions of 10 Hz rTMS treatment administered to the left dorsolateral prefrontal cortex (DLPFC) with depression and pain symptoms measured before and after treatment. For a subset of 96 patients, a resting-state electroencephalogram (EEG) was recorded at baseline. Clinical outcome was compared between subjects with and without comorbid pain, and the relationships among outcome, pain severity, individual peak alpha frequency (PAF), and PAF phase-coherence in the EEG were examined. 64.8% of all subjects reported pain, and both depressive and pain symptoms were significantly reduced after rTMS treatment, irrespective of age or gender. Patients with severe pain were 27% less likely to respond to MDD treatment than pain-free individuals. PAF was positively associated with pain severity. PAF phase-coherence in the somatosensory and default mode networks was significantly lower for MDD subjects with pain who failed to respond to MDD treatment. Pain symptoms improved after rTMS to left DLPFC in MDD irrespective of age or gender, although the presence of chronic pain symptoms reduced the likelihood of treatment response. Individual PAF and baseline phase-coherence in the sensorimotor and midline regions may represent predictors of rTMS treatment outcome in comorbid pain and MDD.

Treatment Differences in Primary and Specialty Settings in Veterans with Major Depression.

The Veterans Health Administration (VHA) supports the nation's largest primary care-mental health integration (PC-MHI) collaborative care model to increase treatment of mild to moderate common mental disorders in primary care (PC) and refer more severe-complex cases to specialty mental health (SMH) settings. It is unclear how this treatment assignment works in practice. Patients (n = 2610) who sought incident episode VHA treatment for depression completed a baseline self-report questionnaire about depression severity-complexity. Administrative data were used to determine settings and types of treatment during the next 30 days. Thirty-four percent (34.2%) of depressed patients received treatment in PC settings, 65.8% in SMH settings. PC patients had less severe and fewer comorbid depressive episodes. Patients with lowest severity and/or complexity were most likely to receive PC antidepressant medication treatment; those with highest severity and/or complexity were most likely to receive combined treatment in SMH settings. Assignment of patients across settings and types of treatment was stronger than found in previous civilian studies but less pronounced than expected (cross-validated AUC = 0.50-0.68). By expanding access to evidence-based treatments, VHA's PC-MHI increases consistency of treatment assignment. Reasons for assignment being less pronounced than expected and implications for treatment response will require continued study.