Diagnosis Of Major Depressive Disorder Research Articles

Assessment of biomarker stability and assay performance parameters for medical diagnosis: a case study of diagnosis of major depressive disorder.

Aim: Assessing the stability profiles and assay performance of 24 biomarker assays in 32 biomarker/body fluid combinations identified as relevant for prediction of major depressive disorder. Materials & methods: Combinations were tested for stability and assay performance with ELISA at different storage and freeze-thaw conditions in pooled samples of 40 patients. Results: Stability and assay performance issues were found in almost all cases except three biomarkers in urine and three in serum. Conclusion: This study shows that, to produce reliable measurement data, assessments of stability and assay performance are essential. In development, other quality assurance parameters might be implemented to increase the level of measurement reliability by increasing assay performance control.

Gender Differences of Antioxidant System and Thyroid Function in Depressed Adolescents with Non-Suicidal Self-Injury.

The aim of our study was to explore the relation between serum levels of non-enzymatic antioxidants, thyroid function with the risk of non-suicidal self-injury (NSSI) in depressed adolescents. We retrospected the electronic records of 454 hospitalized patients aged 13-17 years old with a diagnosis of major depressive disorder (239 patients with NSSI and 215 subjects without NSSI), and collected their demographic and clinical information, including serum levels of total bilirubin (Tbil), uric acid (UA), free triiodothyronine (FT3), free thyroxine (FT4) and thyroid stimulating hormone (TSH). The incidence of NSSI was 52.6% among depressed adolescents aged 13-17, 57.1% in female and 38.5% in male. After using the propensity scoring method to exclude the influence of age between the two groups, it was found that patients with NSSI showed lower levels of Tbil (P=0.046) and UA (P=0.015) compared with those without NSSI. Logistic regression results showed that serum UA was associated with NSSI behavior in female patients (OR=0.995, 95% CI: 0.991-0.999, P=0.014), and TSH was associated with NSSI in male participants (OR=0.499, 95% CI: 0.267-0.932, P=0.029). Female and male may have different pathological mechanisms of NSSI. NSSI is more likely to be related to antioxidant reaction in female adolescent patients, while more likely to be related to thyroid function in male depressed adolescent patients.

Automated Diagnosis of Major Depressive Disorder With Multi-Modal MRIs Based on Contrastive Learning: A Few-Shot Study.

Depression ranks among the most prevalent mood-related psychiatric disorders. Existing clinical diagnostic approaches relying on scale interviews are susceptible to individual and environmental variations. In contrast, the integration of neuroimaging techniques and computer science has provided compelling evidence for the quantitative assessment of major depressive disorder (MDD). However, one of the major challenges in computer-aided diagnosis of MDD is to automatically and effectively mine the complementary cross-modal information from limited datasets. In this study, we proposed a few-shot learning framework that integrates multi-modal MRI data based on contrastive learning. In the upstream task, it is designed to extract knowledge from heterogeneous data. Subsequently, the downstream task is dedicated to transferring the acquired knowledge to the target dataset, where a hierarchical fusion paradigm is also designed to integrate features across inter- and intra-modalities. Lastly, the proposed model was evaluated on a set of multi-modal clinical data, achieving average scores of 73.52% and 73.09% for accuracy and AUC, respectively. Our findings also reveal that the brain regions within the default mode network and cerebellum play a crucial role in the diagnosis, which provides further direction in exploring reproducible biomarkers for MDD diagnosis.

DiffMDD: A Diffusion-Based Deep Learning Framework for MDD Diagnosis Using EEG.

Major Depression Disorder (MDD) is a common yet destructive mental disorder that affects millions of people worldwide. Making early and accurate diagnosis of it is very meaningful. Recently, EEG, a non-invasive technique of recording spontaneous electrical activity of brains, has been widely used for MDD diagnosis. However, there are still some challenges in data quality and data size of EEG: (1) A large amount of noise is inevitable during EEG collection, making it difficult to extract discriminative features from raw EEG; (2) It is difficult to recruit a large number of subjects to collect sufficient and diverse data for model training. Both of the challenges cause the overfitting problem, especially for deep learning methods. In this paper, we propose DiffMDD, a diffusion-based deep learning framework for MDD diagnosis using EEG. Specifically, we extract more noise-irrelevant features to improve the model's robustness by designing the Forward Diffusion Noisy Training Module. Then we increase the size and diversity of data to help the model learn more generalized features by designing the Reverse Diffusion Data Augmentation Module. Finally, we re-train the classifier on the augmented dataset for MDD diagnosis. We conducted comprehensive experiments to test the overall performance and each module's effectiveness. The framework was validated on two public MDD diagnosis datasets, achieving the state-of-the-art performance.

The Effects of Selective Serotonin Reuptake Inhibitors on Impulsivity in Young Adults with Major Depression in the Early Phase of Treatment.

Whether selective serotonin reuptake inhibitors (SSRI) increase suicide risk, especially in young adults, is still a controversial issue. This study aimed to examine the change in impulsivity characteristics and to evaluate the relationship between impulsivity and suicidality in young adults with major depression who were started on SSRIs. The study included 50 patients between the ages of 18-24 years with a diagnosis of major depression who were planned to start SSRIs. Participants were evaluated with the Beck Depression Scale, Beck Anxiety Scale, Young Mania Rating Scale, Columbia Suicide Severity Rating Scale, Barratt Impulsivity Scale, Daily Impulsivity Scale (DIS), and Go/ No-Go Task (GNG) before and at the end of the first week of treatment. Seventy percent of the patients (n: 35) completed the assessments at baseline and at the end of the first week. At the end of one-week there was a statistically significant decrease in the DIS (t=2.283, p=0.029) and commission errors in GNG (t=3.19, p=0.003). In addition, 7 out of 11 patients who had suicidal ideation at the first evaluation did not continue to have suicidal ideation at the end of the first week and there was a significant decrease in the severity of suicidal ideation at the end of the follow-up (W:132.0, p<0.001). One-week SSRI use in young adults resulted in a decrease in impulsivity in self-report scales assessing state impulsivity and in the GNG. It was observed that the severity of suicidal ideation decreased at the end of the one-week treatment period.

Vitamin D deficiency in Scheuermann's disease is associated with increased adverse outcomes.

Scheuermann's disease is a diagnosis of hyperkyphosis commonly encountered in pediatric patients. Studies in animal models suggest an association with vitamin D deficiency, however, extensive studies have not been performed in humans. This study analyzes the role of vitamin D deficiency on unfavorable results in patients with Scheuermann's disease. The TriNetX database was utilized to perform a retrospective analysis. Patients in the United States aged 0-18years with Scheuermann's disease were identified using International Classification of Diseases, Tenth Revision (ICD-10) codes and categorized into those with and without a diagnosis of vitamin D deficiency. Comparison of patient groups depending on age, sex, ethnic origin, prior diagnosis of fibromyalgia, anxiety disorder, myositis, and major depressive disorder. Statistical analysis was conducted to identify the association between vitamin D levels and unfavorable results including pain, depression, suicide attempt, emergency department (ED) consult, hospitalization, and procedures on the spine or spinal cord. In total, 11,277 patients were identified, 39% of whom had a concurrent diagnosis of scoliosis. A total of 1,024 (9.08%) were deficient in vitamin D. Patients with vitamin D deficiency had greater odds of pain (P<0.0001), depression (P<0.0001), suicide attempt (P=0.0021), ED visits (P=0.0246), and hospital admission (P<0.0015). Conversely, patients with vitamin D deficiency had decreased odds of surgery on the spine or spinal cord (P=0.0009). Vitamin D deficiency is associated with an elevated risk of pain, depression, suicide attempts, ED visits, and hospitalization. Our analysis highlights the need for more research to study the effect of vitamin D on Scheuermann's disease. Level III, Prognostic.

Effects of the Mental Health Law on Peruvian Primary Care Physicians: A Cross-Sectional Study on Knowledge of Major Depressive Disorder.

The objective was to evaluate the level of knowledge of primary care physicians in Lambayeque, Peru, regarding the diagnosis and treatment of Major Depressive Disorder. A cross-sectional analytical study was conducted among 106 primary care physicians in Lambayeque, Peru. Data were collected using a validated questionnaire assessing Major Depressive Disorder diagnosis and treatment knowledge. Physicians' knowledge levels were categorized as inadequate, adequate, or excellent. Poisson regression models were employed to identify factors associated with adequate knowledge. The response rate was 81.21%. Only 36.79% of physicians demonstrated adequate knowledge, with none achieving excellent knowledge. The median score was 6 (IQR: 5-7). Mental health training, which may reflect the new law's implementation, was significantly associated with higher knowledge levels (PR: 2.42, 95% CI: 1.02 to 5.10). Other factors were not significantly associated with knowledge levels. The proportion of primary care physicians with adequate Major Depressive Disorder knowledge has doubled since 2014, indicating a positive effect of the mental health law. However, to increase this number, continuous professional development programs are needed.

Substance Use Disorder and Suicidal Ideation in Rural Maryland

Background Rural areas in the United States have been disproportionately burdened with high rates of substance use, mental health challenges, chronic stress, and suicide behaviors. Factors such as a lack of mental health services, decreased accessibility to public health resources, and social isolation contribute to these disparities. The current study explores risk factors to suicidal ideation, using emergency room discharge data from Maryland. Methods The current study used data from the Healthcare Cost and Utilization Project (HCUP) State Emergency Department Databases (SEDD) from the State of Maryland. Logistic regression was used to assess the association between ICD-10 coded opioid use disorder, alcohol use disorder, cannabis use disorder, major depressive disorder, and the outcome variable of suicidal ideation discharge. We controlled for income, race, age, and gender. Results Lifetime major depressive disorder diagnosis (odds ration [OR] = 79.30; 95% confidence interval [CI] 51.91-121.15), alcohol use disorder (OR = 6.87; 95% CI 4.97-9.51), opioid use disorder (OR = 5.39; 95% CI 3.63-7.99), and cannabis use disorder (OR = 2.67; 95% CI 1.37-5.18) were all positively associated with suicidal ideation. Conclusions The study highlights the strong link between prior substance use disorder, depression, and suicidal ideation visit to the emergency room, indicating the need for prevention and intervention, particularly among those in rural areas where the burden of suicidal ideation and chronic stress are high. As health disparities between rural and urban areas further widened during the COVID-19 pandemic, there is an urgent need to address these issues.

PREVIDA- IMPACT OF VORTIOXETINE ON MAJOR DEPRESSIVE DISORDER AND PERCEIVED COGNITIVE DYSFUNCTION: A MULTICENTER STUDY

Objective: To find out how common cognitive impairment is in people with major depressive disorder (MDD) and assess how well vortioxetine works for treating both cognitive dysfunction and depressive symptoms. Study Design: Multi-centred, Cross-sectional, prospective follow up design Place &amp; Duration of Study: The research took place over the course of 12 weeks at 16 different psychiatric outpatient clinics throughout Pakistan, from September 2020 to November 2020. Method: The study included 498 individuals with serious depressive disorder diagnoses. The degree of depression symptoms and cognitive impairment was assessed. Psychiatrists gave trial participants vortioxetine after completing the Clinical Global Impression-Severity scale (CGI-S), the Patient Health Questionnaire-9 (PHQ-9), and the Perceived Deficits Questionnaire (PDQ). The variables were reassessed one week (+/- 3 days), one month (+/- 7 days), and three months (+/- 14 days) after the treatment began. Results: After completing a 12-week course of therapy with vortioxetine, mean PHQ 9 and PDQ scores of MDD individuals showed significant improvements. This indicates that vortioxetine is effective in reducing depression symptoms as well as cognitive impairments in MDD patients. A strong connection was seen between PHQ-9 and PDQ scores, indicating a direct relationship between cognitive impairment and depressed symptoms. Conclusions: The results of the research demonstrate how well vortioxetine works to treat cognitive impairments in MDD patients while also reducing depressed symptoms. These findings demonstrate the possibility of vortioxetine as a beneficial therapeutic option for those with MDD diagnoses who also have cognitive impairment. Additional investigation is required to validate these findings and assess their suitability for other demographics.

Evolution of anhedonia in adolescent depression: An interpretative phenomenological analysis study.

Anhedonia is a symptom complex currently linked to dysfunctional reward processing. Phenomenological studies capture anhedonia as a loss of hedonic and eudemonic pleasure. However, there is a lack of integration between neurobiological understanding and clinical phenomenology. This study used a qualitative method to explore the interplay of sociocultural contexts and individual factors associated with the evolution of dysfunctional reward processing in adolescents with depression and anhedonia. Ten female adolescents with a current or prior diagnosis of major depressive disorder were recruited from a public tertiary care child and adolescent psychiatry service. In-depth interviews were conducted, voice recorded, and transcribed verbatim. The transcripts were analyzed using Interpretative Phenomenological Analysis (IPA). The adolescents hailed from urban families. Educational stress and relational difficulties figured prominently. Amotivation was the most important subdomain of anhedonia affected. An integrated framework for understanding the evolution of anhedonia is presented. Five main patterns of dysfunctional reward processing emerged in our study: an overworked system, erroneous reward valuation, reward-effort imbalance, and diversion of the reward processing system for self-preservation. There is a necessity to build robust theoretical models of the evolution of anhedonia, hence finding homogenous sub-groups, paving the way for person-centric interventions for anhedonia.

Cross-Silo, Privacy-Preserving, and Lightweight Federated Multimodal System for the Identification of Major Depressive Disorder Using Audio and Electroencephalogram.

In this day and age, depression is still one of the biggest problems in the world. If left untreated, it can lead to suicidal thoughts and attempts. There is a need for proper diagnoses of Major Depressive Disorder (MDD) and evaluation of the early stages to stop the side effects. Early detection is critical to identify a variety of serious conditions. In order to provide safe and effective protection to MDD patients, it is crucial to automate diagnoses and make decision-making tools widely available. Although there are various classification systems for the diagnosis of MDD, no reliable, secure method that meets these requirements has been established to date. In this paper, a federated deep learning-based multimodal system for MDD classification using electroencephalography (EEG) and audio datasets is presented while meeting data privacy requirements. The performance of the federated learning (FL) model was tested on independent and identically distributed (IID) and non-IID data. The study began by extracting features from several pre-trained models and ultimately decided to use bidirectional short-term memory (Bi-LSTM) as the base model, as it had the highest validation accuracy of 91% compared to a convolutional neural network and LSTM with 85% and 89% validation accuracy on audio data, respectively. The Bi-LSTM model also achieved a validation accuracy of 98.9% for EEG data. The FL method was then used to perform experiments on IID and non-IID datasets. The FL-based multimodal model achieved an exceptional training and validation accuracy of 99.9% when trained and evaluated on both IID and non-IIID datasets. These results show that the FL multimodal system performs almost as well as the Bi-LSTM multimodal system and emphasize its suitability for processing IID and non-IIID data. Several clients were found to perform better than conventional pre-trained models in a multimodal framework for federated learning using EEG and audio datasets. The proposed framework stands out from other classification techniques for MDD due to its special features, such as multimodality and data privacy for edge machines with limited resources. Due to these additional features, the framework concept is the most suitable alternative approach for the early classification of MDD patients.

Multi-feature concatenation and multi-classifier stacking: An interpretable and generalizable machine learning method for MDD discrimination with rsfMRI

Major depressive disorder (MDD) is a serious and heterogeneous psychiatric disorder that needs accurate diagnosis. Resting-state functional MRI (rsfMRI), which captures multiple perspectives on brain structure, function, and connectivity, is increasingly applied in the diagnosis and pathological research of MDD. Different machine learning algorithms are then developed to exploit the rich information in rsfMRI and discriminate MDD patients from normal controls. Despite recent advances reported, the MDD discrimination accuracy has room for further improvement. The generalizability and interpretability of the discrimination method are not sufficiently addressed either. Here, we propose a machine learning method (MFMC) for MDD discrimination by concatenating multiple features and stacking multiple classifiers. MFMC is tested on the REST-meta-MDD data set that contains 2428 subjects collected from 25 different sites. MFMC yields 96.9% MDD discrimination accuracy, demonstrating a significant improvement over existing methods. In addition, the generalizability of MFMC is validated by the good performance when the training and testing subjects are from independent sites. The use of XGBoost as the meta classifier allows us to probe the decision process of MFMC. We identify 13 feature values related to 9 brain regions including the posterior cingulate gyrus, superior frontal gyrus orbital part, and angular gyrus, which contribute most to the classification and also demonstrate significant differences at the group level. The use of these 13 feature values alone can reach 87% of MFMC’s full performance when taking all feature values. These features may serve as clinically useful diagnostic and prognostic biomarkers for MDD in the future.

Fusing multi-scale fMRI features using a brain-inspired multi-channel graph neural network for major depressive disorder diagnosis

Depression stands as one of the most pernicious mental disorders in contemporary society, characterized by a highly intricate pathological mechanism. Specifically, individuals suffering from Major Depressive Disorder (MDD) exhibit heightened vulnerability to suicidal tendencies. Currently, healthcare practitioners often encounter challenges related to the misdiagnosis and underdiagnosis of depression during clinical assessments. Consequently, it is of paramount importance to develop highly accurate auxiliary diagnostic tools for depression. Unfortunately, traditional machine learning and deep learning methodologies frequently neglect the integration of multi-source data and disregard the intricate topological structure and high-order attributes of brain networks. In this study, a multi-scale feature fusion classification framework is proposed to distinguish between MDD patients and healthy controls. Within the proposed model, a novel method, the Cross-Level High-Order Interaction (CLHOI), is introduced and implemented on a low-order functional connectivity (LOFC) matrix to derive two distinct high-order functional connectivity (HOFC) matrices. Subsequently, a Multi-Channel Fusion Graph Convolutional Network (MFGCN) is trained by integrating high-order and low-order brain graph features along with phenotypic information. The results of 10-fold cross-validation experiments conducted on the publicly available REST-meta-MDD dataset indicate that the fusion of multi-scale features improves the average accuracy by approximately 3%, resulting in an accuracy rate of 77.6%. Simultaneously, the hypothesis asserting the existence of intricate information interactions at various levels within brain connectivity networks is validated. Moreover, our model exhibits strong explanatory capabilities, effectively identifying brain regions closely associated with MDD, including the Precentral gyrus, Superior frontal gyrus, Cuneus, Lingual gyrus, and Fusiform gyrus. In comparison to numerous advanced studies within the same domain, our approach has produced competitive results. Furthermore, our proposed method can be readily extended to facilitate the diagnosis of various neurological diseases.

The relationship between familial-genetic risk and pharmacological treatment in a Swedish national sample of patients with major depression, bipolar disorder, and schizophrenia.

Using Swedish registers, we examine whether the prescription of and the response to antidepressants (AD), mood stabilizers (MS), and antipsychotics (AP) in the treatment of, respectively, major depression (MD), bipolar disorder (BD), and schizophrenia (SZ), are influenced by familial-genetic risk. We examined individuals born in Sweden 1960-1995 with a first diagnosis of MD (n = 257,177), BD (n = 23,032), and SZ (n = 4248) from 2006 to 2018. Drug classes and Defined Daily Dose (DDD) were obtained from the Pharmacy register using the Anatomical Therapeutic Chemical system. We utilized the Familial Genetic Risk Scores (FGRS) calculated from morbidity risks in first- through fifth degree relatives. Treatment with antidepressants (AD) in MD, mood-stabilizers (MS) in BD, and antipsychotics (AP) in SZ were associated with significantly higher disorder-specific familial-genetic risks. Using dosage trajectory analysis of AD, MS, and AP treatment for MD, BD, and SZ, respectively, familial-genetic risk was positively associated with higher and/or increasing drug dosages over time. For MD and BD, examining cases started on the most common pharmacologic treatment class (SSRIs for MD and "other anti-epileptics" for BD), familial-genetic risks were significantly lower in those who did not versus did later receive treatment from other AD and MS classes, respectively. Higher familial-genetic risk for BD predicted switching AD medication in cases of MD. Among pharmacologically treated cases of BD, familial-genetic risk was significantly higher for those treated with lithium. In a large population-based patient cohort, we found evidence of a wide-spread association between higher familial-genetic risk and i) increased likelihood of receiving pharmacologic treatment but 2) responding more poorly to it-as indicated by a switching of medications -- and/or requiring higher doses. Further investigations into the clinical utility of genetic risk scores in the clinical managements of MD, BD, and SZ are warranted.

Differentiation Between Alzheimer̓s Disease and Old Age Depression: Overview and Conception of a Questionnaire for Relatives

Differential diagnosis of Alzheimer's disease and major depression in the elderly is difficult because of overlapping and similar symptoms. A questionnaire for relatives may facilitate the diagnostic burden in clinical practice. Based on a selection of clinical criteria, an expert survey, and statistical analysis of the expert survey, a questionnaire for relatives was designed. 18 out of 48 suggested clinical criteria were assessed by neuropsychologists as suitable for the differential diagnosis and were summarized in a questionnaire for relatives (ADAD). First results of the validation of the questionnaire gave an indication of the suitability of the clinical criteria. The ADAD questionnaire could be simply implemented in clinical practice to differentiate between symptoms of Alzheimer's disease and major depression, but it needs to be evaluated in a larger group of patients.

DepressionGraph: A Two-Channel Graph Neural Network for the Diagnosis of Major Depressive Disorders Using rs-fMRI

Major depressive disorder (MDD) is a prevalent psychiatric condition with a complex and unknown pathological mechanism. Resting-state functional magnetic resonance imaging (rs-fMRI) has emerged as a valuable non-invasive technology for MDD diagnosis. By utilizing rs-fMRI data, a dynamic brain functional connection network (FCN) can be constructed to represent the complex interacting relationships of multiple brain sub-regions. Graph neural network (GNN) models have been widely employed to extract disease-associated information. The simple averaging or summation graph readout functions of GNNs may lead to a loss of critical information. This study introduces a two-channel graph neural network (DepressionGraph) that effectively aggregates more comprehensive graph information from the two channels based on the node feature number and node number. Our proposed DepressionGraph model leverages the transformer–encoder architecture to extract the relevant information from the time-series FCN. The rs-fMRI data were obtained from a cohort of 533 subjects, and the experimental data show that DepressionGraph outperforms both traditional GNNs and simple graph readout functions for the MDD diagnosis task. The introduced DepressionGraph framework demonstrates efficacy in extracting complex patterns from rs-fMRI data and exhibits promising capabilities for the precise diagnosis of complex neurological disorders. The current study acknowledges a potential gender bias due to an imbalanced gender distribution in the dataset. Future research should prioritize the development and utilization of gender-balanced datasets to mitigate this limitation and enhance the generalizability of the findings.

Client Centered Therapy for Clients with a Diagnosis of Major Depression

This research aims to reduce symptoms of depression using client centered therapy (CCT) and after that a test is carried out to see its effectiveness. The definition of depression according to the World Health Organization is a mental health disorder characterized by intense feelings of sadness, reduced interest in things you usually enjoy, decreased energy, feelings of guilt, low self-esteem, insomnia, reduced appetite, fatigue, lack of concentration and lack of thinking. to suicide (WHO, 2023). The subject in this study was a 24-year-old man who was diagnosed with severe depression. The approach used is a quantitative approach with an experimental method using a Single Case Design or Single-Subject Design. The data obtained used observation methods, interviews, psychological tests consisting of cognitive tests (WAIS), personality (Grafis, Wartegg, SSCT) and inventory, namely the DASS scale (Depression Anxiety Stress Scale). The research results showed that client centered therapy (CCT) was effective in reducing depressive symptoms experienced by the subjects. After carrying out quantitative measurements, it showed a decrease in depression scores from the very severe category (26) to the normal category (1). Qualitatively, it shows a reduction in depressive symptoms in the subjects, which can be seen from cognitive changes that become more capable of positive thinking and changes in more productive behavior. As for further research, it is hoped that client centered therapy can be used to treat clients with other diagnoses to enrich research results related to client centered therapy.Penelitian ini bertujuan untuk menurunkan gejala depresi dengan menggunakan client centered therapy (CCT) dan setelah itu dilakukan uji untuk melihat efektivitasnya. Defini depresi mengacu pada World Health Organization merupakan gangguan kesehatan mental yang ditandai dengan perasaan sedih yang intens, berkurangnya minat terhadap sesuatu yang biasa disenangi, penurunan energi, adanya perasaan bersalah, rendah diri, insomnia, berkurangnya nafsu makan, kelelahan, kurang konsentrasi dan adanya pemikiran untuk bunuh diri (WHO, 2023). Subjek pada penelitian ini yaitu seorang laki-laki berumur 24 tahun yang terdiagnosa mengalami depresi berat. Pendekatan yang digunakan adalah pendekatan kuantitatif dengan metode eksperimen menggunakan rancangan Single Case Design atau Single-Subject Design. Data yang diperoleh menggunakan metode observasi, wawancara, tes psikologi yang terdiri dari tes kognitif (WAIS), kepribadian (Grafis, Wartegg, SSCT) dan inventori yaitu skala DASS (Depression Anxiety Stress Scale). Hasil penelitian menunjukkan bahwa client centered therapy (CCT) efektif untuk menurunkan gejala depresi yang dialami oleh subjek. Setelah dilakukan pengukuran secara kuantitatif menunjukkan penurunan skor depresi dari kategori sangat parah (26) menjadi kategori normal (1). Secara kualitatif menunjukkan berkurangnya gejala depresi pada subjek yaitu terlihat dari perubahan kognitif yang menjadi lebih mampu berpikir positif dan perubahan pada perilaku yang lebih produktif. Adapun untuk penelitian selanjutnya, diharapkan dapat menggunakan client centered therapy untuk menangani klien dengan diagnosis lain guna memperkaya hasil penelitian terkait client centered therapy.

Arthroscopic subacromial decompression improved outcomes in situationally depressed patients compared to clinically depressed or nondepressed patients

The purpose of this study is to evaluate patient reported outcomes after arthroscopic extensive débridement of the shoulder with subacromial decompression (SAD) for subacromial impingement using the Patient-Reported Outcomes Measurement Information System (PROMIS) system and evaluate if depression (Dep) (clinical or situational) impacts patients achieving a Minimal Clinically Important Difference (MCID). Preoperative PROMIS Physical function (PF), Mood, and Dep scores were obtained at the closest date prior to arthroscopic rotator cuff repair and postoperative scores were collected at every clinical visit thereafter. Final PROMIS score used for data analysis was determined by the patients final PROMIS value between 90 to 180 days. Clinical Dep was determined by patients having a formal diagnosis of "Depression or Major Depressive Disorder" at the time of their surgery. Situationally depressed patients, those without a formal diagnosis yet exhibited symptomatic depressive symptoms, were classified by having a PROMIS-Dep cutoff scores larger than 52.5. A total of 136 patients were included for final statistical analysis. 13 patients had a clinical but not situational diagnosis of Dep, 86 patients were identified who had no instance of clinical or situational Dep (nondepressed). 35 patients were situationally depressed. All three cohorts demonstrated a significant improvement in postoperative PROMIS Dep, PI, and PF score relative to their preoperative value (P=.001). Situationally depressed patients achieved greater delta PROMIS-Dep compared to patients with major depressive disorder. Depressed patients had a higher chance of achieving MCID for PROMIS-Dep compared to nondepressed patients (P=.01). Logistic regression analysis demonstrated that underlying Dep did not alter the odds of obtaining MCID compared to nondepressed patients. Nonsmoking patients had significantly greater odds of achieving MCID for PF (P=.02). Patients improved after undergoing SAD regardless of underlying Dep or depressive symptoms. Depressed patients exhibited greater change in PROMIS scores compared to nondepressed patients. Smoking remains a risk factor for postoperative outcomes in patients undergoing SAD for subacromial impingement. Identifying and counseling patients with underlying depressive symptoms without a formal major depressive disorder diagnosis may lead to improved outcomes. These findings may help guide clinicians in deciding who would benefit the most from this procedure.

Real-world clinical outcomes and treatment patterns in patients with MDD treated with vortioxetine: a retrospective study

BackgroundThis study included evaluation of the effectiveness of vortioxetine, a treatment for adults with major depressive disorder (MDD), using patient-reported outcome measures (PROMs) in a real-world setting.MethodsThis retrospective chart review analyzed the care experiences of adult patients with a diagnosis of MDD from Parkview Physicians Group – Mind-Body Medicine, Midwestern United States. Patients with a prescription for vortioxetine, an initial baseline visit, and ≥ 2 follow-up visits within 16 weeks from September 2014 to December 2018 were included. The primary outcome measure was effectiveness of vortioxetine on depression severity as assessed by change in Patient Health Questionnaire-9 (PHQ-9) scores ~ 12 weeks after initiation of vortioxetine. Secondary outcomes included changes in depression-related symptoms (i.e., sexual dysfunction, sleep disturbance, cognitive function, work/social function), clinical characteristics, response, remission, and medication persistence. Clinical narrative notes were also analyzed to examine sleep disturbance, sexual dysfunction, appetite, absenteeism, and presenteeism. All outcomes were examined at index (start of vortioxetine) and at ~ 12 weeks, and mean differences were analyzed using pairwise t tests.ResultsA total of 1242 patients with MDD met inclusion criteria, and 63.9% of these patients had ≥ 3 psychiatric diagnoses and 65.9% were taking ≥ 3 medications. PHQ-9 mean scores decreased significantly from baseline to week 12 (14.15 ± 5.8 to 9.62 ± 6.03, respectively; p < 0.001). At week 12, the response and remission rates in all patients were 31.0% and 23.1%, respectively, and 67% continued vortioxetine treatment. Overall, results also showed significant improvements by week 12 in anxiety (p < 0.001), sexual dysfunction (p < 0.01), sleep disturbance (p < 0.01), cognitive function (p < 0.001), work/social functioning (p = 0.021), and appetite (p < 0.001). A significant decrease in presenteeism was observed at week 12 (p < 0.001); however, no significant change was observed in absenteeism (p = 0.466).ConclusionsUsing PROMs, our study results suggest that adults with MDD prescribed vortioxetine showed improvement in depressive symptoms in the context of a real-world clinical practice setting. These patients had multiple comorbid psychiatric and physical diagnoses and multiple previous antidepressant treatments had failed.

Finding a balance: resilience in older adults after depression in later life

Abstract Older adults who have had a major depressive disorder (MDD) have a high risk of relapse. Although risk factors for depression have been researched extensively, less is known about protective factors, and what experiences might strengthen subsequent resilience and help to prevent relapse. Therefore, this qualitative study explored factors of resilience in older adults who recovered from MDD and did not relapse across at least six years. Twenty-five semi-structured interviews were held with older adults aged 73–85 years who participated in the Netherlands Study of Depression in Older Persons from 2008 to 2014 and were re-interviewed for the present study in 2020–2021. Participants were defined as resilient based on having an MDD diagnosis at baseline but not on two- and six-year follow-up. We used grounded theory coding techniques and thematic analysis to identify factors contributing to resilience. Factors contributing to resilience included: taking agency; receiving social support and engaging in social activities; doing activities individually; and managing thought processes. Resilience after late-life depression appeared to be a dynamic process involving internal and external factors, including finding a balance between rest and activity, between taking initiative and receiving support by others, and between accepting negative emotions and ignoring negative thoughts. Additionally, the ability to learn from depression shows that resilience is not only about avoiding psychopathology, but also about the recovery process and preventing relapse. These findings highlight the need for research and interventions to focus on understanding and influencing the dynamics underlying resilience.