Diagnosis Of Depression Research Articles

Transdiagnostic Attentional Deficits Are Associated with Depressive and Externalizing Symptoms in Children and Adolescents with Neuropsychiatric Disorders.

Although inattention, impulsivity, and impairments to vigilance are most associated with attention-deficit/hyperactivity disorder (ADHD), transdiagnostic attentional deficits are prevalent across all psychiatric disorders. To further elucidate this relationship, the present study investigated parent-reported neuropsychiatric symptom correlates of attention deficits using the factor structure of the Conners' Continuous Performance Test (CPT-II), a neuropsychological test of attention. Two-hundred and eighteen children and adolescents (7-21years old) completed the CPT-II as part of standard clinical protocol during outpatient pediatric neuropsychology visits. The factor structure of the CPT-II was determined with a principal component analysis (PCA) using Promax rotation. Pearson correlation analyses and regression models examined the relationship between the generated factor structure, parent-reported clinical symptoms, and pre-determined clinical diagnoses. Results from the PCA suggested a three-factor model best supported the structure of the CPT-II, and were subsequently defined as inattention, impulsivity, and vigilance. Performance-based inattention was significantly correlated with parent-reported hyperactivity, aggression, conduct problems, and depression. Parent-reported depressive symptoms and conduct problems were the strongest correlates of performance-based inattention, not hyperactivity or aggression. Performance-based inattention was significantly associated with an ADHD diagnosis but not a depression or anxiety diagnosis. Findings suggest attentional deficits are not specific to any one disorder. To enhance the identification, classification, and treatment of neuropsychiatric disorders, both researchers and clinicians alike must diminish the importance of categorical approaches to child/adolescent psychopathology and continue to consider the dimensionality of transdiagnostic characteristics such as inattention.

An historical overview of artificial intelligence for diagnosis of major depressive disorder.

The Artificial Intelligence (AI) technology holds immense potential in the realm of automated diagnosis for Major Depressive Disorder (MDD), yet it is not without potential shortcomings. This paper systematically reviews the research progresses of integrating AI technology with depression diagnosis and provides a comprehensive analysis of existing research findings. In this context, we observe that the knowledge-driven first-generation of depression diagnosis methods could only address deterministic issues in structured information, with the selection of depression-related features directly influencing identification outcomes. The data-driven second-generation of depression diagnosis methods achieved automatic learning of features but required substantial high-quality clinical data, and the results were often obtained solely from the black-box models which lack sufficient explainability. In an effort to overcome the limitations of the preceding approaches, the third-generation of depression diagnosis methods combined the strengths of knowledge-driven and data-driven approaches. Through the fusion of information, the diagnostic accuracy is greatly enhanced, but the interpretability remains relatively weak. In order to enhance interpretability and introduce diagnostic criteria, this paper offers a new approach using Large Language Models (LLMs) as AI agents for assisting the depression diagnosis. Finally, we also discuss the potential advantages and challenges associated with this approach. This newly proposed innovative approach has the potential to offer new perspectives and solutions in the diagnosis of depression.

Eye movement characteristics of emotional face recognizing task in patients with mild to moderate depression.

Depression is a complex affective disorder characterized by high prevalence and severe impact, commonly presenting with cognitive impairment. The objective diagnosis of depression lacks precise standards. This study investigates eye movement characteristics during emotional face recognition task (EFRT) in depressive patients to provide empirical support for objective diagnosis. We recruited 43 patients with depression (Depressive patients, DP) from a psychiatric hospital and 44 healthy participants (Healthy Control, HC) online. All participants completed an EFRT comprising 120 trials. Each trial presented a gray screen for 800 ms followed by a stimulus image for judgment. Emotions were categorized as positive, neutral, or negative. Eye movement trajectories were recorded throughout the task. Latency of First Fixation (LFF), Latency of First Fixation for Eye AOI, and Latency of First Fixation for Mouth AOI were used as representative indicators of early attention, Proportion of Eye AOI, and Proportion of Mouth AOI as measures of intermediate attention, Accuracy (ACC) and Reaction Time (RT) as behavioral indicators of late-stage attention. In this study, these metrics were employed to explore the differences between patients with depression and healthy individuals. Compared to healthy participants, individuals with depression exhibit longer first fixation latencies on the eyes and mouth during the early attention stage of emotional face recognition, indicating an avoidance tendency toward key facial recognition cues. In the mid-to-late attention stages, depressive individuals show an increased fixation ratio on the eyes and a decreased fixation ratio on the mouth, along with lower accuracy and longer response times. These findings suggest that, relative to healthy individuals, individuals with depression have deficits in facial recognition. This study identified distinct attention patterns and cognitive deficits in emotional face recognition among individuals with depression compared to healthy individuals, providing an attention-based approach for exploring potential clinical diagnostic markers for depression.

Changes in Exercise Performance in Patients During a 6-Week Inpatient Psychiatric Rehabilitation Program and Associated Effects on Depressive Symptoms.

Background/Objectives: The impact of exercise on affective disorders has been demonstrated in various studies. However, almost no data are available on performance effects. Therefore, this study investigated exercise performance related to the severity of depression symptoms in a pre-post within-subjects design in a 6-week standard inpatient psychiatric rehabilitation program. Methods: A total of 53 individuals (20 female; mean age, 40.98 ± 11.33) with a primary diagnosis of depression performed a cardiopulmonary exercise test (CPX) to obtain maximal oxygen uptake (VO2max), maximal power output (Pmax), and the first and second ventilatory threshold (VT1, VT2) at the start and the end of the rehabilitation. Degree of depression was assessed by Becks Depression Inventory (BDI) and the Brief Symptom Inventory test (BSI). Overall activity was measured by accelerometer step-counts. Results: Mean total step-count per day during rehabilitation was high (12,586 ± 2819 steps/day). Patients' BDI and BSI at entry were 21.6 ± 8.83 and 65.1 ± 6.8, respectively, and decreased significantly (p < 0.001) following rehabilitation, to 10.1 ± 9.5 and 54.5 ± 11.3, respectively. Pmax and VO2max increased significantly (p < 0.001) from entry values (182.6 ± 58.7 W, 29.74 ± 5.92 mL·kg-1·min-1) following rehabilitation: by 11.91 ± 12.09 W and 1.35 ± 2.78 mL·kg-1·min-1, respectively. VT1 and VT2 showed a similar behavior. An increase in physical performance could predict improvement in BDI (R2 = 0.104, F(1,48) = 5.582, p = 0.022) but not in BSI. Conclusions: The program was associated with improved mental health status in all patients and increased performance in the majority of patients, although increases were small. Since improvements in exercise performance may be positively related to depression symptoms and comorbidities, it is recommended to individualize and tailor exercise programs, which could yield larger effects.

Abstract 4147617: Depression and the Development of Cardiovascular Disease: Insights from All of Us Research Program

Introduction/Background: Depression is recognized as a risk factor for worse outcomes in patients with cardiovascular disease, but there is limited research on whether individuals with depression have an increased incidence of future cardiovascular disease. Research questions/Hypothesis: Do individuals with depression have an increased incidence of cardiovascular disease? Goals/Aims: This study was conducted to evaluate the association between a diagnosis of depression and the risk of developing future cardiovascular events, to determine whether the treatment of depression should be considered as a primary form of cardiovascular disease prevention. Methods/Approach: We conducted a prospective analysis using the “All Of Us” Research Program dataset. Participants who responded to the historical depression status in the “Personal/Family History Survey” and with linked electronic health records data were considered eligible for this study. We used Cox regression models to calculate the incidence of myocardial infarction (MI), stroke, heart failure (HF), and atrial fibrillation (AF) and the hazard ratio (HR) of a diagnosis of depression. Models were adjusted for sex, race, age, and prevalent hypertension, diabetes mellitus, hyperlipidemia, and smoking. The survival analysis of the four outcomes was conducted in parallel. We conducted sensitivity analysis by excluding participants with documentation of prior MI, stroke, HF, and/or AF. Results/Data: 100,030 participants were included in this study, with a mean age of 53.2 (standard deviation of 16.5). 68.4% of participants were female, 10% were Hispanic, 9.6% were Black, and 37.1% had a diagnosis of depression. The median follow up time was 3 years. Compared to participants without depression, participants with depression were at risk of developing MI (aHR 1.22, 95% CI 1.05–1.41), stroke (aHR 1.68, 95% CI 1.06–2.68), HF (aHR 1.28, 95% CI 1.15–1.43), and AF (aHR 1.14, 95% CI 1.02–1.27). In the sensitivity analysis, participants with depression were at risk of developing MI (aHR 1.22, 95% CI 1.03–1.44), stroke (aHR 1.68, 95% CI 1.06–2.68), and HF (aHR 1.31, 95% CI 1.15–1.48) but not AF (aHR 1.12, 95% CI 0.99–1.27). Conclusions: This study indicates that patients with a diagnosis of depression were more likely to develop cardiovascular disease, even if they had no known history of cardiovascular disease. Providers should include the treatment of depression as a part of a holistic approach to cardiovascular disease prevention.

DPD (DePression Detection) Net: a deep neural network for multimodal depression detection.

Depression is one of the most prevalent mental conditions which could impair people's productivity and lead to severe consequences. The diagnosis of this disease is complex as it often relies on a physician's subjective interview-based screening. The aim of our work is to propose deep learning models for automatic depression detection by using different data modalities, which could assist in the diagnosis of depression. Current works on automatic depression detection mostly are tested on a single dataset, which might lack robustness, flexibility and scalability. To alleviate this problem, we design a novel Graph Neural Network-enhanced Transformer model named DePressionDetect Net (DPD Net) that leverages textual, audio and visual features and can work under two different application settings: the clinical setting and the social media setting. The model consists of a unimodal encoder module for encoding single modality, a multimodal encoder module for integrating the multimodal information, and a detection module for producing the final prediction. We also propose a model named DePressionDetect-with-EEG Net (DPD-E Net) to incorporate Electroencephalography (EEG) signals and speech data for depression detection. Experiments across four benchmark datasets show that DPD Net and DPD-E Net can outperform the state-of-the-art models on three datasets (i.e., E-DAIC dataset, Twitter depression dataset and MODMA dataset), and achieve competitive performance on the fourth one (i.e., D-vlog dataset). Ablation studies demonstrate the advantages of the proposed modules and the effectiveness of combining diverse modalities for automatic depression detection.

Life After EBPs: Characterizing Subsequent Engagement in Evidence-Based Psychotherapy After Completion of an Initial Trauma-Focused EBP in a National Sample of VA Patients.

Many Veterans who complete prolonged exposure (PE) or cognitive processing therapy (CPT) report residual symptoms, but it is unclear how to best address the mental health needs of these individuals. Examining patterns of mental health service utilization following completion of these two treatments may provide insight into how to best serve this group of individuals. In a large cohort of Veterans (N = 12,514) who sought treatment in the Veterans Health Administration during Fiscal Years 2015-2019, logistic regression models were used to assess the odds of initiating an additional course of trauma-focused (i.e., PE or CPT) or depression-focused psychotherapy in the year following completion of PE or CPT based on demographic, psychiatric, and treatment effectiveness-related variables. Approximately 9% of Veterans engaged in either trauma-(6%) or depression-(3%) related psychotherapy in the year following discharge from PE or CPT. Factors associated with increased odds of trauma-focused treatment initiation included having a sleep disorder diagnosis (OR = 1.23), a substance use disorder diagnosis (OR = 1.27), or experiencing military sexual trauma (OR = 1.64). Factors associated with increased odds of depression-focused treatment initiation included having a depression diagnosis (OR = 2.02). This study suggests that certain subgroups of Veterans who engage in PE or CPT (e.g., Veterans with comorbid sleep or substance use problems) are more likely to seek additional evidence-based treatment and may require augmentations to maximize clinical benefits, either during the initial course of treatment or subsequent to PTSD treatment.

Abstract 4121039: Refining the American Heart Association Predicting Risk of Cardiovascular Disease EVENTs (PREVENT) equations using depression and anxiety disorder

Background: Growing evidence has suggested associations of common mental health conditions including depression and anxiety disorder with CVD, but no studies have examined their incremental predictive values in PREVENT (AHA Predicting Risk of CVD EVENTs). Research questions: Does including anxiety and/or depression improve the PREVENT predictive performance? Aims: We used a range of measures of anxiety and depression and combinations to examine whether these conditions improved the prediction performance of PREVENT. Methods: New risk prediction models were developed and internally validated using 60% and 40% of the UK Biobank cohort data, respectively. Mental health predictors included: depressive symptom score based on the four-item Patient Health Questionnaire and self-reported and record-based anxiety and depression diagnoses before baseline. CVD events were ascertained through hospital admission and death certificate data after baseline. The Cox proportional hazards models were used to calculate coefficients and 95% confidence intervals. The incremental predictive values were examined by adding the mental health predictors to the PREVENT predictors based on Harrell’s C-indices, sensitivity, specificity, positive and negative predictive values, and net reclassification improvement indices (NRIs). Results: Of the 502,366 UK Biobank participants, 195,489 were included in the derivation set and 130,326 were included in the validation set. In the validation set, the inclusion of each mental health measure produced an increase in C-index and higher sensitivity, specificity, and positive and negative predictive values compared with the PREVENT equations, with the greatest improvements for depressive symptom score. Depressive symptom score produced the greatest improvements in net reclassification overall (NRI 0.84, 95% CI 0.46–1.20) and this was further improved by adding recorded anxiety and depression (NRI 0.89, 95% CI 0.52–1.24). In addition, depressive symptom score showed better discrimination and NRI in both female and male validation sets. Conclusions: Our findings suggest that including depressive symptom score in PREVENT could improve the prediction of CVD with low cost and without invasive procedures.

Abstract 4118183: Anxiety disorder, depression and coronary artery disease: associations and modification by genetic susceptibility

Background: Associations of anxiety disorder and depression with coronary artery disease (CAD) are heterogeneous between populations. Research questions: How does genetic susceptibility to CAD alter the associations between anxiety and depression and incident CAD? Aims: To investigate how genetic susceptibility to CAD alters the associations of anxiety and depression with incident CAD, comparing and combining anxiety and depression. Methods: This is a prospective cohort study using UK Biobank. Diagnoses of anxiety disorder and depression before the baseline assessment were ascertained through linked hospital admission data. Incident CAD was ascertained through hospital admission and death certificate data after baseline. CAD polygenic risk score (PRS) was obtained from CARDIoGRAMplus4 and categorised into low, intermediate, and high. Cox proportional hazard models were used to examine associations between anxiety and depression and CAD. The product term of PRS and these mental health conditions, and the relative excess risk due to interaction (RERI), were used to investigate the presence of multiplicative and additive interactions, respectively. Results: Our study included 288,152 participants of whom 54.4% were female, with a mean age of 56.4 years. Both anxiety disorder (HR: 2.24, 95% CI: 1.86–2.70) and depression (HR: 2.04, 95% CI: 1.80–2.32) were associated with CAD after adjusting for sociodemographic confounders. There was an addictive interaction between depression and PRS (RERI: 0.86; 95% CI: 0.06–1.65) such that the association between depression and CAD was strongest among those with a high PRS whilst there was no such evidence for anxiety disorder. Anxiety only (HR 1.69, 95% 1.17–2.45), depression only (HR 2.01, 95% CI 1.62–2.49) and concomitant anxiety and depression (HR 3.51, 95% CI 2.26–5.45) were associated with CAD even among people with a low PRS. Lifestyle mediators attenuated all these associations across PRS categories. Conclusions: CAD genetic susceptibility might partly contribute to the clustering of depression and CAD but does not provide a full explanation, nor does it explain the association between anxiety disorder and CAD. Therefore, other mechanisms should be explored.

Abstract 4120458: Predictors of School Non-Engagement in School Aged Children and Adolescents Born with Heart Disease

Introduction: School engagement, or caring about doing well and doing required homework, is an important factor for academic success and may contribute to poorer academic outcomes in children born with heart disease (HD), compared to heart-healthy peers. Factors that influence lack of school engagement in children born with HD are not well known. Research Question: What factors predict school non-engagement in students with HD? Aims: To identify factors that predict school non-engagement in students with HD to assist in earlier detection and support for students at increased risk. Methods: We analyzed data from the 2022 National Survey of Children’s Health (NSCH), using procedures for complex weighted survey data. School-related outcomes in children ages 6 to 17 years born with and without HD were compared using Chi-square tests. Multivariate logistic regression was used to examine predictors of school non-engagement in students with HD. Results/Data: Of 54,065 NSCH responses, 34,284 were children ages 6 to 17 years, and 826 of these were born with HD. Children born with HD had lower school engagement (p&lt;0.001) and missed more days of school (p&lt;0.001) than peers not born with HD, but grades were not different between groups. Students born with HD were more likely than peers not born with HD to be bullied (p = 0.001), have a diagnosis of anxiety (3.71% vs 1.95%, p&lt;0.001) or depression (3.56% vs 2.09%, p =0.012), require a Special Education Plan (34.9% vs 16.1%, p&lt;0.001), or to receive mental health services (23% vs 14%, p &lt;0.001). Multivariate logistic regression identified factors that significantly predicted school non-engagement in students born with HD, including diagnosis of attention deficit/hyperactivity disorder (ADHD) (aOR 1.94, 95% CI [1.20, 4.58], p=0.013), behavior/conduct problems (aOR 2.77, 95% CI [1.46, 5.23], p=0.002), or learning disability (aOR 3.44, 95% CI [1.79, 6.59], p&lt;0.001). Sociodemographic variables, days of missed school, anxiety, depression, intellectual disability, autism spectrum disorder, and speech disorder were not significant predictors of school non-engagement. Special healthcare needs status neared significance in predicting lack of school engagement (p = 0.056) Conclusions: We identified ADHD, behavior/conduct problems, and learning disabilities as predictors of school non-engagement in students with HD. This information can be used to identify at-risk students born with HD who may benefit from proactive screening and support.

Can an evidence-based mental health intervention be implemented into preexisting home visiting programs using implementation facilitation? Study protocol for a three variable implementation effectiveness context hybrid trial

BackgroundPerinatal mental health conditions are the most common complication of pregnancy and childbirth (1 in 8 women). When left untreated, perinatal depression and anxiety adversely affects the entire family with pregnancy complications and negative outcomes including preterm birth, impaired mother-infant bonding, impaired lactation, substance abuse, divorce, suicide, and infanticide. Significant disparities persist in the diagnosis and treatment of perinatal depression and anxiety and these inequities are often intersectional. Preliminary research with stakeholders including community advisory boards, underrepresented and minority birthing people, and state departments of health, demonstrates the importance of social support as a mechanism for reducing disparities in perinatal depression, particularly in rural geographies. Home visiting programs (HVPs) can provide the social support needed to improve mental health outcomes in pregnant and postpartum women. Our project aims to explore the impact of context on the implementation of a mental health intervention, focusing on the lived experiences of diverse populations served by HVPs to reduce disparities in adverse maternal outcomes.MethodsUsing implementation facilitation, our study will engage multilevel stakeholders (e.g., policymakers, front-line implementers, and intervention recipients) to adapt facilitation to integrate a maternal mental health intervention (i.e., Mothers and Babies) across two midwestern, rural states (Iowa and Indiana) with multiple HVP models. Given the complexity and heterogeneity of the contexts in which Mothers and Babies will be integrated, a three variable hybrid implementation-effectiveness-context trial will test the adapted facilitation strategy compared with implementation as usual (i.e., standard education) and will assess contextual factors related to the outcomes. Using an evidence-based implementation strategy that tailors implementation delivery to the needs of the specific populations and context may improve fidelity and adoption, particularly in rural states where residents have limited access to care.DiscussionThe immediate impact of this research will be to show whether adapted facilitation can improve the uptake and fidelity of Mothers and Babies across multiple HVP models and thus positively affect depressive symptoms and perceived stress of recipients. Our implementation protocol may be used by researchers, practitioners, and policy makers to better integrate evidence-based interventions into diverse contexts, leading to more equitable implementation and improved health outcomes.Trial registrationClinicalTrials.gov Identifier: NCT06575894, registered on August 29, 2024 https://clinicaltrials.gov/study/NCT06575894?id=NCT06575894&rank=1.

Detection of Major Depressive Disorder Using Genetic Algorithm for Features from EEG Signals

Introduction: Depression is a widespread mental disorder that significantly impacts individuals' well-being and quality of life. In the previous few years, researchers have looked into the potential of electroencephalogram signals in detecting and diagnosing depression. The present study investigates an approach for depression detection using a genetic algorithm to optimize the selection of discrete wavelet transform features from EEG signals. The proposed method involves decomposing EEG data into seven sub-bands using the DWT, extracting relevant statistical, geometric, and physiological features, and then employing a genetic algorithm to identify the most informative features for depression recognition. The SVM classifier achieved the highest overall accuracy of 91.73%, a sensitivity of 91.01%, a recall of 92.18%, and an F1-score of 91.45%, outperforming the other models. Experimental results indicate that the proposed approach outperforms hand-engineered methods and highlighting its potential as a complementary tool for depression diagnosis.

Advances in Brain-Computer Interface Technology for Depression Diagnosis: A Focus on EEG, MEG, and fNIRS

Abstract. With the acceleration of the pace of modern social life, the psychological pressure on individuals has also increased. Consequently, a series of problems brought about by depression are increasingly recognized, such as its impact on mental health, social functioning, and overall quality of life, and the demand for accurate depression diagnosis has gradually increased. This has led to the exploration of innovative Brain-Computer Interface (BCI)-based diagnostic methods that leverage brain-machine interface technology. The application of related technologies for depression diagnosis is introduced from the aspects of Electroencephalography (EEG), Magnetoencephalography (MEG), and Near-infrared (NIR) imaging technology. These three technologies represent emerging approaches in depression diagnosis, adopting concepts that differ from traditional diagnostic methods. During the diagnostic process, they rely on electrocardiac and brain magnetic field patterns to provide important information related to brain activity. These methods enable non-invasive monitoring of cerebral blood oxygen levels, which is crucial for understanding the physiological underpinnings of depressive symptoms.

Adherence to the Mediterranean diet and risk of anxiety and depression in people with obesity: a cross-sectional analysis.

Anxiety and depression are common mental disorders worldwide, in particular in people with obesity. Adherence to the Mediterranean diet has been found beneficial for the prevention of anxiety and depression in the general population. We aimed to evaluate this association in a large cohort of people with obesity. A cross-sectional study of 4957 patients with obesity (63.1% women, median age 49 years, IQR 40-58 years and BMI 33.6 kg/m2, IQR 31.6-36.9 kg/m2) was carried out. Clinical history, anthropometric measurements and lifestyle-related information were investigated. A 14-item MEDAS questionnaire was used to assess adherence to the Mediterranean diet. The 20-item STAI2 questionnaire and the 24-item QD questionnaire were used to assess the presence of anxious and depressive symptoms, respectively. Clinical cases of anxiety and depression were identified among patients with a physician-made diagnosis of anxiety and depression or with STAI2 and QD score ≥95th percentiles of reference population. Overall, 11.9% of participants were positive for anxiety and 11% for depression. Multivariate linear regression models showed a decrease in STAI2 and QD scores of 0.18 points (95%CI: -0.33, -0.03) and 0.10 points (95%CI: -0.16, -0.03), respectively, for each 1-point increase in MEDAS score. The multivariate logistic regression model showed a 7% reduction in the odds of anxiety and depression for each 1-point MEDAS increase (OR = 0.93, 95%CI: 0.89, 0.99; OR = 0.93, 95%CI: 0.88, 0.98). Mediterranean diet is associated with a lower risk of anxiety and depression in people with obesity. Longitudinal studies are needed to determine causality.

Relationship between a diagnosis of kidney failure and heart diseases in patients with hyperkalemia

Objectives This study seeks to determine the association between kidney failure and heart diseases by examining how they influence the diagnosis of hyperkalemia. Methods We employ a fuzzy regression discontinuity design (RDD) by harnessing the inherent threshold in potassium levels, which serves as a diagnostic criterion for hyperkalemia. Simultaneously, we utilize patient diagnosis data related to kidney failure and heart diseases. This approach allows us to evaluate the causal impact of both diagnoses on hyperkalemia. Results Significant overall increases in the risk of developing hyperkalemia are evident subsequent to a diagnosis of kidney failure or heart disease. The study finds that the probability of receiving a kidney failure diagnosis increases by 11.2% regarding a cut-off of 6 mEq/L of potassium. In addition, there is a 6.8% likelihood of experiencing hyperkalemia in the case of a prior diagnosis of hypertension, and an 8.8% probability in the case of a diagnosis of depression. The findings remain robust when considering alternative parametric and non-parametric specifications as well as placebo tests. Conclusions This study provides new empirical insights into the causal impact of kidney failure and heart disease, underscoring the significance of monitoring such patients to prevent serious complications in the future.

Self-Rated ECT Outcomes in Patients With Depression: A Naturalistic Single-Site Study.

Electroconvulsive therapy (ECT) is considered to be the most effective treatment for severe depression. This study investigated recent ECT outcomes for depression at a large tertiary center, which also provides community care. Data were obtained from Mayo Clinic Rochester patients ages 18 and older who received an acute course of ECT between August 1, 2017 and April 30, 2024. Patients were included if there was a depressive disorder diagnosis (unipolar or bipolar) and a self-rated Patient Health Questionnaire-9 (PHQ-9) within 10 days of the start and end of the acute course. Patients were excluded if the starting PHQ-9 score was less than 10. The age, sex, number of acute course treatments, stimulus electrode lead placement, and PHQ-9 scores were collected. Response (PHQ-9 improvement ≥50%) and remission (PHQ-9 < 5) rates were calculated. Linear and logistic regressions were performed to investigate predictors of response and remission. Of 1206 patients identified, 408 met final inclusion and exclusion criteria. The response rate was 80.4%, and remission rate was 52.7%. Logistic regression for response showed no significant predictors; the P value for age just missed statistical significance (odds ratio, 1.0152; 95% confidence interval, 0.9991-1.0316; P = 0.0641). Logistic regression for remission showed only a lower baseline PHQ-9 score (odds ratio, 0.9465; 95% confidence interval, 0.9049-0.9891; P = 0.0152) as a significant predictor. Our results affirmed the high efficacy of ECT in severe depression. No other established treatment for depression can report a response rate as high as 80% in a naturalistic study. This study supports the continued relevance and place of ECT for severe depression.

Gender identity and symptoms of anxiety and depression and their relationship with sleep disorders among Polish adolescents during the Covid-19 pandemic and the outbreak of war in the Ukraine.

For most people, gender identity is consistent with biological sex and such people are called cisgender. People in whom such a relationship does not occur or occurs to a lesser extent are referred to as gender non-conforming-and these include transgender, non-binary, agender and gender-fluid people. These individuals are usually affected by minority stress, which, combined with the circumstances of the pandemic and Russian aggression against Ukraine, may have led to mental disorders and sleep disorders in this population. The aim of the study was to analyze the symptoms of anxiety, depression and insomnia in a group of Polish youth during the Covid-19 pandemic and the outbreak of the war in Ukraine, taking into consideration gender differences, including non-binary gender. The study involved 1621 secondary school students aged 14 to 19, the average age was 16.73 ± 1.35, including 857 girls, 690 boys and 74 people who defined their gender as non-binary. A set of questionnaires for the Diagnosis of Depression in Children (CDI 2), the State-Trait Anxiety Inventory (STAI), the X-1 subscale, The Athens Insomnia Scale (AIS) and an original questionnaire of sociodemographic data were used in research. Analyzing the results obtained in the study group, the respondents scored an average of 17.99 ± 9.55 points in the assessment of depressive symptoms. After division into groups taking into account gender, the corresponding score was 19.69 ± 9.40 points for girls, 15.03 ± 8.68 for boys and 25.86 ± 9.91 for non-binary people. The difference was statistically significant (p = 0.000) in all three study groups. In the anxiety symptoms assessment, the respondents scored an average of 46.92 ± 11.67 points. After division into gender groups, the corresponding score was 49.21 ± 11.12 points for girls, 43.39 ± 11.47 for boys and 53.39 ± 10.41 for non-binary people. The difference was statistically significant (p = 0.000) in all three study groups. Analyzing the results obtained in The Athens Insomnia Scale (AIS), the average score was 8.31 ± 4.58 points, which allows to evaluate sleep onset as a norm. After dividing into groups, the results were 8.95 ± 4.55 points, respectively for girls, 7.19 ± 4.21 points for boys and 11.35 ± 5.43 for non-binary people, the difference was significant in terms of comparisons between all statistically tested groups. The analysis of the relationships between the studied variables was presented for each gender separately. Statistically significant positive correlations were found between the results obtained on the scale assessing depressive symptoms (CDI-2) and anxiety symptoms (STAI-X1) and the results obtained on the AIS scale. Among the studied group of teenagers, the highest intensity of depressive symptoms is demonstrated by non-binary people, followed by females, and finally by males. Similar results were obtained in the assessment of anxiety symptoms. The non-binary group achieved results indicating sleep disorders, while the cisgender group's results of sleep onset were borderline normal. Whatsmore, the greater the severity of depressive and anxiety disorders, the greater the sleep disorders in all study groups, regardless of gender.

The Impact of Previously Diagnosed Depression on Early and One-Year Mortality in Patients with Acute Ischemic Stroke in Opole Province, Poland.

Background: Depression is a known stroke risk factor, but its influence on stroke course depending on gender and age is not clearly defined. The purpose of this study was to determine the impact of previously diagnosed depression on early and one-year mortality in patients with acute ischemic stroke (IS) in relation to gender, age, and concomitant diseases. Methods: This study was based on the registry created from the public health insurer in Poland (2009-2020). Two groups were distinguished: IS-D-patients with IS and a diagnosis of depression within preceding 5 years (n = 520); IS-nD-patients with IS who had never been diagnosed with depression (n = 11,505). Results: In-hospital, 30-day, and 1-year mortality did not differ between groups (3.8% vs. 5.1%, 13.1% vs. 12.9%, and 27.1% vs. 26.8%, respectively). However, when statistical analysis was performed stratified by gender and age, we found a significantly higher 30-day and 1-year mortality in men under the age of 65 with previously diagnosed depression in comparison to those without depression (13.6% vs. 3.8%, p < 0.001; and 20.3% vs. 10.8, p < 0.021, respectively). Conclusions: The incidence of post-stroke depression was higher in IS-D group, regardless of gender and age. Early and one-year mortality was higher in younger men with IS and previously diagnosed depression.

Improving Depression Screening in Adult Patients With Cancer

Depression in adult patients with cancer may lead to decreased treatment adherence, decreased quality of life, and possible suicidal ideation. Adequate screening can promote timely diagnosis and treatment of depression. A quality improvement project was implemented at a cancer center in which adult patients are diagnosed with and treated for cancer. A paper version of the 9-item Patient Health Questionnaire (PHQ-9), a validated tool to screen for depression, was provided to patients during their scheduled appointment. A two-sample test of proportions was used to compare the proportion of patients screened before project implementation to the proportion of patients screened after project implementation. Depression screening rates increased from 2% before to 12% after project implementation. Frequent screening with the PHQ-9 should occur in adult patients with cancer to adequately identify depressive symptoms. Adequate screening will provide the necessary information for providers to make referrals to mental health services and allow patients to adhere to their treatment plans, improving their quality of life.

Real-World Use of Trazodone in Older Persons in Long Term Care Setting: A Retrospective Study.

Trazodone, an antidepressant drug is also largely used in several medical contexts. Insomnia, behavioral disorders, and anxiety may be underlying symptoms for prescribing trazodone. This cross-sectional study aims to identify reasons for trazodone prescription, assess the efficacy, as well as identify any related side effects in older persons living in long term care facilities (LTCFs). Older adults aged ≥ 60years, at risk of or affected with Covid-19 and enrolled in the GeroCovid Observational study from LTCFs, and using trazodone were included. A structured questionnaire was administered to treating physicians regarding reasons for trazodone prescription, discontinuation, possible adverse events and benefits. Thirty-seven out 74 LTCFs participating in both the GeroCovid and GeroCovid Vax studies completed the questionnaire regarding trazodone use. Of the 427 participants included in this study analysis, we found that 43% had diagnoses of dementia and depression, 33% had dementia, no behavioral and psychological symptoms of dementia (BPSD) and no depression, 14% had dementia with BPSD and no depression, and < 11% had only depression. The main reasons for trazodone prescription included agitation, insomnia, depression and anxiety. Trazodone use was reported as partially or totally effective in more than 90% of participants using the drug. Falls were the most frequent adverse event (30% of participants). Our data suggest that trazodone behaves as an eclectic antidepressant that, in the clinical practice, may also be used for BPSD and insomnia, especially in older people with dementia.