Screening For Major Depressive Disorder Research Articles

Cost-effectiveness of screening and treating alcohol use and depression among people living with HIV in Zimbabwe: a mathematical modeling study

BackgroundAlcohol use disorder (AUD) and major depressive disorder (MDD) drive HIV transmission in many sub-Saharan African settings. The impact of screening and treating AUD and MDD on HIV outcomes is unknown. We aimed to identify the cost-effectiveness of AUD and MDD interventions in Zimbabwe, and their potential contribution to reaching Zimbabwe’s Ending the HIV Epidemic 2030 goal.MethodsUsing a validated HIV compartmental transmission model in Zimbabwe, we compared four policy scenarios: prevention as usual (baseline); implement AUD screening (using AUDIT) and treatment (motivational interviewing and cognitive-behavioral therapy); implement MDD screening (using PHQ-9) and treatment (cognitive-behavioral therapy); and implement screening and treatment for both. Outcomes were HIV incidence projections, infections averted through 2030, quality-adjusted life-years gained, cost per infection averted, and cost per QALY gained. Analyses considered “spillover,” when treatment for AUD also results in an improvement in MDD and the converse. Sensitivity analyses identified cost reductions necessary for AUD and MDD interventions to be as cost-effective as other HIV interventions, particularly the scale-up of long-acting PrEP.ResultsAUD and MDD combined will be responsible for 21.1% of new HIV infections in Zimbabwe by 2030. Without considering spillover, compared to the baseline, MDD intervention can reduce new infections by 5.4% at $2039/infection averted and $3186/QALY. AUD intervention can reduce new infections by 5.8%, but at $2,968/infection averted and $4753/QALY, compared to baseline. Both MDD and AUD interventions can reduce new infections by 11.1% at $2810/infection averted and $4229/QALY, compared to baseline. Considering spillover, compared to the baseline, MDD intervention can reduce new infections by 6.4% at $1714/infection averted and $2630/QALY. AUD intervention can reduce new infections by 7.4%, but at $2299/infection averted and $3560/QALY compared to baseline. Both MDD and AUD interventions can reduce new infections by 11.9% at $2247/infection averted and $3382/QALY compared to baseline. For MDD intervention to match the cost-effectiveness of scaling long-acting PrEP, the cost of MDD intervention would need to be reduced from $16.64 to $12.88 per person.ConclusionsImplementing AUD and MDD interventions can play an important role in HIV reduction in Zimbabwe, particularly if intervention cost can be decreased while preserving effectiveness.

Identifying neuroimaging biomarkers in major depressive disorder using machine learning algorithms and functional near-infrared spectroscopy (fNIRS) during verbal fluency task

One of the most prevalent psychiatric disorders is major depressive disorder (MDD), which increases the probability of suicidal ideation or untimely demise. Abnormal frontal hemodynamic changes detected by functional near-infrared spectroscopy (fNIRS) during verbal fluency task (VFT) have the potential to be used as an objective indicator for assessing clinical symptoms. However, comprehensive quantitative and objective assessment instruments for individuals who exhibit symptoms suggestive of depression remain undeveloped. Drawing from a total of 467 samples in a large-scale dataset comprising 289 MDD patients and 178 healthy controls, fNIRS measurements were obtained throughout the VFT. To identify unique MDD biomarkers, this research introduced a data representation approach for extracting spatiotemporal features from fNIRS signals, which were subsequently utilized as potential predictors. Machine learning classifiers (e.g., Gradient Boosted Decision Trees (GBDT) and Multilayer Perceptron) were implemented to assess the ability to predict selected features. The mean and standard deviation of the cross-validation indicated that the GBDT model, when combined with the 180-feature pattern, distinguishes patients with MDD from healthy controls in the most effective manner. The accuracy of correct classification for the test set was 0.829 ± 0.053, with an AUC of 0.895 (95 % CI: 0.864–0.925) and a sensitivity of 0.914 ± 0.051. Channels that made the most important contribution to the identification of MDD were identified using Shapley Additive Explanations method, located in the frontopolar area and the dorsolateral prefrontal cortex, as well as pars triangularis Broca's area. Assessment of abnormal prefrontal activity during the VFT in MDD serves as an objectively measurable biomarker that could be utilized to evaluate cognitive deficits and facilitate early screening for MDD. The model suggested in this research could be applied to large-scale case-control fNIRS datasets to detect unique characteristics of MDD and offer clinicians an objective biomarker-based analytical instrument to assist in the evaluation of suspicious cases.

Assessing the associations of 1,400 blood metabolites with major depressive disorder: a Mendelian randomization study.

Major Depressive Disorder (MDD) is one of the most prevalent and debilitating health conditions worldwide. Previous studies have reported a link between metabolic dysregulation and MDD. However, evidence for a causal relationship between blood metabolites and MDD is lacking. Using a two-sample bidirectional Mendelian randomization analysis (MR), we assessed the causal relationship between 1,400 serum metabolites and Major Depressive Disorder (MDD). The Inverse Variance Weighted method (IVW) was employed to estimate the causal association between exposures and outcomes. Additionally, MR-Egger regression, weighted median, simple mode, and weighted mode methods were used as supplementary approaches for a comprehensive appraisal of the causality between blood metabolites and MDD. Pleiotropy and heterogeneity tests were also conducted. Lastly, the relevant metabolites were subjected to metabolite function analysis, and a reverse MR was implemented to explore the potential influence of MDD on these metabolites. After rigorous screening, we identified 34 known metabolites, 13 unknown metabolites, and 18 metabolite ratios associated with Major Depressive Disorder (MDD). Among all metabolites, 33 were found to have positive associations, and 32 had negative associations. The top five metabolites that increased the risk of MDD were the Arachidonate (20:4n6) to linoleate (18:2n6) ratio, LysoPE(18:0/0:0), N-acetyl-beta-alanine levels, Arachidonate (20:4n6) to oleate to vaccenate (18:1) ratio, Glutaminylglutamine, and Threonine to pyruvate ratio. Conversely, the top five metabolites that decreased the risk of MDD were N6-Acetyl-L-lysine, Oleoyl-linoleoyl-glycerol (18:1 to 18:2) [2] to linoleoyl-arachidonoyl-glycerol (18:2 to 20:4) [2] ratio, Methionine to phosphate ratio, Pregnanediol 3-O-glucuronide, and 6-Oxopiperidine-2-carboxylic acid. Metabolite function enrichment was primarily concentrated in pathways such as Bile Acid Biosynthesis (FDR=0.177), Glutathione Metabolism (FDR=0.177), Threonine, and 2-Oxobutanoate Degradation (FDR=0.177). In addition, enrichment was noted in pathways like Valine, Leucine, and Isoleucine Biosynthesis (p=0.04), as well as Ascorbate and Aldarate Metabolism (p=0.04). Within a pool of 1,400 blood metabolites, we identified 34 known metabolites and 13 unknown metabolites, as well as 18 metabolite ratios associated with Major Depressive Disorder (MDD). Additionally, three functionally enriched groups and two metabolic pathways were selected. The integration of genomics and metabolomics has provided significant insights for the screening and prevention of MDD.

Prevalence of major depressive disorder and post-traumatic stress disorder among first-time sleep center attendees

BackgroundSleep disorders and psychiatric disorders stand in a bidirectional relationship. Sleep complaints are prominent in populations with psychiatric disorders, especially amongst people with major depressive disorder (MDD) and post-traumatic stress disorder (PTSD). Consultations at sleep clinics offer opportunities to screen psychiatric disorders and to propose primary psychiatric care. MethodsThis descriptive study was conducted on 755 patients making their first visit to sleep clinic, with 574 seeking consultation for suspected obstructive sleep apnoea-hypopnoea syndrome (OSAHS), 139 for complaints of insomnia, and 42 for complaints of hypersomnia. The results of 387 screening scales for MDD (BDI-II) and 403 for TSPT (PCL-5) were compared according to the reason given for the consultation. ResultsIn the whole group, 12.1 % of patients presented a positive MDD screening and 4.9 % for PTSD. Among patients presenting with insomnia, 19.8 % had a positive screening for MDD, as compared to 9.3 % in patients presenting with suspected OSAHS (p = 0.02). Regarding PTSD, 9.7 % of patients seeking consultation because of insomnia had a positive screening, compared to 2.9 % among patients with suspected OSAHS (p = 0.03). Among patients with a positive screening for MDD, 40.5 % were not receiving antidepressant or mood stabilizer treatment. ConclusionPositive screening for MDD and PTSD are frequent in patients who attend sleep centers, especially amongst those presenting with insomnia. Nearly half of the patients with positive screening for MDD or PTSD were not receiving a dedicated pharmacological treatment. These figures emphasize systematic screening for psychiatric disorders in sleep clinics.

Health service and psychotropic medication use for mental health conditions among healthcare workers active during the Spain Covid-19 Pandemic – A prospective cohort study using web-based surveys.

Little is known about healthcare workers’ (HCW) use of healthcare services for mental disorders. This study presents data from a 16-month prospective cohort study of Spanish HCW (n = 4,809), recruited shortly after the COVID-19 pandemic onset, and assessed at four timepoints using web-based surveys. Use of health services among HCW with mental health conditions (i.e., those having a positive screen for mental disorders and/or suicidal thoughts and behaviours [STB]) was initially low (i.e., 18.2 %) but increased to 29.6 % at 16-month follow-up. Service use was positively associated with pre-pandemic mental health treatment (OR=1.99), a positive screen for major depressive disorder (OR=1.50), panic attacks (OR=1.74), suicidal thoughts and behaviours (OR=1.22), and experiencing severe role impairment (OR=1.33), and negatively associated with being female (OR = 0.69) and a higher daily number of work hours (OR=0.95). Around 30 % of HCW with mental health conditions used anxiolytics (benzodiazepines), especially medical doctors. Four out of ten HCW (39.0 %) with mental health conditions indicated a need for (additional) help, with most important barriers for service use being too ashamed, long waiting lists, and professional treatment not being available. Our findings delineate a clear mental health treatment gap among Spanish HCW.

Combining Glial Fibrillary Acidic Protein and Neurofilament Light Chain for the Diagnosis of Major Depressive Disorder.

Major depressive disorder (MDD) is a prevalent brain disorder affecting more than 2% of the world's population. Due to the lack of well-specific biomarkers, it is difficult to distinguish MDD from other diseases with similar clinical symptoms (such as Alzheimer's disease and cerebral thrombosis). In this work, we provided a strategy to address this issue by constructing a combinatorial biomarker of serum glial fibrillary acidic protein (GFAP) and neurofilament light chain (NFL). To achieve the convenient and sensitive detection of two proteins, we developed an electrochemical immunosandwich sensor using two metal-ion-doped carbon dots (Pb-CDs and Cu-CDs) as probes for signal output. Each probe contains approximately 300 Pb2+ or 200 Cu2+, providing excellent signal amplification. This method achieved detection limits of 0.3 pg mL-1 for GFAP and 0.2 pg mL-1 for NFL, lower than most of the reported detection limits. Analysis of real serum samples showed that the concentration ratio of GFAP to NFL, which is associated with the relative degree of brain inflammation and neurodegeneration, is suitable for not only distinguishing MDD from healthy individuals but also specifically distinguishing MDD from Alzheimer's disease and cerebral thrombosis. The good specificity gives the combinatorial GFAP/NFL biomarker broad application prospects in the screening, diagnosis, and treatment of MDD.

Privacy Preserving Collaboratively Training Framework for Classification of Major Depressive Disorder using Non-IID Three Channel Electroencephalogram

Major Depressive Disorder (MDD) is characterized by low mood, loss of interest and even suicidal ideation. Electroencephalogram based diagnosis of a variety of neurological conditions has been conducted in recent years using modern neurocomputing and deep learning approaches. Privacy is a high concern for medical data over distributed training scenarios. Hence, this paper aims to develop an EEG-based privacy-preserved system to identify MDD through the Federated Learning (FL) approach. In this study, training for 3-channel EEG-based MDD screening is introduced over FL using deep learning (DL) algorithms such as Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), Gated Recurrent Unit (GRU), and One Dimensional Convolutional Neural Network (1D-CNN). A privacy-preserving solution for MDD patients utilizing FL is advantageous because clinical data is sensitive and most people do not want to share their personal information. Hence, the analysis of the comparative results for multiple applicable strategies including Independent and Identically Disturbed (IID) data, non-IID data, and algorithms provide proof that FL can be used to train DL models. The MODMA dataset of 3-channel EEG with 26 MDD and 29 non-MDD individuals is used in this paper. Future study on MDD has several potential areas of use and these FL approaches can also be coupled with new technologies.

Detecting major depressive disorder presence using passively-collected wearable movement data in a nationally-representative sample

Major Depressive Disorder (MDD) is a heterogeneous disorder, resulting in challenges with early detection. However, changes in sleep and movement patterns may help improve detection. Thus, this study aimed to explore the utility of wrist-worn actigraphy data in combination with machine learning (ML) and deep learning techniques to detect MDD using a commonly used screening method: Patient Health Questionnaire-9 (PHQ-9). Participants (N = 8,378; MDD Screening = 766 participants) completed the and wore Actigraph GT3X+ for one week as part of the National Health and Nutrition Examination Survey (NHANES). Leveraging minute-level, actigraphy data, we evaluated the efficacy of two commonly used ML approaches and identified actigraphy-derived biomarkers indicative of MDD. We employed two ML modeling strategies: (1) a traditional ML approach with theory-driven feature derivation, and (2) a deep learning Convolutional Neural Network (CNN) approach, coupled with gramian angular field transformation. Findings revealed movement-related features to be the most influential in the traditional ML approach and nighttime movement to be the most influential in the CNN approach for detecting MDD. Using a large, nationally-representative sample, this study highlights the potential of using passively-collected, actigraphy data for understanding MDD to better improve diagnosing and treating MDD.

The Kessler Psychological Distress Scale in Australian adolescents: Analysis of the second Australian Child and Adolescent Survey of Mental Health and Wellbeing.

The 10-item Kessler Psychological Distress Scale (K10) is used to screen adolescents for mental disorders in Australian clinical practice; however, there are no Australian adolescent normative data. Data were drawn from a nationally representative sample (N = 2964) of Australian adolescents (11-17 years). This study had three aims: (1) to examine concurrent validity between the K10 and Strengths and Difficulties Questionnaire (SDQ) emotional symptoms subscale, (2) to establish normative Australian adolescent K10 data and (3) to determine optimal K10 cut-off scores for screening for major depressive disorder (MDD) via receiver operator characteristic curve analysis and stratum-specific likelihood ratios. The K10 and SDQ emotional symptoms scales were moderately correlated (rs = 0.63, p < 0.001). Older female adolescents reported higher total K10 scores compared with younger female adolescents (15-17 years: M = 20.2, standard error [SE] = 0.3; 11-14 years: M = 16.8, SE = 0.3) and male adolescents (11-14 years: M = 16.6, SE = 0.2; 15-17 years: M = 16.0, SE=0.2). K10 scores to optimally discriminate those with and without MDD varied by age and sex and had low specificities. Stratum-specific likelihood ratios indicated adolescents with a K10 score of ≥30 will have a 12.9 (95% confidence interval = [10.2, 16.2]) increased likelihood of MDD. The K10 has utility for assessing psychological distress in health care and epidemiological research in Australian adolescents. Adolescents with K10 scores in the 'very high' range are at increased risk of MDD. Further assessment of these young people is indicated to identify those with or at risk of developing MDD.

Psychological Status of Healthcare Professionals of Bangladesh during the COVID-19 Pandemic

Background: The wellbeing of Healthcare professionals (HPs) across the world had been severely affected by the COVID-19 pandemic. Previous researches on other similar infectious diseases consistently showed that many HPs reported symptoms of anxiety and depression, both during and after the outbreak, causing a severe impact on their coping abilities, in some cases with long-lasting effects. Objectives: The present study was designed to find out the impact on mental health of the HPs in Bangladesh during COVID-19 pandemic. Methodology: This survey-based cross-sectional study was carried out in Combined Military Hospital, Bogura, a tertiary hospital in Bangladesh. The study was conducted during 15-20 August, 2020, including 303 HPs during the COVID-19 pandemic. Thestudy incorporated the patient health questionnaire (PHQ-2) and generalized health questionnaire (GAD-2), validated for screening of Major Depressive Disorder (MDD) and Generalized Anxiety Disorder (GAD) respectively. Results: Among the respondents, majority were males (201, 66.34%), between 20-29 years of age (127, 41.92%), married (244, 80.53%), fertile (205, 67.66%) and health assistants (151, 49.83%). Some 137(45.21%) respondents were involved directly to treat diagnosed cases of COVID-19 and 20(06.60%) had been suffering from insomnia. A total of 73(24.09%) HPs remained in anxiety every time that their family members may be affected by COVID-19 through them. The MDD was found in 77(25.41%) respondents and GAD in 42(13.86%). The MDD was found more (25.40% versus 23.53%) in the HPs who were staying without family, butGAD (21.57% versus 12.30%) and Anxiety to infect family members through them was remarkably higher (49.10% versus 19.05%) in the HPs who stayed with family. Conclusion: The present survey showed that a remarkable number of HPs experienced anxiety and depression due to prevailing COVID-19 pandemic Journal of Monno Medical College, June 2023; 9 (1): 15-19

Validity of the Spanish-Language Patient Health Questionnaires 2 and 9

Reliable screening for major depressive disorder (MDD) relies on valid and accurate screening tools. To examine the validity, accuracy, and reliability of the Spanish-language Patient Health Questionnaires 2 and 9 (PHQ-2 and PHQ-9) to screen for MDD. PubMed, Web of Science, Embase, and PsycINFO from data initiation through February 27, 2023. English- and Spanish-language studies evaluating the validity of the Spanish-language PHQ-2 or PHQ-9 in screening adults for MDD compared with a standardized clinical interview (gold standard). Search terms included PHQ-2, PHQ-9, depression, and Spanish. Two reviewers performed abstract and full-text reviews, data extraction, and quality assessment. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. Random-effects meta-analyses of sensitivity, specificity, and area under the curve (AUC) were performed. Internal consistency was evaluated using Cronbach α and McDonald ψ. Test accuracy and internal consistency. The PHQ-2 is composed of the first 2 questions of the PHQ-9 (targeting core depression symptoms of depressed mood and anhedonia; a score of 3 or higher (score range, 0-6) is generally considered a positive depression screen. If a patient screens positive with the PHQ-2, a follow-up assessment with the PHQ-9 and a clinical diagnostic evaluation are recommended. Once depression is diagnosed, a PHQ-9 score of 10 or higher (score range, 0-27) is often considered an acceptable threshold for treating depression. Ten cross-sectional studies involving 5164 Spanish-speaking adults (mean age range, 34.1-71.8 years) were included; most studies (n = 8) were in primary care settings. One study evaluated the PHQ-2, 7 evaluated the PHQ-9, and 2 evaluated both the PHQ-2 and PHQ-9. For the PHQ-2, optimal cutoff scores ranged from greater than or equal to 1 to greater than or equal to 2, with an overall pooled sensitivity of 0.89 (95% CI, 0.81-0.95), overall pooled specificity of 0.89 (95% CI, 0.81-0.95), and overall pooled AUC of 0.87 (95% CI, 0.83-0.90); Cronbach α was 0.71 to 0.75, and McDonald ψ was 0.71. For the PHQ-9, optimal cutoff scores ranged from greater than or equal to 5 to greater than or equal to 12, with an overall pooled sensitivity of 0.86 (95% CI, 0.82-0.90), overall pooled specificity of 0.80 (95% CI, 0.75-0.85), and overall pooled AUC of 0.88 (95% CI, 0.87-0.90); Cronbach α was 0.78 to 0.90, and McDonald ψ was 0.79 to 0.90. Four studies were considered to have low risk of bias; 6 studies had indeterminate risk of bias due to a lack of blinding information. In this systematic review and meta-analysis, limited available evidence supported the use of the Spanish-language PHQ-2 and PHQ-9 in screening for MDD, but optimal cutoff scores varied greatly across studies, and few studies reported on blinding schemes. These results suggest that MDD should be considered in Spanish-speaking individuals with lower test scores. Given the widespread clinical use of the tools and the heterogeneity of existing evidence, further investigation is needed.

The validity of the Edmonton Symptom Assessment System-Depression item for screening for depression in individuals with cancer pain: A cross-sectional study.

Depression is common in individuals with cancer and pain, negatively impacts quality of life, treatment adherence, tumor progression, and survival. The primary aims of this study were to (1) evaluate the validity of the Edmonton Symptom Assessment System's depression (ESAS-D) for detecting major depressive disorder (MDD) as diagnosed by a psychiatrist and (2) identify the best cutoff for this purpose in a sample of cancer pain individuals. The secondary aim was to compare ESAS-D with another commonly used screening measure (Patient Health Questionnaire-2 [PHQ-2]) for classifying individuals as meeting or not meeting Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) criteria for depression. 49 cancer pain individuals completed the ESAS-D and PHQ-2 Within 2 weeks, a psychiatrist interviewed the participants and determined whether or not they met criteria for MDD based on the DSM-5. The ESAS-D demonstrated acceptable accuracy and validity for classifying MDD. A cutoff of ≥2 was identified as being best able to balance sensitivity (85%) and specificity (76%) and had an overall accuracy of 79%. A receiver operating characteristic curve analysis showed an area under the curve (AUC) of 0.81 (95% confidence interval [CI]: 0.68-0.94). The ESAS-D also compared favorably with the modified Thai PHQ-2 (sensitivity, 75%; specificity, 72%; overall accuracy, 73%; AUC, 0.74 [95% CI: 0.59-0.88]) for identifying MDD individuals. The ESAS-D showed acceptable sensitivity, specificity, and overall accuracy for screening for MDD in cancer and pain. It could therefore be used to screen for probable depression in this population.

A novel tool for improving the accuracy of major depressive disorder screening: A prospective study on andrology with external validation

Patient Health Questionnaire-9 (PHQ-9) is the most widely used tool for screening for major depressive disorder (MDD). Although its reliability and validity have been proven, missed or misjudged cases during MDD screening are often encountered. A nomogram that considers the weights of depressive symptoms was developed using data from premature ejaculation patients to improve screening accuracy. During a 33-month prospective study, a training cohort comprising 605 participants from Xijing Hospital was used to develop and internally validate the nomogram. A validation cohort comprising 461 patients from Xi'an Daxing Hospital was also used to externally test the nomogram. The nomogram was established by integrating the LASSO regression-based optimal predictors of MDD according to their coefficients in a multivariate logistic regression model. The nomogram was well-calibrated during internal and external validations. Moreover, it showed a better discriminatory capacity and yielded more net benefits in both validations than PHQ-9. With better performance, the nomogram may help reduce the number of missed or misjudged cases during MDD screening. This study is the first to weigh the direct indicators of MDD under the DSM-5 criteria, presenting a fresh concept that can be applied to other populations to enhance screening accuracy.

Identification of major depression patients using machine learning models based on heart rate variability during sleep stages for pre-hospital screening

With the COVID-19 pandemic causing challenges in hospital admissions globally, the role of home health monitoring in aiding the diagnosis of mental health disorders has become increasingly important. This paper proposes an interpretable machine learning solution to optimise initial screening for major depressive disorder (MDD) in both male and female patients. The data is from the Stanford Technical Analysis and Sleep Genome Study (STAGES). We analyzed 5-min short-term electrocardiogram (ECG) signals during nighttime sleep stages of 40 MDD patients and 40 healthy controls, with a 1:1 gender ratio. After preprocessing, we calculated the time-frequency parameters of heart rate variability (HRV) based on the ECG signals and used common machine learning algorithms for classification, along with feature importance analysis for global decision analysis. Ultimately, the Bayesian optimised extremely randomized trees classifier (BO-ERTC) showed the best performance on this dataset (accuracy 86.32%, specificity 86.49%, sensitivity 85.85%, F1-score 0.86). By using feature importance analysis on the cases confirmed by BO-ERTC, we found that gender is one of the most important factors affecting the prediction of the model, which should not be overlooked in our assisted diagnosis. This method can be embedded in portable ECG monitoring systems and is consistent with the literature results.

A Machine Learning Framework for Major Depressive Disorder (MDD) Detection Using Non-invasive EEG Signals

According to World Health Organization (WHO) report, every 40 seconds a person attempts suicide globally. Depression, one of the world’s most prevailing diseases has become a reason behind these suicides. It is believed that early diagnosis of major depressive disorder (MDD) can reduce the adversity of this heinous deformity. For few years various machine learning and advanced neurocomputing techniques are being utilized in Electroencephalogram (EEG) based detection of multiple neurological diseases. In the proposed study, an EEG based screening of MDD is presented while using various Machine Learning and one Deep Learning approach. The majority of previous EEG based MDD decoding research has concentrated on a limited features. It was necessary to conduct in-depth comparisons of different approaches, besides more detailed feature-based EEG analysis. This research starts with the creation of a complete feature-based framework, which is then further compared against the state of the art end to end techniques. The K-nearest neighbors (KNN) model outperformed the other models and gained an accuracy of 87.5%. While long short term memory (LSTM) model acquired an accuracy of 83.3%. This study can further support in clinical diagnosis of multiple stages of MDD and can attempt to provide an early intervention.

Screening of Major Depression Disorder in Patients With Epilepsy in Indonesian National Referral Hospital.

Major depressive disorder (MDD) is a common psychiatric disorder in patients with epilepsy (PWE). The Neurological Disorders Depression Inventory for Epilepsy (NDDI-E) is one of the MDD screening tools used in PWE. This study aims to determine the accuracy of the valid and reliable NDDI-E Indonesian version as an MDD screening tool in PWE and investigate the prevalence and risk factors for the development of MDD in PWE. A diagnostic cross-sectional study was conducted at Cipto Mangunkusumo National Referral Hospital, Indonesia. Patients were PWE aged 18 years or older who visited the epilepsy outpatient clinic. The valid and reliable NDDI-E Indonesian version and Mini International Neuropsychiatric Interview (MINI) International Classification of Disease, 10th Revision (ICD-10) were used to diagnose MDD. In phase II of the study, diagnostic accuracy was evaluated using the receiver operative characteristic (ROC) curve method to obtain the area under the curve (AUC) and diagnostic 2 x 2 table to determine the cutoff point, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). During phase III of the study, eligible individuals were screened for MDD using the NDDI-E Indonesian version. Demographic and clinical data were collected. Data analysis was performed using the χ2 test, Mann-Whitney test, and multivariate logistic regression analysis. A total of 105 individuals were involved, and only 23 of them were found to experience MDD based on MINI ICD-10. The best cutoff point for the NDDI-E Indonesian version was ≥11, with a sensitivity of 91.3%, specificity 89%, PPV 70%, and NPV 97.3%. The AUC obtained from ROC analysis was 97.5% (95% CI 95-99%, p < 0.001). Then, the survey was completed by 79 individuals, predominantly male, mostly within the age range of 26-45 years. The prevalence of MDD in PWE was 50.6%, and the significant risk factors were seizure frequency ≥8 times a year and the presence of chronic diseases (p < 0.001). The NDDI-E Indonesian version was a screening tool with a high diagnostic accuracy to detect MDD in PWE at a cutoff point of 11. Poor seizure control and the presence of other chronic diseases were the risk factors correlated with MDD development in PWE.

Screening for Major Depressive Disorder Using a Wearable Ultra-Short-Term HRV Monitor and Signal Quality Indices.

To encourage potential major depressive disorder (MDD) patients to attend diagnostic sessions, we developed a novel MDD screening system based on sleep-induced autonomic nervous responses. The proposed method only requires a wristwatch device to be worn for 24 h. We evaluated heart rate variability (HRV) via wrist photoplethysmography (PPG). However, previous studies have indicated that HRV measurements obtained using wearable devices are susceptible to motion artifacts. We propose a novel method to improve screening accuracy by removing unreliable HRV data (identified on the basis of signal quality indices (SQIs) obtained by PPG sensors). The proposed algorithm enables real-time calculation of signal quality indices in the frequency domain (SQI-FD). A clinical study conducted at Maynds Tower Mental Clinic enrolled 40 MDD patients (mean age, 37.5 ± 8.8 years) diagnosed on the basis of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, and 29 healthy volunteers (mean age, 31.9 ± 13.0 years). Acceleration data were used to identify sleep states, and a linear classification model was trained and tested using HRV and pulse rate data. Ten-fold cross-validation showed a sensitivity of 87.3% (80.3% without SQI-FD data) and specificity of 84.0% (73.3% without SQI-FD data). Thus, SQI-FD drastically improved sensitivity and specificity.

Triagem positiva para transtorno depressivo maior em gestantes de alto risco

ABSTRACT Objective: To estimate the frequency of positive screening for major depressive disorder and associated factors in high-risk pregnant women at a reference hospital of the Brazilian Public Health System. Methods: Cross-sectional study with 184 high-risk pregnant women at the Maternity at the Hospital Regional de São José , SC, Brazil. Positive screening for major depressive disorder using the Edinburgh Postpartum Depression Scale was selected as the dependent variable. Socio-demographic and pregnancy-related variables were also collected. Data were analyzed using Poisson regression with a robust estimator, including all variables that presented a p-value &lt; 0.20 in the bivariate analysis. Statistically significant differences were considered when p ≤ 0.05. Results: The frequency of positive screening for major depressive disorder was 37.5%. Non-white skin color, income of less than USD 572,56 per month and maternal age of less than 18 years or greater than or equal to 35 years were statistically and independently associated with positive screening for major depressive disorder in high-risk pregnant women. Conclusion: The frequency of positive screening for major depressive disorder in the high-risk pregnant women studied was 37.5%. The frequency was statistically associated with skin color, family income and extremes in the maternal age.

Abstract P3-03-08: Depression and breast cancer recurrence among female veterans in the United States: a retrospective cohort study

Abstract Background: The importance of mental health to improve the quality of cancer survivorship and outcomes is gaining increased attention. Major depressive disorder (MDD) has been associated with higher breast cancer (BC) incidence, BC-specific and all-cause mortality. The effect of MDD on BC recurrence remains under-studied. The Veterans Health Administration (VHA) provides a unique opportunity to examine this relationship as it mandates annual MDD screening in the primary care setting and reports national screening rates of 96%. Objective: To examine the relationship between pre-existing MDD and BC recurrence among a diverse cohort of women in the United States. Methods: We established a retrospective cohort of women (age ≥ 18 years) diagnosed with early-stage incident BC from 2010 through 2019 using the VHA’s electronic medical record database. We used a two-year window to identify women with clinically diagnosed MDD prior to their BC diagnosis. Our outcome of interest was BC recurrence (local recurrence/second primary and distant recurrence). We used multivariable proportional hazards regression to examine the association between MDD and BC recurrence, accounting for competing risk of death. Our analyses were adjusted for age, race, ethnicity, marital status, priority group rating (proxy for socioeconomic status), rurality of primary residence, geographical region, cancer stage, year of BC diagnosis, and comorbidity burden. Priority group rating is a metric that incorporates patients’ income, receipt of VHA assistance benefits, capacity for gainful employment, and severity of service-connected conditions. We conducted subgroup analyses by tumor subtype. RESULTS: Our cohort consists of 6,045 women with BC, of whom 1,750 (29.0%) had a pre-existing MDD diagnosis. The median length of follow-up from BC diagnosis was 4.5 years (IQR=4.8 years). The average age at BC diagnosis was 57 years (SD=11 years) overall and 56 years (SD=10 years) among women with MDD. Sixty-one percent of women were white, 29% were black, 27% were married, and 28% were rural dwelling. Forty-four percent of invasive carcinomas were localized and 21% were regional. Thirty-five percent of patients had ductal carcinoma in situ. Women with MDD had a higher average Charlson Comorbidity Index score (mean=0.75, SD=1.13 vs. mean=0.58, SD=1.03) and proportion with a priority group rating between 1-4 (40% vs. 32%). The distribution of other described patient characteristics among women with MDD were similar to the overall cohort. On multivariable analysis, women with MDD had a 31% (95% CI: 1.11, 1.56) higher risk of BC recurrence compared to women without MDD. MDD was associated with a 29% (95% CI: 1.01, 1.66) and 33% (95% CI: 1.05, 1.67) higher risk of local recurrence/second primary and distant recurrence, respectively. The relationship between MDD and BC recurrence remained significant after adjusting for tumor subtype (HR=1.40, 95% CI: 1.07, 1.83). In subgroup analyses stratified by tumor subtype, MDD was associated with a higher risk of BC recurrence among women with estrogen receptor positive versus negative tumors (HR=1.65, 95% CI: 1.27, 2.14 vs. HR=0.71, 95% CI: 0.34, 1.46) and HER2/neu negative versus positive tumors (HR=1.53, 95% CI: 1.16, 2.00 vs. HR=1.24, 95% CI: 0.51, 2.96). The interaction between estrogen receptor status and MDD was statistically significant (p-interaction=0.02). CONCLUSION: Women with incident BC should be screened for MDD early in the cancer care continuum given the prevalence of MDD and association with increased risk of local recurrence/second primary and distant BC recurrence. Further research is needed to understand the interplay between these two diseases and their treatments. Citation Format: Maya Aboumrad, Brian Shiner, Avonne Connor, Bradley V. Watts, Kala Visvanathan. Depression and breast cancer recurrence among female veterans in the United States: a retrospective cohort study [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P3-03-08.

P869 Prevalence of Depression and Anxiety before and during the COVID-19 Pandemic in Puerto Ricans with Inflammatory Bowel Disease

Abstract Background A higher rate of anxiety and depression is present in patients with inflammatory bowel disease (IBD). The COVID-19 pandemic has negatively impacted mental health in the general population. This study aims to compare the prevalence of depression and anxiety in patients with IBD during the pre-pandemic period and the pandemic period, and to explore trends in patients' specific IBD-related needs during the pandemic era. Methods Data was collected from subjects with IBD in two different timepoints: 2016 and 2021-2022. Self-administered questionnaires were given to adults with confirmed IBD attending an academic IBD Clinic. Data collected included demographic information, Patient Health Questionnaire-9 (PHQ-9) for major depressive disorder (MDD), General Anxiety Disorder-7 (GAD-7) questionnaire and subject’s need for referrals to other services such as nutrition, pain management, mental health services. Scores of 10 or more in the GAD-7 or PHQ-9 indicate need for professional evaluation. Statistical analysis was performed using R Studio v.4.2.1. This study is approved by the UPR MSC-IRB. Results Two hundred-fifty-two questionnaires were collected in 2016 and 196 during the 2021-22 pandemic period. The majority of subjects were male (54.6%) and had Crohn’s disease (69.9%). Mean ± SD age was 40.2 ± 14.6. In the PHQ-9, 33.7% (n=91) during 2016, and 16.33% (n=32) during 2021-22 screened positive for MDD. For the GAD-7, 30.7% (n=83) during 2016, and 12.76% (n=25) during 2021-22 screened positive for generalized anxiety disorder (GAD). Sex and age differences were not detected for MDD and GAD screening, with exception for age differences in MDD during 2021-2022. The most frequent needs for referrals during 2021-2022 were nutrition evaluation (n = 74), access to IBD education (n = 73), psychology services (n = 71) and participation in research for IBD studies (n = 66) Conclusion The frequency of depression and anxiety in Puerto Ricans with IBD during the pandemic period was lower than pre-pandemic. Possible factors related to this include active use of telemedicine providing regular follow-up, integrated psychological support as part of the regular clinic services, and access to information regarding COVID-19 and IBD.