Ketamine versus ECT for Nonpsychotic Treatment-Resistant Major Depression.

BACKGROUND: Electroconvulsive therapy (ECT) and subanesthetic intravenous ketamine are both currently used for treatment-resistant major depression, but the comparative effectiveness of the two treatments remains uncertain. METHODS: We conducted an open-label, randomized, noninferiority trial involving patients referred to ECT clinics for treatment-resistant major depression. Patients with treatment-resistant major depression without psychosis were recruited and assigned in a 1:1 ratio to receive ketamine or ECT. During an initial 3-week treatment phase, patients received either ECT three times per week or ketamine (0.5 mg per kilogram of body weight over 40 minutes) twice per week. The primary outcome was a response to treatment (i.e., a decrease of ≥50% from baseline in the score on the 16-item Quick Inventory of Depressive Symptomatology-Self-Report; scores range from 0 to 27, with higher scores indicating greater depression). The noninferiority margin was -10 percentage points. Secondary outcomes included scores on memory tests and patient-reported quality of life. After the initial treatment phase, the patients who had a response were followed over a 6-month period. RESULTS: A total of 403 patients underwent randomization at five clinical sites; 200 patients were assigned to the ketamine group and 203 to the ECT group. After 38 patients had withdrawn before initiation of the assigned treatment, ketamine was administered to 195 patients and ECT to 170 patients. A total of 55.4% of the patients in the ketamine group and 41.2% of those in the ECT group had a response (difference, 14.2 percentage points; 95% confidence interval, 3.9 to 24.2; P<0.001 for the noninferiority of ketamine to ECT). ECT appeared to be associated with a decrease in memory recall after 3 weeks of treatment (mean [±SE] decrease in the T-score for delayed recall on the Hopkins Verbal Learning Test-Revised, -0.9±1.1 in the ketamine group vs. -9.7±1.2 in the ECT group; scores range from -300 to 200, with higher scores indicating better function) with gradual recovery during follow-up. Improvement in patient-reported quality-of-life was similar in the two trial groups. ECT was associated with musculoskeletal adverse effects, whereas ketamine was associated with dissociation. CONCLUSIONS: Ketamine was noninferior to ECT as therapy for treatment-resistant major depression without psychosis. (Funded by the Patient-Centered Outcomes Research Institute; ELEKT-D ClinicalTrials.gov number, NCT03113968.).

Extracellular vesicle biomarkers for complement dysfunction in schizophrenia.

Schizophrenia, a complex neuropsychiatric disorder, frequently experiences a high rate of misdiagnosis due to subjective symptom assessment. Consequently, there is an urgent need for innovative and objective diagnostic tools. In this study, we used cutting-edge extracellular vesicles' (EVs) proteome profiling and XGBoost-based machine learning to develop new markers and personalized discrimination scores for schizophrenia diagnosis and prediction of treatment response. We analysed plasma and plasma-derived EVs from 343 participants, including 100 individuals with chronic schizophrenia, 34 first-episode and drug-naïve patients, 35 individuals with bipolar disorder, 25 individuals with major depressive disorder and 149 age- and sex-matched healthy controls. Our innovative approach uncovered EVs-based complement changes in patients, specific to their disease-type and status. The EV-based biomarkers outperformed their plasma counterparts, accurately distinguishing schizophrenia individuals from healthy controls with an area under curve (AUC) of 0.895, 83.5% accuracy, 85.3% sensitivity and 82.0% specificity. Moreover, they effectively differentiated schizophrenia from bipolar disorder and major depressive disorder, with AUCs of 0.966 and 0.893, respectively. The personalized discrimination scores provided a personalized diagnostic index for schizophrenia and exhibited a significant association with patients' antipsychotic treatment response in the follow-up cohort. Overall, our study represents a significant advancement in the field of neuropsychiatric disorders, demonstrating the potential of EV-based biomarkers in guiding personalized diagnosis and treatment of schizophrenia.

Quality Indicators for Major Depressive Disorder in Adults: A Review of Performance Measures by the American College of Physicians.

Major depressive disorder (MDD) is a severe mood disorder that affects at least 8.4% of the adult population in the United States. Characteristics of MDD include persistent sadness, diminished interest in daily activities, and a state of hopelessness. The illness may progress quickly and have devastating consequences if left untreated. Eight performance measures are available to evaluate screening, diagnosis, and successful management of MDD. However, many performance measures do not meet the criteria for validity, reliability, evidence, and meaningfulness.The American College of Physicians (ACP) embraces performance measurement as a means to externally validate the quality of care of practices, medical groups, and health plans and to drive reimbursement processes. However, a plethora of performance measures that provide low or no value to patient care have inundated physicians, practices, and systems and burdened them with collecting and reporting of data. The ACP's Performance Measurement Committee (PMC) reviews performance measures using a validated process to inform regulatory and accreditation bodies in an effort to recognize high-quality performance measures, address gaps and areas for improvement in performance measures, and help reduce reporting burden. Out of 8 performance measures, the PMC found only 1 measure (suicide risk assessment) that was valid at all levels of attribution. This paper presents a review of MDD performance measures and highlights opportunities to improve performance measures addressing MDD management.

Recognizing depression as an inflammatory disease: the search for endotypes.

Major depressive disorder (MDD) affects millions of individuals worldwide, leading to considerable social and economic costs. Despite advancements in pharmacological treatments, achieving remission remains a key challenge, with a substantial number of patients showing resistance to existing therapies. This resistance is often associated with elevated levels of proinflammatory cytokines, suggesting a connection between inflammation, MDD pathophysiology, and treatment efficacy. The observation of increased immune activation in about a quarter of patients with MDD resulted in the distinction between inflammatory and noninflammatory endotypes. Although anti-inflammatory treatments show promise in alleviating depression-like symptoms, responses are heterogeneous, thus highlighting the importance of identifying distinct inflammatory endotypes to tailor effective therapeutic strategies. The intestinal microbiome emerges as a crucial modulator of mental health, mediating its effects partially through different immune pathways. Microbiota-derived short-chain fatty acids (SCFAs) significantly impact innate and adaptive immune cells, regulating their differentiation, function, and cellular response. Furthermore, gut-educated immune cells reach the border regions of the central nervous system (CNS), regulating glial cell functions. Although the CNS modulates immune responses via efferent parts of the vagus nerve, afferent tracts concurrently transport information on peripheral inflammation back to the brain. This bidirectional communication is particularly relevant in depression, allowing for therapeutic stimulation of the vagus nerve in the context of inflammatory depression endotypes. In this review, we explore the intricate relationship between inflammation and depression, discuss how inflammatory signals are translated into depressive-like symptoms, and highlight immune-modulating therapeutic avenues.

Evaluation of a Nondepleted Plasma Multiprotein-Based Model for Discriminating Psychiatric Disorders Using Multiple Reaction Monitoring-Mass Spectrometry: Proof-of-Concept Study.

Psychiatric evaluation relies on subjective symptoms and behavioral observation, which sometimes leads to misdiagnosis. Despite previous efforts to utilize plasma proteins as objective markers, the depletion method is time-consuming. Therefore, this study aimed to enhance previous quantification methods and construct objective discriminative models for major psychiatric disorders using nondepleted plasma. Multiple reaction monitoring-mass spectrometry (MRM-MS) assays for quantifying 453 peptides in nondepleted plasma from 132 individuals [35 major depressive disorder (MDD), 47 bipolar disorder (BD), 23 schizophrenia (SCZ) patients, and 27 healthy controls (HC)] were developed. Pairwise discriminative models for MDD, BD, and SCZ, and a discriminative model between patients and HC were constructed by machine learning approaches. In addition, the proteins from nondepleted plasma-based discriminative models were compared with previously developed depleted plasma-based discriminative models. Discriminative models for MDD versus BD, BD versus SCZ, MDD versus SCZ, and patients versus HC were constructed with 11 to 13 proteins and showed reasonable performances (AUROC = 0.890-0.955). Most of the shared proteins between nondepleted and depleted plasma models had consistent directions of expression levels and were associated with neural signaling, inflammatory, and lipid metabolism pathways. These results suggest that multiprotein markers from nondepleted plasma have a potential role in psychiatric evaluation.

BPI-GNN: Interpretable brain network-based psychiatric diagnosis and subtyping.

Converging evidence increasingly suggests that psychiatric disorders, such as major depressive disorder (MDD) and autism spectrum disorder (ASD), are not unitary diseases, but rather heterogeneous syndromes that involve diverse, co-occurring symptoms and divergent responses to treatment. This clinical heterogeneity has hindered the progress of precision diagnosis and treatment effectiveness in psychiatric disorders. In this study, we propose BPI-GNN, a new interpretable graph neural network (GNN) framework for analyzing functional magnetic resonance images (fMRI), by leveraging the famed prototype learning. In addition, we introduce a novel generation process of prototype subgraph to discover essential edges of distinct prototypes and employ total correlation (TC) to ensure the independence of distinct prototype subgraph patterns. BPI-GNN can effectively discriminate psychiatric patients and healthy controls (HC), and identify biological meaningful subtypes of psychiatric disorders. We evaluate the performance of BPI-GNN against 11 popular brain network classification methods on three psychiatric datasets and observe that our BPI-GNN always achieves the highest diagnosis accuracy. More importantly, we examine differences in clinical symptom profiles and gene expression profiles among identified subtypes and observe that our identified brain-based subtypes have the clinical relevance. It also discovers the subtype biomarkers that align with current neuro-scientific knowledge.

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.

Cardiac autonomic function is preserved in young adults with major depressive disorder.

The prevalence of major depressive disorder (MDD) is highest in young adults and contributes to an increased risk of developing future cardiovascular disease (CVD). However, the underlying mechanisms remain unclear. The studies examining cardiac autonomic function that have included young unmedicated adults with MDD report equivocal findings, and few have considered the potential influence of disease severity or duration. We hypothesized that heart rate variability (HRV) and cardiac baroreflex sensitivity (BRS) would be reduced in young unmedicated adults with MDD (18-30 yr old) compared with healthy nondepressed young adults (HA). We further hypothesized that greater symptom severity would be related to poorer cardiac autonomic function in young adults with MDD. Heart rate and beat-to-beat blood pressure were continuously recorded during 10 min of supine rest to assess HRV and cardiac BRS in 28 HA (17 female, 22 ± 3 yr old) and 37 adults with MDD experiencing current symptoms of mild-to-moderate severity (unmedicated; 28 female, 20 ± 3 yr old). Neither HRV [root mean square of successive differences between normal heartbeats (RMSSD): 63 ± 34 HA vs. 79 ± 36 ms MDD; P = 0.14] nor cardiac BRS (overall gain, 21 ± 10 HA vs. 23 ± 7 ms/mmHg MDD; P = 0.59) were different between groups. In young adults with MDD, there was no association between current depressive symptom severity and either HRV (RMSSD, R2 = 0.004, P = 0.73) or cardiac BRS (overall gain, R2 = 0.02, P = 0.85). Taken together, these data suggest that cardiac autonomic dysfunction may not contribute to elevated cardiovascular risk factor profiles in young unmedicated adults with MDD of mild-to-moderate severity.NEW & NOTEWORTHY This study investigated cardiac autonomic function in young unmedicated adults with major depressive disorder (MDD). The results demonstrated that both heart rate variability and cardiac baroreflex sensitivity were preserved in young unmedicated adults with MDD compared with healthy nondepressed young adults. Furthermore, in young adults with MDD, current depressive symptom severity was not associated with any indices of cardiac autonomic function.

Gender Differences in the Prevalence and Clinical Correlates of Metabolic Syndrome in First-Episode and Drug-Naïve Patients With Major Depressive Disorder.

OBJECTIVE: Major depressive disorder (MDD) is a severe psychiatric symptom worldwide, and the coexistence of MDD with metabolic syndrome (MetS) is common in clinical practice. However, gender differences in comorbid MetS in first-episode and drug-naïve (FEDN) MDD patients have not been reported. Here, we explored potential gender differences in the prevalence and clinical correlates of comorbid MetS in FEDN MDD patients. METHODS: A cross-sectional study of 1718 FEDN MDD patients was conducted. Demographic and clinical data were collected. The Hamilton Depression Scale (HAMD), Hamilton Anxiety Scale, and Positive and Negative Syndrome Scale positive subscale were used to evaluate depression, anxiety, and psychotic symptoms, respectively. RESULTS: The prevalence of MetS was 1.645-fold higher in female MDD patients (38.50%) than in male patients (26.53%). Patients with MetS had higher HAMD score, Hamilton Anxiety Scale score, and Positive and Negative Syndrome Scale positive subscale score than patients without MetS (p values < .001). Furthermore, suicide attempts (male: odds ratio [OR] = 1.706, p = .034; female: OR = 1.639, p = .004) and HAMD score (male: OR = 1.251, p < .001; female: OR = 1.148, p < .001) were independently associated with MetS in male and female patients, whereas age of onset was independently associated with MetS only in female patients (OR = 1.744, p = .047). CONCLUSIONS: Our findings suggest significant gender differences in the prevalence and clinical correlates of comorbid MetS in FEDN MDD patients. Clinical variables (suicide attempts and HAMD scores) may be independently associated with MetS in MDD patients.

Towards precision in the diagnostic profiling of patients: leveraging symptom dynamics as a clinical characterisation dimension in the assessment of major depressive disorder.

BACKGROUND: International guidelines present overall symptom severity as the key dimension for clinical characterisation of major depressive disorder (MDD). However, differences may reside within severity levels related to how symptoms interact in an individual patient, called symptom dynamics. AIMS: To investigate these individual differences by estimating the proportion of patients that display differences in their symptom dynamics while sharing the same overall symptom severity. METHOD: Participants with MDD (n = 73; mean age 34.6 years, s.d. = 13.1; 56.2% female) rated their baseline symptom severity using the Inventory for Depressive Symptomatology Self-Report (IDS-SR). Momentary indicators for depressive symptoms were then collected through ecological momentary assessments five times per day for 28 days; 8395 observations were conducted (average per person: 115; s.d. = 16.8). Each participant's symptom dynamics were estimated using person-specific dynamic network models. Individual differences in these symptom relationship patterns in groups of participants sharing the same symptom severity levels were estimated using individual network invariance tests. Subsequently, the overall proportion of participants that displayed differential symptom dynamics while sharing the same symptom severity was calculated. A supplementary simulation study was conducted to investigate the accuracy of our methodology against false-positive results. RESULTS: Differential symptom dynamics were identified across 63.0% (95% bootstrapped CI 41.0-82.1) of participants within the same severity group. The average false detection of individual differences was 2.2%. CONCLUSIONS: The majority of participants within the same depressive symptom severity group displayed differential symptom dynamics. Examining symptom dynamics provides information about person-specific psychopathological expression beyond severity levels by revealing how symptoms aggravate each other over time. These results suggest that symptom dynamics may be a promising new dimension for clinical characterisation, warranting replication in independent samples. To inform personalised treatment planning, a next step concerns linking different symptom relationship patterns to treatment response and clinical course, including patterns related to spontaneous recovery and forms of disorder progression.

Elemental psychopathology: distilling constituent symptoms and patterns of repetition in the diagnostic criteria of the DSM-5.

BACKGROUND: The DSM-5 features hundreds of diagnoses comprising a multitude of symptoms, and there is considerable repetition in the symptoms among diagnoses. This repetition undermines what we can learn from studying individual diagnostic constructs because it can obscure both disorder- and symptom-specific signals. However, these lost opportunities are currently veiled because symptom repetition in the DSM-5 has not been quantified. METHOD: This descriptive study mapped the repetition among the 1419 symptoms described in 202 diagnoses of adult psychopathology in section II of the DSM-5. Over a million possible symptom comparisons needed to be conducted, for which we used both qualitative content coding and natural language processing. RESULTS: In total, we identified 628 distinct symptoms: 397 symptoms (63.2%) were unique to a single diagnosis, whereas 231 symptoms (36.8%) repeated across multiple diagnoses a total of 1022 times (median 3 times per symptom; range 2-22). Some chapters had more repetition than others: For example, every symptom of every diagnosis in the bipolar and related disorders chapter was repeated in other chapters, but there was no repetition for any symptoms of any diagnoses in the elimination disorders, gender dysphoria or paraphilic disorders. The most frequently repeated symptoms included insomnia, difficulty concentrating, and irritability - listed in 22, 17 and 16 diagnoses, respectively. Notably, the top 15 most frequently repeating diagnostic criteria were dominated by symptoms of major depressive disorder. CONCLUSION: Overall, our findings lay the foundation for a better understanding of the extent and potential consequences of symptom overlap.

Trajectories of remitted psychotic depression: identification of predictors of worsening by machine learning.

BACKGROUND: Remitted psychotic depression (MDDPsy) has heterogeneity of outcome. The study's aims were to identify subgroups of persons with remitted MDDPsy with distinct trajectories of depression severity during continuation treatment and to detect predictors of membership to the worsening trajectory. METHOD: One hundred and twenty-six persons aged 18-85 years participated in a 36-week randomized placebo-controlled trial (RCT) that examined the clinical effects of continuing olanzapine once an episode of MDDPsy had remitted with sertraline plus olanzapine. Latent class mixed modeling was used to identify subgroups of participants with distinct trajectories of depression severity during the RCT. Machine learning was used to predict membership to the trajectories based on participant pre-trajectory characteristics. RESULTS: Seventy-one (56.3%) participants belonged to a subgroup with a stable trajectory of depression scores and 55 (43.7%) belonged to a subgroup with a worsening trajectory. A random forest model with high prediction accuracy (AUC of 0.812) found that the strongest predictors of membership to the worsening subgroup were residual depression symptoms at onset of remission, followed by anxiety score at RCT baseline and age of onset of the first lifetime depressive episode. In a logistic regression model that examined depression score at onset of remission as the only predictor variable, the AUC (0.778) was close to that of the machine learning model. CONCLUSIONS: Residual depression at onset of remission has high accuracy in predicting membership to worsening outcome of remitted MDDPsy. Research is needed to determine how best to optimize the outcome of psychotic MDDPsy with residual symptoms.

Omega-3 fatty acids for inflamed depression - A match/mismatch study.

Despite decades of research on the pathophysiology of depression, the development of new therapeutic interventions has been slow, and no biomarkers of treatment response have been clinically implemented. Several lines of evidence suggest that the clinical and biological heterogeneity among patients with major depressive disorder (MDD) has hampered progress in this field. MDD with low-grade inflammation - "inflamed depression" - is a subtype of depression that may be associated with a superior antidepressant treatment response to anti-inflammatory compounds. Omega-3 fatty acid eicosapentaenoic acid (EPA) has anti-inflammatory properties, and preliminary data suggest that it may be particularly efficacious in inflamed depression. In this study we tested the hypothesis that add-on EPA has greater antidepressant efficacy in MDD patients with high baseline high-sensitivity C-reactive protein (hs-CRP) compared to MDD patients with low hs-CRP. All subjects received 2.2 g EPA, 400 mg docosahexaenoic acid and 800 mg of other fatty acids daily for 8 weeks, added to stable ongoing antidepressant treatment. The primary outcome was change in the 17-item Hamilton Depression Rating Scale (HAMD-17). Patients and raters were blind to baseline hs-CRP status. In an intention-to-treat analysis including all subjects with at least one post baseline visit (n = 101), ahs-CRPcut-off of ≥1 mg/L, but not ≥3 mg/L, was associated with a greater improvement in HAMD-17 total score. In addition to a general antidepressant effect among patients with hs-CRP ≥ 1 mg/L, adjuvant EPA treatment improved symptoms putatively related to inflamed depression such as fatigue and sleep difficulties. This adds to the mounting evidence that delineation of MDD subgroups based on inflammation may be clinically relevant to predict treatment response to anti-inflammatory interventions.

Can the DEX/CRH test or markers of oxidative stress distinguish work-related stress from major depressive disorder and normal controls?

Hypothalamic-pituitary-adrenal (HPA)-axis hyperactivity measured by the combined dexamethasone-CRH test (DEX-CRH test) has been found in patients with major depressive disorder (MDD), whereas hypoactivity has been found in patients with work-related stress. We aimed to investigate the DEX-CRH test as a biomarker to distinguish between MDD and work-related stress (exhaustion disorder - ED). We hypothesized that there would be lower cortisol and ACTH response in participants with ED compared to MDD and healthy controls (HC). Also, we explored if the cortisol response of those patients interacted with robust markers of oxidative stress. Thirty inpatients with MDD and 23 outpatients with ED were recruited. Plasma cortisol and ACTH were sampled during a DEX-CRH test. The main outcome measure, area under the curve (AUC) for cortisol and ACTH, was compa-red between MDD vs. ED participants and a historical HC group. Secondary markers of oxidative stress urinary 8-oxodG and 8-oxoGuo; quality of sleep and psychometrics were obtained. Cortisol concentrations were higher in MDD and ED participants compared to HC, and no differences in AUC cortisol and ACTH were found between ED vs. MDD. Compared to ED, MDD participants had higher stress symptom severity and a lower sense of well-being. No differences in oxidative stress markers or quality of sleep between the groups were found. The result indicates that the patients with ED, like patients with MDD, are non-suppressors in DEX-CRH test and not hypocortisolemic as suggested.

Is peripartum anhedonia a missing target?

The perinatal period is an extremely delicate phase that can involve a high risk for onset of depressive disorders. The Edinburgh Postnatal Depression Scale (EPDS) is a widely validated instrument for assessing perinatal depressive symptoms, including the dimension of anhedonia. There are studies suggesting that the neural mechanism underlying the occurrence of anhedonia in patients with major depressive disorder (MDD) and bipolar depression (BD) might be distinct. Anhedonia seems to represent a more stable and frequent symptom in women with postpartum bipolar relative to unipolar depressive disorder and is associated with significantly higher depressive symptom severity. Perinatal medicine is an important component of women's health. Treatment of anhedonia can be challenging, and the most effective treatment can be a combination of psychotherapy and medication, but the screening of anhedonia in peripartum women can prevent the development of other psychiatric disorders and maladaptive behaviors.

Detecting adolescent depression through passive monitoring of linguistic markers in smartphone communication.

BACKGROUND: Cross sectional studies have identified linguistic correlates of major depressive disorder (MDD) in smartphone communication. However, it is unclear whether monitoring these linguistic characteristics can detect when an individual is experiencing MDD, which would facilitate timely intervention. METHODS: Approximately 1.2 million messages typed into smartphone social communication apps (e.g. texting, social media) were passively collected from 90 adolescents with a range of depression severity over a 12-month period. Sentiment (i.e. positive vs. negative valence of text), proportions of first-person singular pronouns (e.g. 'I'), and proportions of absolutist words (e.g. 'all') were computed for each message and converted to weekly aggregates temporally aligned with weekly MDD statuses obtained from retrospective interviews. Idiographic, multilevel logistic regression models tested whether within-person deviations in these linguistic features were associated with the probability of concurrently meeting threshold for MDD. RESULTS: Using more first-person singular pronouns in smartphone communication relative to one's own average was associated with higher odds of meeting threshold for MDD in the concurrent week (OR = 1.29; p = .007). Sentiment (OR = 1.07; p = .54) and use of absolutist words (OR = 0.99; p = .90) were not related to weekly MDD. CONCLUSIONS: Passively monitoring use of first-person singular pronouns in adolescents' smartphone communication may help detect MDD, providing novel opportunities for early intervention.

Network connectivity between psychological resilience and depression in prostate cancer patients.

OBJECTIVES: Prostate cancer (PCa) patients often experience depression. One possible buffer against stress-related depression is psychological resilience (PR), which has been described as heterogeneous in structure, like major depressive disorder (MDD). Although both of these constructs are central to understanding and assisting distressed PCa patients, no data have been reported on how they connect via network arrays at a component and symptom level. Such information has the potential to inform clinical practice with depressed PCa patients. METHODS: Using a cross-sectional design, 555 PCa patients completed the Patient Health Questionnaire-9 (PHQ-9) and the Connor-Davison Resilience Scale (CDRISC). Data were analysed via network analysis. RESULTS: Network analysis indicated that various CDRISC factors interacted with different PHQ-9 symptoms. For example, trust in one's instincts, tolerance of negative affect, and strengthening effects of stress (CDRISC) was associated with concentration problems and suicidal ideation (PHQ-9); positive acceptance of change, and secure relationships (CDRISC) was linked to low self-worth, anhedonia, fatigue/lethargy, motor problems, depressed mood, and concentration and appetite problems (PHQ-9). Similarly heterogeneous associations were found between individual CDRISC items and PHQ-9 symptoms. Network analytic figures depict both these sets of associations. CONCLUSIONS: As well as confirming the heterogeneous nature of PR and MDD in PCa patients, these findings argue for the further development of 'individualised' medicine approaches when working with PCa patients and their experiences of depression.

Longitudinal Clusters of Long-Term Trajectories in Patients with Early-Onset Chronic Depression: 2 Years of Naturalistic Follow-Up after Extensive Psychological Treatment.

INTRODUCTION: In clinical trials, mostly group-level treatment effects of repeated cross-sectional measures are analyzed. However, substantial heterogeneity regarding individual symptom profiles and the variability of treatment effects are often neglected, especially over the long-term course. To provide effective personalized treatments, investigations of these characteristics are urgently needed. METHODS: Depression severity ratings over 104 weeks of follow-up after year-long treatment with the Cognitive Behavioral Analysis System of Psychotherapy (CBASP) or Supportive Psychotherapy (SP) were analyzed. Longitudinal cluster analysis and multinomial logistic regression analysis were conducted to investigate intraindividual trajectories from one of the largest psychotherapy trials in early-onset chronic depression. RESULTS: Two-year post-study-treatment trajectories of N = 188 patients with early-onset chronic depression were grouped into four prototypical clusters. Overall, 16.0% of patients remitted (cluster 1) and most of them did not receive any treatment during the 2-year follow-up. However, 84.0% of patients continued to experience subthreshold (37.2% cluster 2) or major depressive symptoms (46.8% clusters 3-4) and spent on average more than half of the follow-up in pharmacological and psychological treatment. Hierarchical regression analysis indicated that previous study treatment with CBASP or SP did not significantly predict cluster allocation, while baseline variables accounted for a large proportion of explained variance (R2 N = 0.64). CONCLUSION: While some patients experienced stable remission over 2 years of follow-up, the majority of patients experienced subthreshold or major depressive symptoms regardless of former study treatment with CBASP or SP. This calls for a long-term perspective implementing staging and innovative treatment approaches such as the sequential model or modular psychotherapy.

Cognitive Impairment and Depression in Mastocytosis: A Synthesis of the Literature.

PURPOSE OF REVIEW: Symptoms of depression and cognitive dysfunction are commonly reported in mastocytosis. The aims of this review paper are to summarize the current literature on cognitive dysfunction and depressive symptoms, elucidate some of the mechanistic pathways underlying depressive symptoms in mastocytosis, identify gaps in the literature, and offer guidance for future research in this area. RECENT FINDINGS: The study of cognition and depression in mastocytosis is in its infancy and the methodological flaws of the current literature limit interpretability. There is preliminary evidence that some individuals with mastocytosis might experience mild deficits in memory. On average, depression symptom scores fell within the mild to moderate or sub-syndromal range. Regrettably, only one study utilized a standardized diagnostic instrument to assess major depressive disorder. The authors' tendency to inaccurately equate depressive symptoms with a diagnosis of major depressive disorder presents a notable issue. The prevalence of cognitive deficits and depression appears to be similar to other chronic illnesses. Future work needs to better characterize cognition and characterize "depression" in this population.

Impact of comorbid opioid use disorder and major depressive disorder on healthcare utilization outcomes in patients with peripheral artery disease: A National Readmission Database analysis.

BACKGROUND: Prior research has demonstrated that individuals with peripheral artery disease (PAD) often have comorbid opioid use disorder (OUD) and major depressive disorder (MDD), with limited data regarding their impact on readmission outcomes, length of stay, and cost. This study aimed to investigate these healthcare utilization outcomes in patients with PAD who have comorbid OUD and MDD. METHODS: Data were obtained from the National Readmission Database from 2011 through 2018. The study population included all hospitalizations with PAD as the primary or secondary diagnosis, from which hospitalizations with OUD and MDD were extracted using appropriate ICD-9/10 diagnosis codes. Primary outcomes were 30-day and 90-day readmission, total cost, and total length of stay within the calendar year. We created hierarchical multivariable logistic regression models examining OUD with and without MDD, with a random effect for healthcare facility location. RESULTS: From 2011 to 2018, 13,265,817 weighted admissions with PAD were identified. These admissions were segmented into four categories: No OUD/No MDD (12,056,466), OUD/No MDD (323,762), No OUD/MDD (867,641), and OUD/MDD (17,948). The group with No OUD/No MDD was used as the reference group for all subsequent comparisons. Regarding 30-day and 90-day readmissions, patients with OUD/MDD had odds of 1.14 (95% CI 1.10, 1.18) and 1.09 (95% CI 1.06, 1.13), respectively. Patients with OUD/No MDD bore the highest median cost of $64,354 (IQR $30,797-137,074), and patients with OUD/MDD marked the lengthiest median stay of 6.01 days (IQR 2.01-13.30). CONCLUSION: This study found a significant association between these comorbidities and outcomes and therefore calls for targeted interventions and pain management strategies.