An examination of DSM-IV depressive symptoms and risk for suicide completion in major depressive disorder: a psychological autopsy study

BACKGROUND: It is unclear whether certain DSM-IV depressive symptoms are more prevalent among individuals who die in the context of a major depressive episode and those who do not, whether this is associated with proximal or distal suicide risk, and whether depressive symptoms cluster to indicate suicide risk. METHOD: A psychological autopsy method with best informants was used to investigate DSM-IV depressive symptoms among 156 suicides who died in the context of a major depressive episode and 81 major depressive controls. RESULTS: Suicides' depressive symptoms were more likely to include weight or appetite loss, insomnia, feelings of worthlessness or inappropriate guilt as well as recurrent thoughts of death or suicidal ideation. Fatigue and difficulties concentrating or indecisiveness were less prevalent among depressed suicides. These associations were independent of concomitant axis I and II psychopathology. The concomitant presence of (a) fatigue as well as impaired concentration or indecisiveness and (b) weight or appetite gain and hypersomnia was associated with decreased suicide risk. Inter-episode symptom concordance suggests that insomnia is an immediate indicator of suicide risk, while weight or appetite loss and feelings of worthlessness or guilt are not. LIMITATIONS: This study employed proxy-based interviews. CONCLUSIONS: We found that discrete DSM-IV depressive symptoms and clusters of depressive symptoms help differentiate depressed individuals who die by suicide and those who do not. Moreover, some DSM-IV depressive symptoms are associated with an immediate risk for suicide, while others may result from an etiology of depression common to suicide without directly increasing suicide risk.     

The Quick Inventory of Depressive Symptomatology-Self-report: a psychometric evaluation in patients with asthma and major depressive disorder.

BACKGROUND: Despite the high co-occurrence between depression and asthma, few studies have addressed methods assessing the severity of depressive symptoms among patients with asthma. OBJECTIVE: To evaluate the psychometric properties of the Quick Inventory of Depressive Symptomatology-Self-report (QIDS-SR16), a 16-item measure of depressive symptom severity, in patients with asthma. METHODS: The psychometric properties of the QIDS-SR16 were compared at treatment exit with those of the 30-item self-report Inventory of Depressive Symptomatology (IDS-SR30) and the 17-item clinician-rated Hamilton Rating Scale for Depression (HRSD17) in 73 outpatients with asthma who were treated with citalopram or placebo for nonpsychotic major depressive disorder. Correlations between the depression rating scales and the Mini Asthma Quality of Life Questionnaire were calculated. RESULTS: Internal consistency at exit was strong for the QIDS-SR16 (Cronbach alpha values are .87 for the QIDS-SR16, .95 for the IDS-SR30, and .87 for the HRSD17). The QIDS-SR16 and HRSD17 total scores were highly correlated (r = 0.85), as were the QIDS-SR16 and IDS-SR30 total scores (r = 0.97). All QIDS-SR16 item total score correlations were significant (P < .001). The QIDS-SR16, IDS-SR30, and HRSD17 showed comparable sensitivity to symptom change, indicating high concurrent validity for all 3 scales. The total QIDS-SR16 baseline to exit change score demonstrated a significant negative correlation (r = -0.49, P < .001) with the Mini Asthma Quality of Life Questionnaire. Thus, greater depressive symptom severity was associated with lower asthma-related quality of life. CONCLUSIONS: The QIDS-SR16 showed good reliability and impressive construct validity. Strong psychometric properties of this brief self-report format and its sensitivity to treatment change suggest that the QIDS-SR16 is a valuable clinical tool.     

A machine learning framework involving EEG-based functional connectivity to diagnose major depressive disorder (MDD)

Major depressive disorder (MDD), a debilitating mental illness, could cause functional disabilities and could become a social problem. An accurate and early diagnosis for depression could become challenging. This paper proposed a machine learning framework involving EEG-derived synchronization likelihood (SL) features as input data for automatic diagnosis of MDD. It was hypothesized that EEG-based SL features could discriminate MDD patients and healthy controls with an acceptable accuracy better than measures such as interhemispheric coherence and mutual information. In this work, classification models such as support vector machine (SVM), logistic regression (LR) and Naïve Bayesian (NB) were employed to model relationship between the EEG features and the study groups (MDD patient and healthy controls) and ultimately achieved discrimination of study participants. The results indicated that the classification rates were better than chance. More specifically, the study resulted into SVM classification accuracy = 98%, sensitivity = 99.9%, specificity = 95% and f-measure = 0.97; LR classification accuracy = 91.7%, sensitivity = 86.66%, specificity = 96.6% and f-measure = 0.90; NB classification accuracy = 93.6%, sensitivity = 100%, specificity = 87.9% and f-measure = 0.95. In conclusion, SL could be a promising method for diagnosing depression. The findings could be generalized to develop a robust CAD-based tool that may help for clinical purposes.     

Recurrence in major depressive disorder: a neurocognitive perspective

Depressive disorders are amongst the leading causes of disability and mortality worldwide and, as such, it is predicted that by 2010 only cardio-ischaemic disorders will provide a greater burden. In addition to the sizable emotional, individual and social burden, depressive disorders cost an estimated US$83.1 billion per year in the United States alone. In spite of effective treatments, a large proportion of sufferers go on to experience recurrences. With successive recurrences, the likelihood of subsequent episodes increases. Despite this, research to date has tended to focus on first episodes or else has not distinguished between episodes. This editorial review highlights a number of differences between first and recurrent episodes which, in turn, recommend more longitudinal, recurrence-oriented, treatments. We also examine the findings from acute tryptophan depletion studies which, it is speculated, help to understand the differences between successive episodes. The overall aim, however, is to highlight the importance of recurrence in depression and to stimulate debate.     

Premenstrual dysphoric disorder and risk for major depressive disorder: a preliminary study.

Investigators examined whether premenstrual dysphoric disorder (PMDD) poses a risk for major depressive disorder (MDD). In an initial study, women rated premenstrual symptoms and functional impairment daily for two menstrual cycles. A semistructured diagnostic interview was given to obtain psychiatric histories and differentiate PMDD from premenstrual exacerbations of other disorders. Participants in this pilot study were eight women with PMDD and a random subgroup without PMDD (n = 9) initially. Another semistructured interview was given to diagnose psychiatric disorders occurring during a two-year follow-up interval. In all, seven of the eight women with PMDD developed MDD within two years, including all those who had never had MDD before. The odds that a woman with PMDD developed MDD were 14 times the odds that a woman without PMDD developed MDD ( p <.05). Premenstrual dysphoric disorder may be a prodrome of or causal risk factor for MDD. Preliminary evidence for the diagnostic validity of PMDD is provided.     

Depression in paranoid and nonparanoid schizophrenic patients compared with major depressive disorder

Background: Depression occurring in schizophrenia is a common problem; however, investigators have typically not studied it with the paranoid/nonparanoid dichotomy in mind. This study examines the quality as well as the severity of depression in three psychiatric groups: paranoid schizophrenia patients, nonparanoid schizophrenia patients, and nonpsychotic major depression patients. Method: Clinical and sociodemographic data were collected on 27 paranoid and 27 nonparanoid schizophrenia patients during their postpsychotic phase while they were at least mildly depressed, and a comparison group of 27 nonpsychotic patients diagnosed with major depressive disorder. The three groups were then assessed on various psychometric scales for severity of depression, profile of symptoms, suicidal risk, and anhedonia. Results: The paranoid schizophrenia patients were more depressed and more at risk for suicide than the nonparanoid schizophrenia patients, yet their depressive profiles and levels of anhedonia were similar. Conclusions: Depressed mood and anhedonia constitute serious problems for schizophrenia patients, but particularly for paranoid schizophrenia patients during the postpsychotic phase of their illnesses. Clinical implications: Schizophrenia patients, especially those with the paranoid features, should be routinely evaluated and monitored for depression. Apart from treatment with drugs, cognitive therapy may be considered a viable option, particularly for paranoid schizophrenia patients. Limitations: Gender was not matched for the two schizophrenia groups and extrapyramidal side effects were not measured.     

Diagnostic conversions from major depressive disorder into bipolar disorder in an outpatient setting: Results of a retrospective chart review

BackgroundThe aim of the study was to check the stability of a diagnosis of major depressive disorder (MDD) in an outpatient setting, as well as to assess the scope of diagnostic conversions into bipolar disorder (BD).Methods: Retrospective chart review of 122 patients with a primary diagnosis of MDD.ResultsDiagnostic conversion from MDD into BD was noticed in 40 subjects (32.8%), 25 patients (20.5%) were treatment-resistant. Mean time to the conversion was 9.27±8.64 years. A negative correlation between the age of illness onset and time to diagnostic conversion was observed (?0.41; p<0.05). Earlier onset of MDD was associated with higher risk of diagnostic conversion (<30vs≥30 years of age at onset: 69% vs 28%, p=0.0001; <35vs≥35 years of age: 50% vs 25%, p=0.0065). Treatment-resistance was more prevalent in the BD conversion group (40% vs 11%; p=0.0002). Diagnostic conversion into BD was also related longer duration of treatment received, higher number of illness episodes, and higher number of hospitalizations.Limitations: Retrospective design of the study.ConclusionsThe problem of diagnosis evolution from MDD to BD was observed in about 1/3 of patients, and was associated with treatment-resistance of depression, earlier onset of depression, longer time of treatment, higher number of depressive episodes and hospitalizations. The variables above may be a useful predictor of bipolar diathesis.     

Screening for Depression in the Medically Ill: a comparison of self-report measures, clinician judgment, and DSM-IV diagnoses

The performance of the self-report 10-item Depression in the Medically Ill scale was observed in 210 patients as part of clinical assessment by consultation-liaison psychiatry clinicians. Both the Depression in the Medically Ill scale and the Beck Depression Inventory for Primary Care were completed by the patient, and the clinicians made their judgment of the presence and severity of "clinical depression" and DSM-IV affective disorder diagnoses. Both the Depression in the Medically Ill scale and the Beck Depression Inventory for Primary Care detected 85% of patients with DSM-IV major depressive episode. The Depression in the Medically Ill scale was slightly superior to the Beck Depression Inventory for Primary Care in its relationship to clinicians' judgments of clinical depression caseness.     

Data-driven subtypes of major depressive disorder: a systematic review

Background

According to current classification systems, patients with major depressive disorder (MDD) may have very different combinations of symptoms. This symptomatic diversity hinders the progress of research into the causal mechanisms and treatment allocation. Theoretically founded subtypes of depression such as atypical, psychotic, and melancholic depression have limited clinical applicability. Data-driven analyses of symptom dimensions or subtypes of depression are scarce. In this systematic review, we examine the evidence for the existence of data-driven symptomatic subtypes of depression.

Methods

We undertook a systematic literature search of MEDLINE, PsycINFO and Embase in May 2012. We included studies analyzing the depression criteria of the Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV) of adults with MDD in latent variable analyses.

Results

In total, 1176 articles were retrieved, of which 20 satisfied the inclusion criteria. These reports described a total of 34 latent variable analyses: 6 confirmatory factor analyses, 6 exploratory factor analyses, 12 principal component analyses, and 10 latent class analyses. The latent class techniques distinguished 2 to 5 classes, which mainly reflected subgroups with different overall severity: 62 of 71 significant differences on symptom level were congruent with a latent class solution reflecting severity. The latent class techniques did not consistently identify specific symptom clusters. Latent factor techniques mostly found a factor explaining the variance in the symptoms depressed mood and interest loss (11 of 13 analyses), often complemented by psychomotor retardation or fatigue (8 of 11 analyses). However, differences in found factors and classes were substantial.

Conclusions

The studies performed to date do not provide conclusive evidence for the existence of depressive symptom dimensions or symptomatic subtypes. The wide diversity of identified factors and classes might result either from the absence of patterns to be found, or from the theoretical and modeling choices preceding analysis.

     

Anxious-retarded depression: relation to two-year outcome of major depressive disorder

BACKGROUND: Anxious-retarded depression is a two-dimensionally defined subcategory of depression derived from DSM-IV melancholia. It is related to increased plasma vasopressin, correlative plasma vasopressin and cortisol levels, and a positive family history. We now explored its relation with outcome. METHODS: Seventy depressed patients were included to follow-up for two years. Outcome was defined by time until full-remission. Cox regression analyses were used to compare anxious-retarded and non-anxious-retarded patients, as well as melancholic and non-melancholic patients. RESULTS: Anxious-retarded depression had poor outcome. LIMITATIONS: The number of patients was relatively small. CONCLUSION: The poor outcome of anxious-retarded depression further supports its validity.