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This study aimed to identify clinical and cognitive factors associated with increased risk for difficult-to-treat depression (DTD) or treatment-resistant depression (TRD).
A total of 229 adult outpatients with major depression were recruited from the mental health unit at a public hospital. Participants were subdivided into resistant and nonresistant groups according to their Maudsley Staging Model score. Sociodemographic, clinical, and cognitive (objective and subjective measures) variables were compared between groups, and a logistic regression model was used to identify the factors most associated with TRD risk.
TRD group patients present higher verbal memory impairment than the nonresistant group irrespective of pharmacological treatment or depressive symptom severity. Logistic regression analysis showed that low verbal memory scores (odds ratio [OR]: 2.02; 95% confidence interval [CI]: 1.38–2.95) together with high depressive symptom severity (OR: 1.29; CI95%: 1.01–1.65) were associated with TRD risk.
Our findings align with neuroprogression models of depression, in which more severe patients, defined by greater verbal memory impairment and depressive symptoms, develop a more resistant profile as a result of increasingly detrimental neuronal changes. Moreover, our results support a more comprehensive approach in the evaluation and treatment of DTD in order to improve illness course. Longitudinal studies are warranted to confirm the predictive value of verbal memory and depression severity in the development of TRD.
Heterogeneity in cognitive functioning among major depressive disorder (MDD) patients could have been the reason for the small-to-moderate differences reported so far when it is compared to other psychiatric conditions or to healthy controls. Additionally, most of these studies did not take into account clinical and sociodemographic characteristics that could have played a relevant role in cognitive variability. This study aims to identify empirical clusters based on cognitive, clinical and sociodemographic variables in a sample of acute MDD patients.
In a sample of 174 patients with an acute depressive episode, a two-step clustering analysis was applied considering potentially relevant cognitive, clinical and sociodemographic variables as indicators for grouping.
Treatment resistance was the most important factor for clustering, closely followed by cognitive performance. Three empirical subgroups were obtained: cluster 1 was characterized by a sample of non-resistant patients with preserved cognitive functioning (n = 68, 39%); cluster 2 was formed by treatment-resistant patients with selective cognitive deficits (n = 66, 38%) and cluster 3 consisted of resistant (n = 23, 58%) and non-resistant (n = 17, 42%) acute patients with significant deficits in all neurocognitive domains (n = 40, 23%).
The findings provide evidence upon the existence of cognitive heterogeneity across patients in an acute depressive episode. Therefore, assessing cognition becomes an evident necessity for all patients diagnosed with MDD, and although treatment resistant is associated with greater cognitive dysfunction, non-resistant patients can also show significant cognitive deficits. By targeting not only mood but also cognition, patients are more likely to achieve full recovery and prevent new relapses.
Mental disorders in the elderly are common, with a 12-month prevalence in the community ranging from 8.54% to 26.4%. Unfortunately, many mental disorders are unrecognized, untreated, and associated with poor health outcomes. The aim of this paper is to describe the prevalence of mental disorders in the elderly primary care (PC) population and its associated factors by age groups.
Cross-sectional survey, conducted in 77 PC centers in Catalonia (Spain), 1,192 patients over 65 years old. The prevalence of mental disorders was assessed through face-to-face evaluations using the Structured Clinical Interview for DSM-IV Axis I Disorders, Research Version (SCID-I-RV) and the Mini International Neuropsychiatric Interview (MINI); chronic physical conditions were noted using a checklist; and disability through the Sheehan Disability Scales (SDS).
Nearly 20% of participants had a mental disorder in the previous 12 months. Anxiety disorders were the most frequent, (10.9%) (95% CI = 8.2–14.4), followed by mood disorders (7.4%) (95% CI = 5.7–9.5). Being female, greater perceived stress and having mental health/emotional problems as the main reason for consultation were associated with the presence of any mental disorder. There were no differences in prevalence across age groups. Somatic comorbidity was not associated with the presence of mental disorders.
Mental disorders are highly prevalent among the elderly in PC in Spain. Efforts are needed to develop strategies to reduce this prevalence and improve the well-being of the elderly. Based on our results, we thought it might be useful to assess perceived stress regularly in PC, focusing on people who consult for emotional distress, or that have greater perceived stress.
Within the ICD and DSM review processes there is growing debate on the
future classification and status of adjustment disorders, even though
evidence on this clinical entity is scant, particularly outside
To estimate the prevalence of adjustment disorders in primary care; to
explore whether there are differences between primary care patients with
adjustment disorders and those with other mental disorders; and to
describe the recognition and treatment of adjustment disorders by general
Participants were drawn from a cross-sectional survey of a representative
sample of 3815 patients from 77 primary healthcare centres in Catalonia.
The prevalence of current adjustment disorders and subtypes were assessed
face to face using the Structured Clinical Interview for DSM-IV Axis I
Disorders (SCID-I). Multilevel logistic regressions were conducted to
assess differences between adjustment disorders and other mental
disorders. Recognition and treatment of adjustment disorders by GPs were
assessed through a review of patients' computerised clinical
The prevalence of adjustment disorders was 2.94%. Patients with
adjustment disorders had higher mental quality-of-life scores than
patients with major depressive disorder but lower than patients without
mental disorder. Self-perceived stress was also higher in adjustment
disorders compared with those with anxiety disorders and those without
mental disorder. Recognition of adjustment disorders by GPs was low: only
2 of the 110 cases identified using the SCID-I were detected by the GP.
Among those with adjustment disorders, 37% had at least one psychotropic
Adjustment disorder shows a distinct profile as an intermediate category
between no mental disorder and affective disorders (depression and
The World Health Organization (WHO) has stated that the three leading
causes of burden of disease in 2030 are projected to include HIV/AIDS,
unipolar depression and ischaemic heart disease.
To estimate health-related quality of life (HRQoL) and quality-adjusted
life-year (QALY) losses associated with mental disorders and chronic
physical conditions in primary healthcare using data from the diagnosis
and treatment of mental disorders in primary care (DASMAP) study, an
epidemiological survey carried out with primary care patients in
A cross-sectional survey of a representative sample of 3815 primary care
patients. A preference-based measure of health was derived from the
12-item Short Form Health Survey (SF–12): the Short Form–6D (SF–6D)
multi-attribute health-status classification. Each profile generated by
this questionnaire has a utility (or weight) assigned. We used
non-parametric quantile regressions to model the association between both
mental disorders and chronic physical condition and SF–6D scores.
Conditions associated with SF–6D were: mood disorders, β =−0.20 (95% CI
−0.18 to −0.21); pain, β = −0.08 (95%CI −0.06 to −0.09) and anxiety, β
=−0.04 (95% CI −0.03 to −0.06). The top three causes of QALY losses
annually per 100 000 participants were pain (5064), mood disorders (2634)
and anxiety (805).
Estimation of QALY losses showed that mood disorders ranked second behind
pain-related chronic medical conditions.
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