Depressive Symptoms, Antidepressants and Disability and Future Coronary Heart Disease and Stroke Events in Older Adults: The Three City Study.

Depressive symptoms, antidepressants and disability and future coronary heart disease and stroke events in older adults: the Three City Study.

Filed under: Depression Treatment

Eur J Epidemiol. 2013 Jan 22;
Péquignot R, Tzourio C, Péres K, Ancellin ML, Perier MC, Ducimetière P, Empana JP

To investigate the association between baseline depressive symptoms and first fatal and non fatal coronary heart disease (CHD) and stroke in older adults, taking antidepressants and disability into account. In the Three City Study, a community-based prospective multicentric observational study cohort, 7,308 non-institutionalized men and women aged ?65 years with no reported history of CHD, stroke or dementia, completed the 20-item Center for Epidemiologic Studies Depression Scale (CESD) questionnaire. First CHD and stroke events during follow-up were adjudicated by an independent expert committee. Hazard ratios (HRs) were estimated by Cox proportional hazard model. After a median follow-up of 5.3 years, 338 subjects had suffered a first non-fatal CHD or stroke event, and 82 had died from a CHD or stroke. After adjustment for study center, baseline socio-demographic characteristics, and conventional risk factors, depressive symptoms (CESD ? 16) were associated with fatal events only: fatal CHD plus stroke (HR = 2.50; 95 % CI 1.57-3.97), fatal CHD alone (n = 57; HR = 2.21 ; 95 %CI 1.27-3.87), and fatal stroke alone (n = 25; HR = 3.27; 95 % CI 1.42-7.52). These associations were even stronger in depressed subjects receiving antidepressants (HR = 4.17; 95 % CI 1.84-9.46) and in depressed subjects with impaired Instrumental Activities of Daily Living (HR = 8.93; 95 % CI 4.60-17.34). By contrast, there was no significant association with non fatal events (HR for non-fatal CHD or stroke = 0.94; 95 % CI 0.66-1.33). In non-institutionalized elderly subjects without overt CHD, stroke or dementia, depressive symptoms were selectively and robustly associated with first fatal CHD or stroke events.
HubMed – depression


Training psychiatry residents in quality improvement: an integrated, year-long curriculum.

Filed under: Depression Treatment

Acad Psychiatry. 2013 Jan 1; 37(1): 42-5
Arbuckle MR, Weinberg M, Cabaniss DL, Kistler SC, Isaacs AJ, Sederer LI, Essock SM

OBJECTIVE The authors describe a curriculum for psychiatry residents in Quality Improvement (QI) methodology. METHODS All PGY3 residents (N=12) participated in a QI curriculum that included a year-long group project. Knowledge and attitudes were assessed before and after the curriculum, using a modified Quality Improvement Knowledge Assessment Tool (QIKAT) and a QI Self-Assessment survey. RESULTS QIKAT scores were significantly higher for residents after participating in the curriculum when compared with pretest scores. Self-efficacy ratings in QI improved after the course for each item. Residents demonstrated gains in QI skills through participation in the group projects in which they increased rates of depression-screening and monitoring in an outpatient clinic. CONCLUSIONS Combining didactic and experiential learning can be an effective means for training psychiatry residents in QI.
HubMed – depression


Towards automated detection of depression from brain structural magnetic resonance images.

Filed under: Depression Treatment

Neuroradiology. 2013 Jan 22;
Kipli K, Kouzani AZ, Williams LJ

INTRODUCTION: Depression is a major issue worldwide and is seen as a significant health problem. Stigma and patient denial, clinical experience, time limitations, and reliability of psychometrics are barriers to the clinical diagnoses of depression. Thus, the establishment of an automated system that could detect such abnormalities would assist medical experts in their decision-making process. This paper reviews existing methods for the automated detection of depression from brain structural magnetic resonance images (sMRI). METHODS: Relevant sources were identified from various databases and online sites using a combination of keywords and terms including depression, major depressive disorder, detection, classification, and MRI databases. Reference lists of chosen articles were further reviewed for associated publications. RESULTS: The paper introduces a generic structure for representing and describing the methods developed for the detection of depression from sMRI of the brain. It consists of a number of components including acquisition and preprocessing, feature extraction, feature selection, and classification. CONCLUSION: Automated sMRI-based detection methods have the potential to provide an objective measure of depression, hence improving the confidence level in the diagnosis and prognosis of depression.
HubMed – depression


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