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104 results for “psychiatry”
Consequences of the QUARANTINE Relating to the COvid-19 Epidemic on the Mental Health of the Patients Followed in PSYchiatry
ClinicalTrials.gov study NCT04405362. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Personalized Psychiatry and Depression: The Role of Sociodemographic and Clinical Variables
<p>Personalized Psychiatry and Depression:<br> The Role of Sociodemographic and Clinical Variables</p> <p>Despite several pharmacological options, the clinical outcomes of major depressive disorder (MDD) are often unsatisfactory. Personalized<br> psychiatry attempts to tailor therapeutic interventions according to each patient’s unique profile and characteristics. This approach<br> can be a crucial strategy in improving pharmacological outcomes in MDD and overcoming trial-and-error treatment choices. In this<br> narrative review, we evaluate whether sociodemographic (i.e., gender, age, race/ethnicity, and socioeconomic status) and clinical [i.e.,<br> body mass index (BMI), severity of depressive symptoms, and symptom profiles] variables that are easily assessable in clinical practice<br> may help clinicians to optimize the selection of antidepressant treatment for each patient with MDD at the early stages of the disorder.<br> We found that several variables were associated with poorer outcomes for all antidepressants. However, only preliminary associations<br> were found between some clinical variables (i.e., BMI, anhedonia, and MDD with melancholic/atypical features) and possible benefits<br> with some specific antidepressants. Finally, in clinical practice, the assessment of sociodemographic and clinical variables considered in<br> our review can be valuable for early identification of depressed individuals at high risk for poor responses to antidepressants, but there<br> are not enough data on which to ground any reliable selection of specific antidepressant class or compounds. Recent advances in computational<br> resources, such as machine learning techniques, which are able to integrate multiple potential predictors, such as individual/<br> clinical variables, biomarkers, and genetic factors, may offer future reliable tools to guide personalized antidepressant choice for each patient<br> with MDD.</p>
'Precision' or 'personalized' psychiatry: different terms – same content?
<p>‘Precision’ or ‘personalized’ psychiatry: different terms – same<br> content?</p> <p>Due to the increased lifetime prevalence and personal,<br> social, and economic burden of mental disorders, psychiatry<br> is in need of a significant change in several aspects of<br> its clinical and research approaches. Over the last few<br> decades, the development of personalized / precision medicine in psychiatry focusing on tailored therapies that<br> fit each patient’s unique individual, physiological, and<br> genetic profile has not achieved the same results as those<br> obtained in other branches, such as oncology. The longawaited<br> revolution has not yet surfaced. There are various<br> explanations for this including imprecise diagnostic criteria,<br> incomplete understanding of the molecular pathology<br> involved, absence of available clinical tools and, finally, the<br> characteristics of the patient. Since then, the co-existence<br> of the two terms has sparked a great deal of discussion<br> around the definition and differentiation between the two<br> types of psychiatry, as they often seem similar or even<br> superimposable. Generally, the two terminologies are used<br> indiscriminately, alternatively, and / or separately, within<br> the same scientific works. In this paper, an overview is<br> provided on the overlap between the application and<br> meaning of the terms ‘precision psychiatry’ and ‘personalized<br> psychiatry’.</p>
Dataset for the project "Towards precision medicine in psychiatry: clinical validation of a combinatorial pharmacogenomic approach" Italian Ministry of Health, Italy, Ricerca Finalizzata (Grant RF-2016-02361697)
<p><strong>Dataset for the project “Towards precision medicine in psychiatry: clinical validation of a combinatorial pharmacogenomic approach” Italian Ministry of Health, Italy, Ricerca Finalizzata (Grant RF-2016-02361697)</strong></p> <p><strong>Aim 1</strong>: Assessment of the validity and clinical utility of a pharmacogenetic test in the selection of antidepressant treatment for patients with Major Depressive Disorder.</p> <p><strong>Aim 2</strong>: Pharmacokinetic analysis and identification of novel genetic variants linked with no response to treatment and/or with the occurrence of side effects.</p> <p><strong>Aim 3</strong>: Implementation of the combinatorial algorithm based on possible new findings.</p> <p>This dataset contains :</p> <ul> <li><strong>RawData_Aim1</strong></li> </ul> <p>· Materials and Methods for Aim 1 (Materials and methods_RawData_Aim1)</p> <p>· An Excel table (RF16 - Genotyping - Aim1) with the following sheets:</p> <p>- Sheet 1: Genotyping results</p> <p>- Sheet 2: Metabolizer phenotype results for CYP2D6 and CYP2C19 genes</p> <ul> <li><strong>RawData_Aim2</strong></li> </ul> <p>· Materials and methods Sequencing_RawData_Aim2</p> <p>· Materials and methods Pharmacokinetics_RawData_Aim2</p> <p>· VCF data from sequencing analysis (filtered_variants - Aim2)</p> <p>· Pharmacokinetics analysis (Pharmacokinetics Data_Aim2)</p> <ul> <li><strong>RawData_Aim3</strong></li> </ul> <p>· An Excel table with data from clinical assessments (Clinical Data_Aim3)</p>
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OpenNeuro
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