Detecting depression in Parkinson disease A systematic review and meta-analysis
Zahra Goodarzi,Kelly Mrklas,Derek J. Roberts,Nathalie Jette,Tamara Pringsheim,Jayna Holroyd-Leduc +5 more
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TL;DR: There are several valid tools for detecting depression in patients with PD and practitioners should choose one that fits their clinical practice, although all estimates had heterogeneity.
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Abstract: Background: Failure to detect depression in patients with Parkinson disease (PD) can lead to worsened outcomes for patients and caregivers. Accurate identification of depression would enable practitioners to provide comprehensive care for their patients with PD. Methods: Our objective was to examine the diagnostic accuracy of tools for detecting depression in adult outpatients with PD. We searched MEDLINE, PsycINFO, and EMBASE (inception to December 1, 2015), gray literature, and bibliographies of included studies. The pooled prevalence of depression across studies and diagnostic accuracy estimates were calculated using random-effects models. Diagnostic accuracy estimates were calculated across the best-reported cutoffs from each study and across specific cutoffs, when feasible. Results: Out of 8,184 citations, 21 studies were included, evaluating 24 tools, with 4 amenable to meta-analysis. The pooled prevalence of major depression was 22.9% (95% confidence interval [CI] 18.1–27.7). The 15-item Geriatric Depression Scale (GDS-15) had a pooled sensitivity of 0.81 (95% CI 0.64–0.91) and specificity of 0.91 (95% CI 0.87–0.94). The most sensitive cutoff for the GDS-15 was 5 at 0.91 (95% CI 0.83–1.00). The Beck Depression Inventory I/Ia had a pooled sensitivity of 0.79 (95% CI 0.61–0.90) and specificity of 0.85 (95% CI 0.79–0.90). The Montgomery-Asberg Depression Rating Scale yielded a pooled sensitivity of 0.77 (95% CI 0.69–0.83) and specificity of 0.92 (95% CI 0.79–0.97). The Unified Parkinson9s Disease Rating Scale had a pooled sensitivity of 0.72 (95% CI 0.64–0.79) and specificity of 0.80 (95% CI 0.70–0.87). All estimates had heterogeneity. Conclusions: There are several valid tools for detecting depression in patients with PD. Practitioners should choose one that fits their clinical practice.
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Citations
Morbidity and Associated Factors of Depressive Disorder in Patients With Parkinson's Disease
TL;DR: It is suggested that use of a standardized structured interview for early detection of depression in PD patients is crucial and anxiety, anxiolytics/hypnotics use, depression, and suicide risks are interrelated and warrant clinical concerns regarding PD patients.
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Electroconvulsive therapy for psychotic depression in a patient with a deep brain stimulator: A Case Report
A. Lucardie,Linda van Diermen,Y. Madani,D. Schrijvers +3 more
TL;DR: A patient with Parkinson's Disease and a deep brain stimulator responded well to electroconvulsive therapy for psychotic depression, with minimal adverse effects, highlighting ECT's safety and efficacy in this population with proper precautions.
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Enhancing Depression Predictive Models: a Comparative Study of Hybrid Ai, Machine Learning and Deep Learning Techniques
Naga Raju` Kanchapogu,Sachi Nandan Mohanty +1 more
- 29 Feb 2024
TL;DR: Enhancing depression predictive models through a comparative study of hybrid AI, machine learning and deep learning techniques. The study revealed a marked superiority of hybrid AI models over standalone ML and DL models in predicting depression with unparalleled accuracy.
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References
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TL;DR: An issue concerning the criteria for tic disorders is highlighted, and how this might affect classification of dyskinesias in psychotic spectrum disorders.
Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement
TL;DR: Moher et al. as mentioned in this paper introduce PRISMA, an update of the QUOROM guidelines for reporting systematic reviews and meta-analyses, which is used in this paper.
Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement
TL;DR: A structured summary is provided including, as applicable, background, objectives, data sources, study eligibility criteria, participants, interventions, study appraisal and synthesis methods, results, limitations, conclusions and implications of key findings.
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