Detecting depression in Parkinson disease A systematic review and meta-analysis

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-Åsberg 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 Parkinson's 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.

Neurology 2016