Predictive accuracy of the Post-Stroke Depression Prediction Scale: A prospective binational observational study ✰ .

Background: Depression after a stroke is a common complication that negatively influences stroke rehabilitation. Early identification, followed by adequate treatment of depression, improves recovery from stroke. To support early identification, the Post-stroke Depression Prediction Scale (DePreS) was developed to predict in the first week after stroke, the risk of depression in the second month. In this study we investigate the predictive accuracy of the DePreS in stroke patients.
Methods: In this prospective multicenter observational study, hospitalized stroke patients were included from three stroke units in the Netherlands and Germany using consecutive sampling. In the first week after stroke, the predicted risk for depression was estimated with the DePreS. Two months after stroke, major depressive disorder was determined with the Composite International Diagnostic Interview.
Results: Of the 93 included patients, 17 (18.3%) showed symptoms of major depressive disorder. With a cut-off value of ≥ 0, DePreS performed optimally with a sensitivity of 0.65 (95% CI 0.42-0.87), specificity of 0.74 (95% CI 0.64-0.84), positive predictive value of 0.35 (95% CI 0.19-0.52), and negative predictive value of 0.90 (95% CI 0.80-1.00). The AUC was 0.71 (95% CI 0.56-0.86).
Limitations: The generalizability of the study findings is limited to patients able to communicate adequately.
Conclusions: This study demonstrates that the DePreS is an adequate instrument for early and reliable identification of stroke patients who are not at risk of MDD in the second months after stroke. This limits the need for structural diagnostic follow-up to patients with a high risk.
Competing Interests: Declaration of Competing Interest None.
(Copyright © 2020. Published by Elsevier B.V.)