Abstract TMP41: High-sensitivity C-Reactive Protein and Depression in Patients With Ischemic Stroke

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Background: Depression is common in patients after stroke and is associated with worse outcomes Recognition of the causal factors contributing to post-stroke depression may lead to new treatment interventions. Ischemic stroke produces inflammatory responses and there is data to suggest that inflammation may promote depression. The objective of this analysis is to determine the association of hsCRP levels with depression in patients with ischemic stroke.Methods: We performed a retrospective cohort study of patients with ischemic stroke seen in a cerebrovascular clinic who completed a PHQ-9 depression screen and had a hsCRP level drawn between 28 days before or within 7 days after the PHQ-9 screen. Multivariate linear regression was performed:- the dependent variable was PHQ-9 score, the independent variable was log-transformed hsCRP. Additional covariates included age, sex, race (White vs. Black/Other), time since stroke (≤ 3 months vs. > 3 months), mRS score (0-2 vs. 3-5) and statin use at time of hsCRP draw (yes vs. no).Results: Between Feb 3, 2009 and March 14, 2014, 803 patients were seen in the cerebrovascular clinic with a diagnosis of ischemic stroke who had hscrp level. Of these, 220 patients had a hSCRP drawn within the prespecified time interval from PHQ9 screen. Mean age was 60.7 years, 43.2% were female and 78.6% were White. There was a significant independent association between hsCRP value and PHQ-9 score; For each 5-fold increase in hsCRP, the average PHQ-9 score increased by 1.22 points (95% CI 0.38 - 2.06, P = 0.005). Other variables independently associated with PHQ-9 score were: mRS > 2 (OR = 3.39, 95% CI 1.40 - 5.38), female (OR=-1.72 95% CI -3.11 - -0.32), and statin use (OR=-1.91, 95% CI -3.34 - -0.47).Conclusions: hsCRP level was independently associated with depressive symptoms. Understanding the association between inflammatory markers and depression following ischemic stroke may lead to the development of improved methods to predict post-stroke depression and new treatment interventions

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