Abstract Body: Background:
Post stroke depression (PSD) affects over 33% of stroke survivors with the highest incidence occurring in the first year after stroke. Predictors of PSD include stroke severity, cognitive impairment, age, and physical disability. Antidepressant prescribing patterns during acute ischemic stroke (AIS) hospitalization may be inconsistent. This study evaluated antidepressant prescribing patterns at hospital discharge in acute AIS and hemorrhagic (ICH) stroke patients.

Methods:
We retrospectively examined prospectively collected data from an IRB-approved stroke registry at two academic Comprehensive Stroke Centers (CSC) between 1/1/2013 and 6/30/2024. We included patients with new diagnosis of AIS or ICH with motor hemiparesis. Patients with initial NIHSS 0 and baseline mRS >2 were excluded. A correlation matrix was constructed followed by stepwise linear regression. Logistic regression modeling was then used for any variables with p<0.10. Chi squared was used to examine nominal variables.

Results:
A total of 745 patients met study criteria, and 167 (22.4%) patients were prescribed antidepressants prior to discharge (AIS=137; ICH=30). Variables that were significantly correlated with prescribing antidepressants during stroke hospitalization were White race (p<0.001), length of stay (LOS) (p<0.001), discharge disposition to acute rehabilitation unit (p=0.02), history of smoking (p=0.002), history of substance abuse (p=0.006), history of depression (p<0.001), dementia (p=0.002), and patients with no medical history (p=0.02). Logistic regression model including LOS (p<0.001), history of depression (p=0.03), and no medical history (p=0.04) was significant in predicting prescribing antidepressants (p<0.001; r=0.30; r²=0.09).

Conclusion:
In patients hospitalized for AIS or hemorrhagic stroke with motor weakness, key demographic factors, LOS, and discharge to acute rehabilitation units were significant predictors for prescribing antidepressants prior to discharge. The independent predictors in our model account for 9% of the variance in prescribing antidepressants, suggesting that further studies are needed to understand other factors that may influence antidepressant prescribing patterns during stroke hospitalization.