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0 0.5 1 1.5 2+ PASC, SSRI 10% Improvement Relative Risk PASC, fluoxetine 15% SSRI for COVID-19  Butzin-Dozier et al.  Prophylaxis Is prophylaxis with SSRI beneficial for COVID-19? Retrospective study in the USA (September 2021 - December 2022) Lower PASC with SSRI (p=0.000009) Butzin-Dozier et al., medRxiv, February 2024 Favors SSRI Favors control

SSRI Use During Acute COVID-19 Infection Associated with Lower Risk of Long COVID Among Patients with Depression

Butzin-Dozier et al., medRxiv, doi:10.1101/2024.02.05.24302352
Feb 2024  
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27th treatment shown to reduce risk in November 2021
*, now known with p = 0.00011 from 20 studies, recognized in 3 countries.
No treatment is 100% effective. Protocols combine complementary and synergistic treatments. * >10% efficacy in meta analysis with ≥3 clinical studies.
3,900+ studies for 60+ treatments.
N3C retrospective 506,903 outpatients with depression showing lower risk of long COVID with SSRI use. There was insufficient sample size to analyze the subgroup of fluvoxamine users.
risk of PASC, 9.9% lower, OR 0.90, p < 0.001, adjusted per study, SSRI, multivariable, RR approximated with OR.
risk of PASC, 15.3% lower, OR 0.85, p = 0.002, adjusted per study, fluoxetine, multivariable, RR approximated with OR.
Effect extraction follows pre-specified rules prioritizing more serious outcomes. Submit updates
Butzin-Dozier et al., 6 Feb 2024, retrospective, USA, preprint, 11 authors, study period 1 September, 2021 - 1 December, 2022.
This PaperFluvoxamineAll
SSRI Use During Acute COVID-19 Infection Associated with Lower Risk of Long COVID Among Patients with Depression
PhD, MPH Zachary Butzin-Dozier, Yunwen Ji, Sarang Deshpande, Eric Hurwitz, Jeremy Coyle, Junming (seraphina) Shi, Andrew Mertens, Mark J Van Der Laan, John M Colford Jr, Rena C Patel, Alan E Hubbard
Background: Long COVID, also known as post-acute sequelae of COVID-19 (PASC), is a poorly understood condition with symptoms across a range of biological domains that often have debilitating consequences. Some have recently suggested that lingering SARS-CoV-2 virus in the gut may impede serotonin production and that low serotonin may drive many Long COVID symptoms across a range of biological systems. Therefore, selective serotonin reuptake inhibitors (SSRIs), which increase synaptic serotonin availability, may prevent or treat Long COVID. SSRIs are commonly prescribed for depression, therefore restricting a study sample to only include patients with depression can reduce the concern of confounding by indication. Methods: In an observational sample of electronic health records from patients in the National COVID Cohort Collaborative (N3C) with a COVID-19 diagnosis between September 1, 2021, and December 1, 2022, and pre-existing major depressive disorder, the leading indication for SSRI use, we evaluated the relationship between SSRI use at the time of COVID-19 infection and subsequent 12-month risk of Long COVID (defined by ICD-10 code U09.9). We defined SSRI use as a prescription for SSRI medication beginning at least 30 days before COVID-19 infection and not ending before COVID-19 infection. To minimize bias, we estimated the causal associations of interest using a nonparametric approach, targeted maximum likelihood estimation, to aggressively adjust for highdimensional covariates. Results: We analyzed a sample (n = 506,903) of patients with a diagnosis of major depressive disorder before COVID-19 diagnosis, where 124,928 (25%) were using an SSRI. We found that SSRI users had a significantly lower risk of Long COVID compared to nonusers (adjusted causal relative risk 0.90, 95% CI (0.86, 0.94)). Conclusion: These findings suggest that SSRI use during COVID-19 infection may be protective against Long COVID, supporting the hypothesis that serotonin may be a key mechanistic biomarker of Long COVID.
Disclaimer The N3C Publication committee confirmed that this manuscript (MSID:1784.118) is in accordance with N3C data use and attribution policies; however, this content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the N3C program. IRB The N3C data transfer to NCATS is performed under a Johns Hopkins University Reliance Protocol # IRB00249128 or individual site agreements with NIH. The N3C Data Enclave is managed under the authority of the NIH; information can be found at This research project was approved by the University of California, Berkeley Committee for the Protection of Human Subjects (CPHS protocol number 2022-01-14980). This approval is issued under University of California, Berkeley Federalwide Assurance #00006252. Individual Acknowledgements For Core Contributors We gratefully acknowledge the following core contributors to N3C: Adam B. Wilcox, Adam M. Lee, Alexis Graves, Alfred (Jerrod) Anzalone, Amin Manna, Amit Saha, Amy Olex, Andrea Zhou, Andrew E. Williams, Andrew Southerland, Andrew T. Girvin, Anita Walden, Anjali A. Sharathkumar, Benjamin Amor, Benjamin Bates, Brian Hendricks, Brijesh Patel, Caleb Alexander, Carolyn Bramante, Cavin Ward-Caviness, Charisse Madlock-Brown, Christine Suver, Christopher Chute, Christopher Dillon, Chunlei Wu, Clare Schmitt, Cliff Takemoto, Dan Housman, Davera Gabriel, David A. Eichmann, Diego..
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