Effect of Paxlovid treatment during acute COVID-19 on Long COVID onset: An EHR-based target trial emulation from the N3C and RECOVER Consortia

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Published

April 2025

Journal

medRxiv

Abstract

Background: Preventing and treating post-acute sequelae of SARS-CoV-2 infection (PASC), commonly known as Long COVID, has become a public health priority. Researchers have begun to explore whether Paxlovid treatment in the acute phase of COVID-19 could help prevent the onset of PASC. Methods and findings: We used electronic health records from the National Clinical Cohort Collaborative (N3C) to define a cohort of 410,026 patients who had COVID-19 since April 1, 2022, and were eligible for Paxlovid treatment due to risk for progression to severe COVID-19. We used the target trial emulation framework to estimate the effect of Paxlovid treatment on PASC incidence. The treatment group was defined as outpatients prescribed Paxlovid within five days of COVID-19 index, and the control group was defined as all patients meeting eligibility criteria not in the treatment group. The follow-up period was 180 days. We estimated overall PASC incidence using a computable phenotype. We also measured incident cognitive, fatigue, and respiratory symptoms in the post-acute period. Paxlovid treatment had a small effect on overall PASC incidence (relative risk [RR] 0.94; 95% CI [0.90, 0.99]; p=0.011). It had a slightly stronger protective effect against cognitive (RR 0.86; 95% CI [0.77, 0.95]; p<0.001) and fatigue (RR 0.92; 95% CI [0.86, 0.97]; p=0.002) symptoms. Conclusions: In this study, Paxlovid had a weaker preventative effect on PASC than in prior observational studies, suggesting that Paxlovid is unlikely to become a definitive solution for preventing PASC. Differing effects by symptom cluster suggest that the etiology of cognitive and fatigue symptoms may be more closely related to viral load than that of respiratory symptoms. Future research should explore potential heterogeneous treatment effects across PASC subphenotypes. Author summary: Why was this study done?: Paxlovid is indicated to prevent severe COVID-19.Long COVID is more likely after more severe COVID-19, so there is a plausible mechanism for Paxlovid to reduce the risk of developing Long COVID by preventing severe COVID-19.Only a few studies have examined the relationship between Paxlovid and Long COVID, with mixed results. To our knowledge, no studies have used causal inference methods to estimate Paxlovid's effect on PASC.If Paxlovid helps prevent Long COVID, it could be a powerful addition to the public health effort to reduce the burden of COVID-19.What did the researchers do and find?: We used a cohort of 410,026 patients from the National Covid Cohort Collaborative's (N3C's) electronic health record database to estimate the effect of Paxlovid treatment during acute COVID-19 on the likelihood of developing Long COVID.We used the target trial emulation technique to estimate the causal effect of Paxlovid treatment using observational data.We found that Paxlovid treatment had a small effect on overall Long COVID incidence.We found that Paxlovid treatment had a slightly stronger effect on certain symptoms (cognitive and fatigue) of Long COVID.What do these findings mean?: Paxlovid is unlikely to become a definitive solution for preventing Long COVID.However, Paxlovid may have a stronger effect on certain subtypes of Long COVID.This difference in effect by symptom also suggests that viral load may be a more common cause of cognitive and fatigue symptoms than of other Long COVID symptoms.This study's main limitation is that it estimates causal effects, but it is not randomized like a clinical trial. Instead, we control for other variables which could bias the estimate, but if we missed some important variables, the estimates could be incorrect. 

Authors

Alexander Preiss, Abhishek Bhatia, Leyna V Aragon, John M Baratta, Monika Baskaran, Frank Blancero, M Daniel Brannock, Robert F Chew, Iván Díaz, Megan Fitzgerald, Elizabeth P Kelly, Andrea Zhou, Thomas W Carton, Christopher G Chute, Melissa Haendel, Richard Moffitt, Emily Pfaff, N3C Consortium and the RECOVER EHR Cohort

Keywords

Not available