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0 0.5 1 1.5 2+ PASC 1% Improvement Relative Risk Cognitive symptom cluster 15% Fatigue symptom cluster 7% Respiratory symptom clu.. 1% Paxlovid  Preiss et al.  EARLY TREATMENT  LONG COVID Does paxlovid reduce the risk of Long COVID (PASC)? Retrospective 426,461 patients in the USA (January - December 2022) No significant difference in PASC Preiss et al., medRxiv, January 2024 Favors paxlovid Favors control

Effect of Paxlovid Treatment on Long COVID Onset: An EHR-Based Target Trial Emulation from N3C

Preiss et al., medRxiv, doi:10.1101/2024.01.20.24301525
Jan 2024  
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Retrospective study of 426,461 high-risk outpatients showing no significant difference in post-acute sequelae of COVID-19 (PASC) incidence with paxlovid treatment. Subgroup analysis showed benefits for cognitive and fatigue symptoms. The study used a large, nationally representative sample and a machine learning model to identify PASC cases.
Confounding by contraindication. Hoertel et al. find that over 50% of patients that died had a contraindication for the use of Paxlovid Hoertel. Retrospective studies that do not exclude contraindicated patients may significantly overestimate efficacy.
Black box warning. The FDA notes that "severe, life-threatening, and/or fatal adverse reactions due to drug interactions have been reported in patients treated with paxlovid" FDA.
risk of PASC, 1.0% lower, RR 0.99, p = 0.45, adjusted per study.
cognitive symptom cluster, 15.0% lower, RR 0.85, p < 0.001, adjusted per study.
fatigue symptom cluster, 7.0% lower, RR 0.93, p < 0.001, adjusted per study.
respiratory symptom cluster, 1.0% lower, RR 0.99, p = 0.59, adjusted per study.
Effect extraction follows pre-specified rules prioritizing more serious outcomes. Submit updates
Preiss et al., 22 Jan 2024, retrospective, USA, preprint, 23 authors, study period 3 January, 2022 - 31 December, 2022. Contact:
This PaperPaxlovidAll
Effect of Paxlovid Treatment on Long COVID Onset: An EHR-Based Target Trial Emulation from N3C
Alexander Preiss, Abhishek Bhatia, Chengxi Zang, Leyna V Aragon, John M Baratta, Monika Baskaran, Frank Blancero, M Daniel Brannock, Robert F Chew, Ivan Diaz, Megan Fitzgerald, Elizabeth P Kelly, Andrea G Zhou, Mark G Weiner, Thomas W Carton, Fei Wang, Rainu Kaushal, Christopher G Chute, Melissa Haendel, Richard Moffitt, Emily Pfaff
Preventing and treating post-acute sequelae of SARS-CoV-2 infection (PASC), commonly known as Long COVID, has become a public health priority. In this study, we examined whether treatment with Paxlovid in the acute phase of COVID-19 helps prevent the onset of PASC. We used electronic health records from the National Covid Cohort Collaborative (N3C) to define a cohort of 426,461 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 (TTE) framework to estimate the effect of Paxlovid treatment on PASC incidence. Our primary outcome measure was a PASC computable phenotype. Secondary outcomes were the onset of novel cognitive, fatigue, and respiratory symptoms in the post-acute period. Paxlovid treatment did not have a significant effect on overall PASC incidence (relative risk [RR] = 0.99, 95% confidence interval [CI] 0.96-1.01). However, its effect varied across the cognitive (RR = 0.85, 95% CI 0.79-0.90), fatigue (RR = 0.93, 95% CI 0.89-0.96), and respiratory (RR = 0.99, 95% CI 0.95-1.02) symptom clusters, suggesting that Paxlovid treatment may help prevent post-acute cognitive and fatigue symptoms more than others.
SUPPLEMENT A -ADDITIONAL TABLES AND FIGURES sleep wake disorders, dyspnea (or shortness of breath), acute pharyngitis, hair loss, edema, fever, malaise and fatigue. 66, 67 The incident post-acute sequelae of SARS-CoV-2 (PASC) symptoms from the list above was observed during the post-acute phase (30 to 180 days after the index date), while absent during the baseline period (7 days to 3 years preceding the index date). Individuals classified as having incident PASC when he/she exhibited at least one incident PASC-related symptom during the post-acute phase. The same set of covariates as N3C were built for the adjustment analyses, including age, self-reported sex, race/ethnicity, CCI score, baseline hospitalization utilization, social-economic status and infection time. The social-economic status was quantified by the national-level area deprivation index (ADI) linked by either 9-digit zip code or geocode. The IPTW re-weighted survival analyses, including Cox proportional hazard model for the relative risk, and the Aalen-Johansen model for the cumulative incidence. Both methods considered the death to be a competing risk for the target incident outcomes. The IPTW weights were learned based on method from Zang et al. 68 Results Table
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