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BackgroundStratification of patients with post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) would allow precision clinical management strategies. However, long COVID is incompletely understood and characterised by a wide range of manifestations that are difficult to analyse computationally. Additionally, the generalisability of machine learning classification of COVID-19 clinical outcomes has rarely been tested.MethodsWe present a method for computationally modelling PASC phenotype data based on electronic healthcare records (EHRs) and for assessing pairwise phenotypic similarity between patients using semantic similarity. Our approach defines a nonlinear similarity function that maps from a feature space of phenotypic abnormalities to a matrix of pairwise patient similarity that can be clustered using unsupervised machine learning.FindingsWe found six clusters of PASC patients, each with distinct profiles of phenotypic abnormalities, including clusters with distinct pulmonary, neuropsychiatric, and cardiovascular abnormalities, and a cluster associated with broad, severe manifestations and increased mortality. There was significant association of cluster membership with a range of pre-existing conditions and measures of severity during acute COVID-19. We assigned new patients from other healthcare centres to clusters by maximum semantic similarity to the original patients, and showed that the clusters were generalisable across different hospital systems. The increased mortality rate originally identified in one cluster was consistently observed in patients assigned to that cluster in other hospital systems.InterpretationSemantic phenotypic clustering provides a foundation for assigning patients to stratified subgroups for natural history or therapy studies on PASC.FundingNIH (TR002306/OT2HL161847-01/OD011883/HG010860), U.S.D.O.E. (DE-AC02-05CH11231), Donald A. Roux Family Fund at Jackson Laboratory, Marsico Family at CU Anschutz.

Background: Nirmatrelvir with ritonavir (Paxlovid) is indicated for patients with Coronavirus Disease 2019 (COVID-19) who are at risk for progression to severe disease due to the presence of one or more risk factors. Millions of treatment courses have been prescribed in the United States alone. Paxlovid was highly effective at preventing hospitalization and death in clinical trials. Several studies have found a protective association in real-world data, but they variously used less recent study periods, correlational methods, and small, local cohorts. Their estimates also varied widely. The real-world effectiveness of Paxlovid remains uncertain, and it is unknown whether its effect is homogeneous across demographic strata. This study leverages electronic health record data in the National COVID Cohort Collaborative's (N3C) repository to investigate disparities in Paxlovid treatment and to emulate a target trial assessing its effectiveness in reducing severe COVID-19 outcomes. Methods and findings: This target trial emulation used a cohort of 703,647 patients with COVID-19 seen at 34 clinical sites across the United States between April 1, 2022 and August 28, 2023. Treatment was defined as receipt of a Paxlovid prescription within 5 days of the patient's COVID-19 index date (positive test or diagnosis). To emulate randomization, we used the clone-censor-weight technique with inverse probability of censoring weights to balance a set of covariates including sex, age, race and ethnicity, comorbidities, community well-being index (CWBI), prior healthcare utilization, month of COVID-19 index, and site of care provision. The primary outcome was hospitalization; death was a secondary outcome. We estimated that Paxlovid reduced the risk of hospitalization by 39% (95% confidence interval (CI) [36%, 41%]; p < 0.001), with an absolute risk reduction of 0.9 percentage points (95% CI [0.9, 1.0]; p < 0.001), and reduced the risk of death by 61% (95% CI [55%, 67%]; p < 0.001), with an absolute risk reduction of 0.2 percentage points (95% CI [0.1, 0.2]; p < 0.001). We also conducted stratified analyses by vaccination status and age group. Absolute risk reduction for hospitalization was similar among patients that were vaccinated and unvaccinate, but was much greater among patients aged 65+ years than among younger patients. We observed disparities in Paxlovid treatment, with lower rates among black and Hispanic or Latino patients, and within socially vulnerable communities. This study's main limitation is that it estimates causal effects using observational data and could be biased by unmeasured confounding. Conclusions: In this study of Paxlovid's real-world effectiveness, we observed that Paxlovid is effective at preventing hospitalization and death, including among vaccinated patients, and particularly among older patients. This remains true in the era of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Omicron subvariants. However, disparities in Paxlovid treatment rates imply that the benefit of Paxlovid's effectiveness is not equitably distributed. Abhishek Bhatia and team estimate the real-world effectiveness of Paxlovid and examine treatment disparities using target trial emulation on a cohort of 703,647 patients from N3C's electronic health record database. Author summary: Why was this study done?: Paxlovid is widely used to prevent severe Coronavirus Disease 2019 (COVID-19), but its real-world effectiveness is uncertain. Prior studies differed in their findings, and none of them used a large, nationally sampled, and recent cohort. It is unknown whether Paxlovid may be more or less effective in certain demographic groups. What did the researchers do and find?: We used a cohort of 703,647 patients from the National Covid Cohort Collaborative's (N3C's) electronic health record database to estimate Paxlovid's real-world effectiveness and investigate disparities in treatment. We used the target trial emulation technique to estimate the causal effect of Paxlovid treatment using observational data. We found that Paxlovid treatment reduced the risk of hospitalization by 39% and the risk of death by 61%. We found that Paxlovid reduced the risk of hospitalization by a similar absolute amount regardless of whether the patient was vaccinated prior to getting COVID-19. We found that Paxlovid reduced risk more among patients aged 65+ years than among younger patients. We found disparities in Paxlovid treatment, with lower rates among black and Hispanic or Latino patients, and within socially vulnerable communities. What do these findings mean?: Paxlovid is effective at preventing severe COVID-19 and is effective against current variants. Fully vaccinated people may have a further reduced risk of severe outcomes by receiving Paxlovid. People aged 65 years or older may benefit the most from Paxlovid treatment. However, disparities in Paxlovid treatment rates imply that the benefit of Paxlovid's effectiveness is not equitably distributed. 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. [ABSTRACT FROM AUTHOR]

We compare the clinical trial success rates of products receiving US Food and Drug Administration (FDA) accelerated approval (AA) to those approved without using this pathway. Our findings raise important questions about how the AA pathway can best optimize early access to therapeutics that are ultimately proven safe and effective. [ABSTRACT FROM AUTHOR]

Purpose: Paxlovid is effective in reducing COVID‐19 hospitalization and mortality. This study characterized Paxlovid use and evaluated racial/ethnic disparities over time among community‐dwelling adults at high risk of progression to severe COVID‐19 disease. Methods: This retrospective cohort study used the National COVID Cohort Collaborative (N3C) data and included individuals aged 18 years or older diagnosed with COVID‐19 between January 2022 and December 2023. The study cohort included nonhospitalized individuals who were at high risk of COVID‐19 progression, and selected the first COVID‐19 episode in each quarter, including reinfection episodes. Paxlovid use was defined as receiving Paxlovid within ±5 days of a COVID‐19 diagnosis. We used descriptive statistics to characterize Paxlovid use overall and by calendar quarter and race/ethnicity. We used a generalized estimating equations (GEE) models to quantify the association of race/ethnicity with Paxlovid use controlling for age, gender, and clinical characteristics. Results: Among 1 264 215 individuals at high risk of disease progression (1 404 607 episodes), Paxlovid use increased from 1.2% in January–March 2022 to 35.1% in October–December 2023. Paxlovid use was more common among non‐Hispanic White individuals (23.9%) than non‐Hispanic Black (16.5%) and Latinx/e (16.7%) patients. After adjusting age, gender, and clinical characteristics, Paxlovid use was less likely among non‐Hispanic Black (odds ratio [OR] 0.69, 95% confidence interval [CI] 0.68–0.70) and Latinx/e (OR 0.72, CI 0.71–0.73) patients than non‐Hispanic White patients. Conclusions: Among a large, diverse cohort of community‐dwelling individuals with COVID‐19, nearly two out of three eligible individuals did not receive Paxlovid, and minoritized racial/ethnic groups were less likely to use Paxlovid than their non‐Hispanic White individuals. [ABSTRACT FROM AUTHOR]