COVID-19 has placed a monumental burden on the health care system globally. Although no longer a public health emergency, there is still a pressing need for effective treatments that prevent adverse outcomes associated with this disease. Nirmatrelvir/ritonavir (NMV-R) is a promising and potentially effective antiviral, which until recently was under emergency use authorization. Our objective was to evaluate the real-world effectiveness of NMV-R in preventing severe illness, hospitalization, death and long-COVID in a large nationwide cohort of outpatients with COVID-19.
Population-based retrospective cohort study of patients with a SARS-CoV-2 positive test or diagnosis (index) date between December 2021 and February 2023 within the National COVID Cohort Collaborative (N3C), with at least one risk factor for severe COVID-19, no evidence of contraindicated medical conditions or medication use, and no hospital or emergency department visit or death within 24 hours of eligibility. We emulated a sequence of target trials beginning on each of the first five days of diagnosis with COVID-19. We identified 921,034 eligible person-trials (each representing a patient’s eligibility at a given diagnosis day across sequential emulated trials), of which 77,449 were initiators and 846,585 were non-initiators of NMV-R treatment. NMV-R Initiators were matched to non-initiators in each trial. The marginal hazard ratio between initiators and non-initiators was estimated for four acute outcomes: severe illness, hospitalization or death, hospitalization, and death; and the post-COVID condition or long COVID.
Of 921,034 eligible “person-trials”, 74,449 were initiators and 846,585 were non-initiators of NMV-R treatment. Pooled across trials, the hazard for severe illness (HR: 0.76, 95% CI: 0.71 to 0.81), hospitalization or death (HR: 0.50, 95% CI: 0.44 to 0.57), hospitalization (sdHR: 0.52, 95% CI: 0.46 to 0.60), death (HR: 0.33, 95% CI: 0.21 to 0.51), and long-COVID (sdHR: 0.85, 95% CI: 0.77 to 0.95) were significantly lower among NMV-R initiators compared to non-initiators. Results further indicated larger associations between NMV-R and reduced risk of both acute and post-acute outcomes with early versus delayed NMV-R treatment initiation, and among unvaccinated versus vaccinated patient subgroups.
NMV-R is overall effective at preventing the risk of severe acute outcomes including hospitalization and death, as well as long COVID. Results were robust across multiple sensitivity considerations.
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Steve R Makkar, Kristen Hansen, Arjun S. Yadaw et al.· BMC Infectious Diseases· 0 citations
Adequately powered analyses in precision oncology often require combining cohorts across institutions. Yet integration is constrained by the least granular source and may become infeasible when data elements are too heterogeneous to harmonize and map to a common data model. This challenge is acute in multi-institutional precision oncology research, where real-world evidence requires harmonized clinico-omic data integration. Existing models often lack sufficient treatment patterns, outcomes, and genomic data, limiting interoperability and scalability. To address these gaps, AACR Project GENIE™ (Genomics Evidence Neoplasia Information Exchange) developed the GENIE Data Model (GDM), a comprehensive, open-source, oncology data model for scalable, consistent, and interoperable data collection across solid tumors designed to effectively capture the patient's journey with cancer. Through iterative consensus-building, four working groups comprising 13 subject matter experts defined data elements across multiple clinical domains: patient characteristics, imaging, diagnosis, surgery, histopathology, biomarkers, systemic therapy, radiation, clinical trial history, disease response and outcomes, and social determinants of health. Elements were defined using standardized terminologies and permissible values to support mapping to HL7 FHIR, OMOP, and other existing oncology standards. The model architecture distinguishes manually abstracted elements from computationally collected elements, enabling parallel workflows. The GDM provides an extensible framework that addresses critical gaps and enables scalable, harmonized data collection essential for precision oncology and real-world evidence generation.
J. Hoppe, Jocelyn Lee, Tomi F. Akinyemiju et al.· Cancer Research Communicatio...· 0 citations