Nirmatrelvir Plus Ritonavir for Early COVID-19 in a Large U.S. Health System : A Population-Based Cohort Study.

BACKGROUND: In the EPIC-HR (Evaluation of Protease Inhibition for Covid-19 in High-Risk Patients) trial, nirmatrelvir plus ritonavir led to an 89% reduction in hospitalization or death among unvaccinated outpatients with early COVID-19. The clinical impact of nirmatrelvir plus ritonavir among vaccinated populations is uncertain. OBJECTIVE: To assess whether nirmatrelvir plus ritonavir reduces risk for hospitalization or death among outpatients with early COVID-19 in the setting of prevalent SARS-CoV-2 immunity and immune-evasive SARS-CoV-2 lineages. DESIGN: Population-based cohort study analyzed to emulate a clinical trial using inverse probability-weighted models to account for anticipated bias in treatment. SETTING: A large health care system providing care for 1.5 million patients in Massachusetts and New Hampshire during the Omicron wave (1 January to 17 July 2022). PATIENTS: 44 551 nonhospitalized adults (90.3% with ≥3 vaccine doses) aged 50 years or older with COVID-19 and no contraindications for nirmatrelvir plus ritonavir. MEASUREMENTS: The primary outcome was a composite of hospitalization within 14 days or death within 28 days of a COVID-19 diagnosis. RESULTS: During the study period, 12 541 (28.1%) patients were prescribed nirmatrelvir plus ritonavir, and 32 010 (71.9%) were not. Patients prescribed nirmatrelvir plus ritonavir were more likely to be older, have more comorbidities, and be vaccinated. The composite outcome of hospitalization or death occurred in 69 (0.55%) patients who were prescribed nirmatrelvir plus ritonavir and 310 (0.97%) who were not (adjusted risk ratio, 0.56 [95% CI, 0.42 to 0.75]). Recipients of nirmatrelvir plus ritonavir had lower risk for hospitalization (adjusted risk ratio, 0.60 [CI, 0.44 to 0.81]) and death (adjusted risk ratio, 0.29 [CI, 0.12 to 0.71]). LIMITATION: Potential residual confounding due to differential access to COVID-19 vaccines, diagnostic tests, and treatment. CONCLUSION: The overall risk for hospitalization or death was already low (1%) after an outpatient diagnosis of COVID-19, but nirmatrelvir plus ritonavir reduced this risk further. PRIMARY FUNDING SOURCE: National Institutes of Health.

Lessons for Oncology From the COVID-19 Pandemic: Operationalizing and Scaling Virtual Cancer Care in Health Systems.

ABSTRACT: After several decades of slow expansion, the use of virtual care in oncology rapidly expanded during the COVID-19 pandemic. Data from cancer centers across the country show that most patients and providers were satisfied with components of virtual care, and virtual care may be able to improve access to care. However, the rapid implementation of programs during the pandemic worsened disparities in access to virtual care. Health systems must develop strategies to monitor quality, support patients and providers, promote health equity, and overcome regulatory challenges to successfully deliver care in hybrid systems that combine in-person and virtual care.

Coronavirus Disease 2019 Infection in a Patient Population with Lung Cancer: Incidence, Presentation, and Alternative Diagnostic Considerations.

INTRODUCTION: Lung cancer is associated with severe coronavirus disease 2019 (COVID-19) infections. Symptom overlap between COVID-19 and lung cancer may complicate diagnostic evaluation. We aimed to investigate the incidence, symptoms, differential diagnosis, and outcomes of COVID-19 in patients with lung cancer. METHODS: To determine an at-risk population for COVID-19, we retrospectively identified patients with lung cancer receiving longitudinal care within a single institution in the 12 months (April 1, 2019 to March 31, 2020) immediately preceding the COVID-19 pandemic, including an "active therapy population" treated within the last 60 days of this period. Among patients subsequently referred for COVID-19 testing, we compared symptoms, laboratory values, radiographic findings, and outcomes of positive versus negative patients. RESULTS: Between April 1, 2019 and March 31, 2020, a total of 696 patients received longitudinal care, including 406 (58%) in the active therapy population. Among 55 patients referred for COVID-19 testing, 24 (44%) were positive for COVID-19, representing a cumulative incidence of 3.4% (longitudinal population) and 1.5% (active therapy population). Compared with patients who were COVID-19 negative, those who were COVID-19 positive were more likely to have a supplemental oxygen requirement (11% versus 54%, p = 0.005) and to have typical COVID-19 pneumonia imaging findings (5 versus 56%, p = 0.001). Otherwise, there were no marked differences in presenting symptoms. Among patients who were COVID-19 negative, alternative etiologies included treatment-related toxicity (26%), atypical pneumonia (22%), and disease progression (22%). A total of 16 patients positive for COVID-19 (67%) required hospitalization, and seven (29%) died from COVID-related complications. CONCLUSIONS: COVID-19 was infrequent in this lung cancer population, but these patients experienced high rates of morbidity and mortality. Oncologists should maintain a low threshold for COVID-19 testing in patients with lung cancer presenting with acute symptoms.

Digital triage: Novel strategies for population health management in response to the COVID-19 pandemic.

The COVID-19 pandemic has created unique challenges for the U.S. healthcare system due to the staggering mismatch between healthcare system capacity and patient demand. The healthcare industry has been a relatively slow adopter of digital innovation due to the conventional belief that humans need to be at the center of healthcare delivery tasks. However, in the setting of the COVID-19 pandemic, artificial intelligence (AI) may be used to carry out specific tasks such as pre-hospital triage and enable clinicians to deliver care at scale. Recognizing that the majority of COVID-19 cases are mild and do not require hospitalization, Partners HealthCare (now Mass General Brigham) implemented a digitally-automated pre-hospital triage solution to direct patients to the appropriate care setting before they showed up at the emergency department and clinics, which would otherwise consume resources, expose other patients and staff to potential viral transmission, and further exacerbate supply-and-demand mismatching. Although the use of AI has been well-established in other industries to optimize supply and demand matching, the introduction of AI to perform tasks remotely that were traditionally performed in-person by clinical staff represents a significant milestone in healthcare operations strategy.