Use of nirmatrelvir and severe consequences of Covid-19 during Omicron surge

study design

This observational, retrospective cohort study was based on data obtained from electronic medical records of members of Clalit Health Services (CHS), a large healthcare organization that covers approximately 52% of the entire Israeli population and nearly two-thirds of the elderly. The study period began on January 9, 2022, which was the first day nirmatrelvir was administered to CHS members, and ended on March 31, 2022. During the study period, the variant omicron was the dominant strain in Israel (see Fig. S1 in the Supplementary Appendix, available with the full text of this article on NEJM.org).

Study population

The study population included all CHS members aged 40 or older, who had confirmed SARS-CoV-2 infection, had been diagnosed with Covid-19 on an outpatient basis, had been assessed as at high risk of progression to serious disease and were deemed eligible to receive treatment with nirmatrelvir. High-risk patients were identified based on a risk model developed by the CHS to assess the risk of severe Covid-19 in patients infected with SARS-CoV-2; details are provided in the supplementary annex. Patients were included in the study cohort if they had a risk score of at least 2 points; details are provided in Table S1. Patients were eligible for inclusion if diagnosed with Covid-19 on or before February 24, 2022. Eligibility to receive antiviral treatment considered drug interactions and other contraindications, as described in the nirmatrelvir prescribing information.3 For each patient, follow-up ended at the earliest of the following times: 35 days after diagnosis of Covid-19, the end of the study period, or the time of data censoring if the patient died during the study period for reasons unrelated to Covid-19.

Most patients who were tested for Covid-19 during the study period underwent such tests due to the onset of symptoms. Polymerase chain reaction (PCR) and state-regulated antigen testing were available free of charge upon patient request. However, no screening for SARS-CoV-2 was performed, even when a patient had been exposed to someone with confirmed Covid-19. CHS policy stated that antiviral therapy should be initiated in eligible patients as soon as possible after testing positive for SARS-CoV-2, in accordance with FDA prescribing information.3 Each CHS district was responsible for administering nirmatrelvir treatment in patients’ homes and verifying adherence to the treatment regimen. High-risk patients who had a contraindication to nirmatrelvir were offered treatment with molnupiravir, which was available in Israel from January 16, 2022. Patients who resided in long-term care facilities and patients who had been hospitalized before or on the same day as a positive SARS-CoV-2 test were excluded from the study, as were patients who received treatment with molnupiravir or the anti-SARS-CoV-2 monoclonal antibodies tixagevimab and cilgavimab.

The study was approved by the CHS Community Helsinki and Data Utilization committees. The study was exempt from the requirement to obtain informed consent due to the retrospective design.

Data sources and organization

We assessed the integrated patient-level data that was maintained by the CHS from two main sources: the primary care operational database and the Covid-19 database. The operational database includes comprehensive sociodemographic and clinical information, such as coexisting chronic conditions, community care visits, medications, and lab test results. The Covid-19 database includes results from PCR and state-regulated rapid antigen tests, vaccinations, hospitalizations and deaths related to Covid-19. These same databases were used in the primary studies that evaluated the efficacy of the BNT162b2 vaccine (Pfizer-BioNTech) in a real setting in Israel.6.7 A description of the data repositories that were used in this study is provided in the supplementary appendix. For each patient in the study, the following sociodemographic data were extracted: age, sex, sector of the population (generalist Jew, ultra-Orthodox Jew or Arab) and socioeconomic status score (ranging from 1 [lowest] at 10 [highest]; details are provided in the additional annex). The following clinical data were extracted: Covid-19 vaccination dates, dates and results of state-regulated PCR and rapid antigen tests, Covid-19 antiviral therapies, hospitalizations and deaths. Data regarding the following clinical risk factors for severe Covid-19 were also collected: immunosuppression, diabetes mellitus, asthma, hypertension, neurological disease, current cancer disease, chronic liver disease, chronic obstructive pulmonary disease, chronic renal failure, chronic heart disease, obesity. , history of stroke or smoking, and recent hospitalizations (within the previous 3 years) for any cause. Additionally, estimated glomerular filtration rate was extracted when available.

Study results

The primary endpoint of the study was hospitalization due to Covid-19. The secondary outcome was death due to Covid-19.

Subgroup analyzes of primary and secondary outcomes were performed to determine the effect of SARS-CoV-2 immunity status. Patients were divided into two categories according to their immune status: those who had previously acquired prior immunity (vaccine-induced, infection-induced, or a hybrid of the two) and those who did not have prior immunity (non-immune). vaccinated or vaccinated with a single mRNA dose of vaccine and no prior documented infection with SARS-CoV-2). This classification was based on Israeli Health Ministry guidelines, which refer to people who receive only a single dose of mRNA vaccine and unvaccinated people as having similar immunity.

Statistical analyzes

All eligible members of the CHS were included in the analysis, in accordance with the study design. Descriptive statistics were used to characterize the patients in the study. Since the independent variable (nirmatrelvir treatment) varied over time, univariate and multivariate survival analyzes were performed with time-dependent covariates.

For patients who did not receive nirmatrelvir treatment, time zero was when each patient was diagnosed with Covid-19. For patients who received treatment with nirmatrelvir, time zero was when a patient started treatment. In order to avoid an immortal temporal bias,8 we performed a time-dependent analysis in which a time-varying covariate was used to indicate the start of treatment for each treated patient. In this analysis, patients who received nirmatrelvir were transferred from the ‘untreated’ at-risk set to the ‘treated’ at-risk set at the start of treatment, changing their treatment status from untreated to treated. Therefore, follow-up of nirmatrelvir-treated patients began at the end of the immortal period.

A sensitivity analysis assessed the magnitude of the nirmatrelvir treatment effect from day 3 of follow-up by excluding patients who were hospitalized within 2 days of the start of follow-up. This approach allowed comparability with the EPIC-HR trial, in which patients were excluded if the need for hospitalization within 2 days of randomisation was anticipated.4

The association between nirmatrelvir treatment and Covid-19 outcomes was estimated using a multivariate Cox proportional hazards regression model with time-dependent covariates; adjustment was made for socio-demographic factors and co-existing conditions. Since many clinical and sociodemographic factors are potential confounders, two-step test criteria were applied for the selection of covariates. First, a Kaplan-Meier univariate analysis with a log-rank test was applied to assess the associations between each independent candidate variable and the time-dependent primary outcome. Then, a comparison of survival curves and Schoenfeld’s global test was used to test the proportional hazards hypothesis for these variables. Variables that met these two test criteria served as inputs for the multivariate regression analysis. An additional multivariate Cox proportional hazards regression model was used to estimate the association between each of the covariates and taking nirmatrelvir treatment.

Statistical software R, version 3.5.0 (R Foundation), was used for univariate and multivariate survival analyzes with time-dependent covariates. SPSS software, version 26 (IBM), was used for all other statistical analyses.

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