Nirmatrelvir Use and Severe Covid-19 Outcomes during the Omicron Surge

Study Design

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

Study Population

The study population comprised all CHS members who were 40 years of age or older, had confirmed SARS-CoV-2 infection, received a diagnosis of Covid-19 as outpatients, were assessed as being at high risk for progression to severe disease, and were deemed eligible to receive nirmatrelvir therapy. High-risk patients were identified on the basis of a risk model that was developed by CHS to evaluate the risk of severe Covid-19 in patients infected with SARS-CoV-2; details are provided in the Supplementary Appendix. 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 they received the Covid-19 diagnosis on or before February 24, 2022. Eligibility for receipt of the antiviral therapy took into account 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 time points: 35 days after the diagnosis of Covid-19, the end of the study period, or the time of data censoring if the patient died during the study period from reasons unrelated to Covid-19.

Most of the patients who were tested for Covid-19 during the study period underwent such testing because of the occurrence of symptoms. Polymerase-chain-reaction (PCR) and state-regulated antigen tests were freely available at the request of the patient. However, no screening for SARS-CoV-2 was performed, even when a patient had been exposed to a person with confirmed Covid-19. CHS policy stipulated that the antiviral therapy be initiated in eligible patients as soon as possible after a positive SARS-CoV-2 test, in accordance with FDA prescribing information.3 Each CHS district was responsible for the delivery of nirmatrelvir therapy to the patients’ homes and for verification of adherence to the treatment regimen. High-risk patients who had a contraindication to nirmatrelvir were offered treatment with molnupiravir, which was available in Israel beginning on January 16, 2022. Patients who were residing 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 had received treatment with molnupiravir or with the anti–SARS-CoV-2 monoclonal antibodies tixagevimab and cilgavimab.

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

Data Sources and Organization

We evaluated integrated patient-level data that were maintained by CHS from two primary sources: the primary care operational database and the Covid-19 database. The operational database includes sociodemographic data and comprehensive clinical information, such as coexisting chronic illnesses, community-care visits, medications, and results of laboratory tests. The Covid-19 database includes results of PCR and state-regulated rapid antigen tests, vaccinations, and hospitalizations and deaths related to Covid-19. These same databases were used in the primary studies that evaluated the effectiveness of the BNT162b2 vaccine (Pfizer–BioNTech) in a real-world 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, population sector (general Jewish, ultra-Orthodox Jewish, or Arab), and score for socioeconomic status (ranging from 1 [lowest] to 10 [highest]; details are provided in the Supplementary Appendix). The following clinical data were extracted: Covid-19 vaccination dates, PCR and state-regulated rapid antigen test dates and results, 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 hepatic disease, chronic obstructive pulmonary disease, chronic kidney failure, chronic heart failure, obesity , history of stroke or smoking, and recent hospitalizations (in the preceding 3 years) for any cause. In addition, the estimated glomerular filtration rate was extracted when available.

Study Outcomes

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

Subgroup analyzes of the primary and secondary outcomes were performed to determine the effect of SARS-CoV-2 immunity status. Patients were divided into one of two categories according to their immunity status: those who had already acquired previous immunity (vaccine-induced, infection-induced, or a hybrid of both) and those with no previous immunity (unvaccinated or vaccinated with only one mRNA vaccine dose and with no previous documented SARS-CoV-2 infection). This classification was based on the Israeli Ministry of Health guidelines, which refer to persons who receive only one mRNA vaccine dose and persons who are unvaccinated as having similar immunity.

Statistical Analysis

All eligible CHS members were included in the analysis, in accordance with the study design. Descriptive statistics were used to characterize the study patients. Because 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 treatment with nirmatrelvir, time zero corresponded to the time at which each patient received a diagnosis of Covid-19. For patients who received treatment with nirmatrelvir, time zero corresponded to the time at which a patient began the treatment. In order to avoid immortal time bias,8 we performed a time-dependent analysis in which a time-varying covariate was used to indicate the initiation of treatment for each treated patient. In this analysis, patients who received nirmatrelvir were transferred from the “untreated” risk set to the “treated” risk set when treatment was initiated, thereby modifying their treatment status from untreated to treated. Consequently, the follow-up of nirmatrelvir-treated patients started at the end of the immortal period.

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

The association between nirmatrelvir therapy and Covid-19 outcomes was estimated with the use of a multivariate Cox proportional-hazards regression model with time-dependent covariates; adjustment was made for sociodemographic factors and coexisting illnesses. Given that many clinical and sociodemographic factors are potential confounders, two-step testing criteria were applied for the selection of covariates. First, a univariate Kaplan–Meier analysis with a log-rank test was applied to evaluate the associations between each independent candidate variable and the time-dependent primary outcome. Then, a comparison of the survival curves and Schoenfeld’s global test was used to test the proportional-hazards assumption for those variables. Variants that met these two testing criteria served as the 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 uptake of nirmatrelvir therapy.

R statistical software, 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.

Leave a Comment

Your email address will not be published.