Nirmatrelvir Use and Severe Covid-19 Outcomes During Omicron Surge

study design

This retrospective, observational cohort study was based on data obtained from electronic medical records of members of the Comprehensive Health Services (CHS), a large healthcare organization covering approximately 52% of the total Israeli population and approximately two-thirds of the elderly. The study period began on January 9, 2022, the first day on which nirmatelvir was administered to members of the Mental Health Association, and ended on March 31, 2022. During the study period, the omicron variant was the dominant strain in Israel (see Figure S1). In the Supplementary Appendix, available with the full text of this article at

Population Study

The study population consisted of all CHS members who were 40 years of age or older, confirmed SARS-CoV-2, received a diagnosis of Covid-19 as an outpatient, were assessed as being at high risk of progression to severe disease, and were considered eligible to receive nimatelvir treatment. . Patients at high risk were identified on the basis of the risk model developed by CHS to assess the risk of severe Covid-19 in patients with SARS-CoV-2; Details are provided in the Supplementary Appendix. Patients were included in the study group 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 a Covid-19 diagnosis on or before February 24, 2022. Eligibility for antiviral therapy has taken into account drug interactions and other contraindications, as described in the prescribing information for nirmatrelvir.3 For each patient, follow-up ended at the following earliest time points: 35 days after the diagnosis of Covid-19, the end of the study period, or the time of observation of the data if the patient died during the study period from causes unrelated to Covid-19.

Most patients tested for Covid-19 during the study period underwent such testing due to the occurrence of symptoms. State-regulated polymerase chain reaction (PCR) and antigen tests were freely available upon patient request. However, no screening has been performed to detect SARS-CoV-2, even when the patient was exposed to someone with confirmed Covid-19. It is CHS policy to start antiviral therapy in eligible patients as soon as possible after a positive SARS-CoV-2 test, according to the FDA’s prescribing information.3 Each CHS district was responsible for delivering nirmatrelvir treatment to patients’ homes and verifying adherence to the treatment regimen. Patients at high risk who had contraindications to nirmatrivir were offered treatment with molnopiravir, which was available in Israel as of January 16, 2022. Patients who were staying in long-term care facilities and patients who were hospitalized before or on the same day tested positive for SARS- CoV-2 was excluded from the study, as were patients who received treatment with mollopiravir or with the anti-SARS-CoV-2 monoclonal antibodies tixagevimab and cilgavimab.

The study was approved by the Community Helsinki Committee and the Data Use Committees of the Humanitarian Core Standard. The study was excluded from the requirement to obtain informed consent due to the retrospective design.

Data sources and organization

We assessed integrated patient-level data held by the Core Humanitarian Standard from two primary sources: the Primary Care Operational Database and the Covid-19 Database. The operational database includes comprehensive sociodemographic and clinical information, such as chronic disease coexistence, community care visits, medication and laboratory test results. The Covid-19 database includes results of the polymerase chain reaction (PCR) test, 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-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, gender, demographics (General Jew, Ultra-Orthodox Jew, or Arab), and a score for socioeconomic status (ranging from 1 [lowest] up to 10 [highest]; Details are provided in the Supplementary Appendix). The following clinical data were extracted: Covid-19 vaccination dates, state-regulated rapid antigen test dates and results and polymerase chain reaction (PCR) dates and outcomes, Covid-19 antiviral treatments, hospitalizations, and deaths. Data on the following clinical risk factors for severe Covid-19 were also collected: immunosuppression, diabetes mellitus, asthma, hypertension, neurological disease, current cancer, chronic liver disease, chronic obstructive pulmonary disease, chronic renal failure, chronic heart failure Obesity, history of stroke or smoking, and recent hospitalization (in the previous 3 years) for any cause. In addition, an estimated glomerular filtration rate was extracted when available.


The primary outcome of the study was hospitalization due to Covid-19. The secondary outcome was death from 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 one of two categories according to their immune status: those who had already acquired prior immunity (vaccine-inducible, infection-inducible, or a combination of both) and those without prior immunity (unvaccinated or vaccinated with only one mRNA dose of vaccine with No documented previous infection with SARS-CoV-2). This classification was based on Israeli Ministry of Health guidelines, which refer to people who receive only one dose of the mRNA vaccine and those who have not been vaccinated as having similar immunity.

statistical analysis

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

For patients who did not receive treatment with nirmatrelvir, time zero corresponds to the time each patient received a diagnosis of Covid-19. For patients who received treatment with nirmatelvir, time zero corresponds to the time the patient started treatment. In order to avoid immortal temporal bias,8 We performed a time-dependent analysis in which a time-varying covariate was used to indicate treatment initiation for each treated patient. In this analysis, patients treated with nirmatrelvir were moved from the defined ‘untreated’ risk to the ‘treated’ defined risk at treatment initiation, thus adjusting their treatment status from untreated to treated. Consequently, follow-up of patients treated with nirmatylvir was started at the end of the immortal period.

A sensitivity analysis assessed the magnitude of the effect of nirmatrelvir treatment starting on the third day of follow-up by excluding patients who were hospitalized within two days after the start of follow-up. This approach allowed for comparison with the EPIC-HR trial, in which patients were excluded if hospitalization was needed within 2 days after randomization was expected.4

The relationship between nirmatrelvir treatment and Covid-19 outcome was estimated using a multivariate Cox proportional hazards regression model with time-dependent covariates; Adjustment for sociodemographic factors and coexisting diseases was made. Given that several clinical and sociodemographic factors are potential confounders, two-step testing criteria were applied to select covariates. First, a univariate Kaplan-Meier analysis with log-rank test was applied to assess the associations between each independent candidate variable and the time-dependent primary outcome. Then, comparison of survival curves and the global Schoenefeld test were used to test the assumption of relative hazards for those variables. Variables that fulfilled these two test criteria served as inputs to 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 nimatelvir treatment.

The 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.

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