This observational retrospective cohort study was based on data obtained from electronic medical records for members of Clalit Health Services (CHS), a large healthcare organization that covers approximately 52% of the entire Israeli population and nearly two-thirds of older adults. . 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 omicron variant was the dominant strain in Israel (see Fig. S1 in the Supplementary Appendixavailable with the full text of this article at NEJM.org).
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 of progression severe disease and were eligible for nirmatrelvir therapy. High-risk patients were identified based on a risk model developed by CHS to assess 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 a diagnosis of Covid-19 on or before February 24, 2022. Eligibility to receive 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 earlier of the following time points: 35 days after Covid-19 diagnosis, 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 of the patients who were tested for Covid-19 during the study period were tested due to the onset of symptoms. Polymerase chain reaction (PCR) and state-regulated antigen tests were freely available upon patient request. However, no SARS-CoV-2 screening was performed, even when a patient had been exposed to a person with confirmed Covid-19. CHS policy stipulated that antiviral therapy be started in eligible patients as soon as possible after a positive SARS-CoV-2 test, according to FDA prescribing information.3 Each CHS district was responsible for delivering nirmatrelvir therapy to patients’ homes and verifying adherence to the treatment regimen. High-risk patients who had a contraindication to nirmatrelvir were offered treatment with molnupiravir, which became available in Israel from January 16, 2022. Patients residing in long-term care facilities and patients who had been hospitalized before or the same day. as positive test for SARS-CoV-2 were excluded from the study, as well as 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 CHS Helsinki community and data utilization committees. The study was exempt from the requirement to obtain informed consent due to the retrospective design.
Data sources and organizations
We evaluated the integrated patient-level data that CHS maintained from two main 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 conditions, community care visits, medications, and laboratory test results. The Covid-19 database includes results from state-regulated PCR and rapid antigen tests, vaccinations, and Covid-19-related hospitalizations and deaths. These same databases were used in the primary studies evaluating 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. The following sociodemographic data were extracted from each patient in the study: age, sex, population sector (general Jew, ultra-orthodox Jew, or Arab) and score by socioeconomic level (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 on 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 failure , obesity , a history of stroke or smoking, and recent hospitalizations (within the past 3 years) for any cause. In addition, 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 immunity status against SARS-CoV-2. Patients were divided into one of two categories based on their immune status: those who had already acquired prior immunity (vaccine-induced, infection-induced, or a hybrid of both) and those without prior immunity (unvaccinated or vaccinated with only one mRNA). ). vaccine dose and no prior documented SARS-CoV-2 infection). This classification was based on the Israeli Ministry of Health guidelines, which refer to people who receive only one dose of mRNA vaccine and people who are not vaccinated as having similar immunity.
All eligible CHS members were included in the analysis, according to the study design. Descriptive statistics were used to characterize the study patients. Because the independent variable (nirmarelvir treatment) varied over time, univariate and multivariate survival analyzes with time-dependent covariates were performed.
For patients who did not receive nirmatrelvir treatment, time zero corresponded to the time each patient received a diagnosis of Covid-19. For patients who received nirmatrelvir treatment, time zero was when a patient started treatment. To avoid the immortal time bias,eight 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’ risk group to the ‘treated’ risk group when treatment was initiated, thus changing their treatment status from untreated to treated. followed, the follow-up of the nirmatrelvir-treated patients began at the end of the immortal period.
A sensitivity analysis evaluated the effect size of nirmatrelvir treatment from day 3 of follow-up by excluding patients who had been hospitalized within 2 days of the start of follow-up. This approach allowed for comparability with the EPIC-HR trial, in which patients were excluded if hospitalization was anticipated within 2 days of randomization.four
The association between nirmatrelvir therapy and Covid-19 outcomes was estimated using a multivariate Cox proportional hazards regression model with time-dependent covariates; it was adjusted for sociodemographic factors and coexisting diseases. Since many clinical and sociodemographic factors are potential confounders, two-step test criteria were applied for covariate selection. First, univariate Kaplan-Meier analysis with a log-rank test was applied to assess associations between each independent candidate variable and the time-dependent primary outcome. A comparison of survival curves and Schoenfeld’s global test were then used to test the proportional hazards assumption for these variables. Variants that met these two test criteria served as inputs for multivariate regression analysis. An additional multivariate Cox proportional hazards regression model was used to estimate the association between each of the covariates and acceptance of nirmatrelvir therapy.
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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