Objective To compare the effectiveness of sotrovimab (a neutralising monoclonal antibody) with molnupiravir (an antiviral) in preventing severe outcomes of covid-19 in adult patients infected with SARS-CoV-2 in the community and at high risk of severe outcomes from covid-19. Design Observational cohort study with the OpenSAFELY platform. Setting With the approval of NHS England, a real world cohort study was conducted with the OpenSAFELY-TPP platform (a secure, transparent, open source software platform for analysis of NHS electronic health records), and patient level electronic health record data were obtained from 24 million people registered with a general practice in England that uses TPP software. The primary care data were securely linked with data on SARS-CoV-2 infection and treatments, hospital admission, and death, over a period when both drug treatments were frequently prescribed in community settings. Participants Adult patients with covid-19 in the community at high risk of severe outcomes from covid-19, treated with sotrovimab or molnupiravir from 16 December 2021. Interventions Sotrovimab or molnupiravir given in the community by covid-19 medicine delivery units. Main outcome measures Admission to hospital with covid-19 (ie, with covid-19 as the primary diagnosis) or death from covid-19 (ie, with covid-19 as the underlying or contributing cause of death) within 28 days of the start of treatment. Results Between 16 December 2021 and 10 February 2022, 3331 and 2689 patients were treated with sotrovimab and molnupiravir, respectively, with no substantial differences in baseline characteristics. Mean age of all 6020 patients was 52 (standard deviation 16) years; 59% were women, 89% were white, and 88% had received three or more covid-19 vaccinations. Within 28 days of the start of treatment, 87 (1.4%) patients were admitted to hospital or died of infection from SARS-CoV-2 (32 treated with sotrovimab and 55 with molnupiravir). Cox proportional hazards models stratified by area showed that after adjusting for demographic information, high risk cohort categories, vaccination status, calendar time, body mass index, and other comorbidities, treatment with sotrovimab was associated with a substantially lower risk than treatment with molnupiravir (hazard ratio 0.54, 95% confidence interval 0.33 to 0.88, P=0.01). Consistent results were found from propensity score weighted Cox models (0.50, 0.31 to 0.81, P=0.005) and when restricted to people who were fully vaccinated (0.53, 0.31 to 0.90, P=0.02). No substantial effect modifications by other characteristics were detected (all P values for interaction >0.10). The findings were similar in an exploratory analysis of patients treated between 16 February and 1 May 2022 when omicron BA.2 was the predominant variant in England. Conclusions In routine care of adult patients in England with covid-19 in the community, at high risk of severe outcomes from covid-19, those who received sotrovimab were at lower risk of severe outcomes of covid-19 than those treated with molnupiravir. Access to the underlying identifiable and potentially re-identifiable pseudonymised electronic health record data are tightly governed by various legislative and regulatory frameworks, and restricted by best practice. The data in OpenSAFELY are drawn from general practice data across England where TPP is the data processor. TPP developers initiate an automated process to create pseudonymised records in the core OpenSAFELY database, which are copies of key structured data tables in the identifiable records. These pseudonymised records are linked onto key external data resources that have also been pseudonymised via SHA-512 one way hashing of NHS numbers with a shared salt. Bennett Institute for Applied Data Science developers and principal investigators holding contracts with NHS England have access to the OpenSAFELY pseudonymised data tables as needed to develop the OpenSAFELY tools. These tools in turn enable researchers with OpenSAFELY data access agreements to write and execute code for data management and data analysis without direct access to the underlying raw pseudonymised patient data, and to review the outputs of this code. All code for the full data management pipeline, from raw data to completed results for this analysis, and for the OpenSAFELY platform as a whole, is available for review at .

中文翻译：

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sotrovimab 和 molnupiravir 预防社区患者严重 covid-19 结果的比较效果：使用 OpenSAFELY 平台的观察性队列研究

目的 比较 sotrovimab（一种中和单克隆抗体）和 molnupiravir（一种抗病毒药物）在预防社区感染 SARS-CoV-2 的成年患者出现 covid-19 严重后果以及 covid-19 严重后果高风险人群中的有效性。 19. 使用 OpenSAFELY 平台设计观察性队列研究。背景 经 NHS England 批准，使用 OpenSAFELY-TPP 平台（用于分析 NHS 电子健康记录的安全、透明、开源软件平台）进行了真实世界队列研究，患者级别的电子健康记录数据来自2400 万人在使用 TPP 软件的英国全科诊所注册。初级保健数据与 SARS-CoV-2 感染和治疗、住院和死亡数据安全关联，在社区环境中经常开出两种药物治疗的时期。参与者自 2021 年 12 月 16 日起接受 sotrovimab 或 molnupiravir 治疗的社区中患有 covid-19 的成年患者因 covid-19 导致严重后果的风险很高。干预措施 covid-19 药物递送单位在社区中给予 sotrovimab 或 molnupiravir。主要结局指标 在开始后 28 天内因 covid-19 入院（即以 covid-19 为主要诊断）或死于 covid-19（即以 covid-19 为根本或促成死亡原因）的治疗。结果 2021 年 12 月 16 日至 2022 年 2 月 10 日期间，分别有 3331 名和 2689 名患者接受了 sotrovimab 和 molnupiravir 治疗，基线特征无显着差异。所有 6020 名患者的平均年龄为 52（标准差 16）岁；59% 是女性，89% 是白人，88% 接种过 3 次或更多次 covid-19 疫苗。在治疗开始后的 28 天内，87 名 (1.4%) 患者因 SARS-CoV-2 感染入院或死亡（32 名患者接受 sotrovimab 治疗，55 名患者接受 molnupiravir 治疗）。按地区分层的 Cox 比例风险模型显示，在调整人口统计信息、高风险队列类别、疫苗接种状态、日历时间、体重指数和其他合并症后，sotrovimab 治疗的相关风险大大低于 molnupiravir 治疗（危害比率 0.54，95% 置信区间 0.33 至 0.88，P=0.01）。从倾向得分加权 Cox 模型中发现了一致的结果（0.50、0.31 至 0.81，P=0。005) 并且仅限于完全接种疫苗的人 (0.53, 0.31 至 0.90, P=0.02)。未检测到其他特征的实质性影响修改（所有相互作用的 P 值 > 0.10）。在对 2022 年 2 月 16 日至 5 月 1 日期间接受治疗的患者进行的探索性分析中，结果相似，当时 omicron BA.2 是英格兰的主要变异体。结论 在英格兰社区中患有 covid-19 的成年患者的常规护理中，covid-19 导致严重后果的风险很高，接受 sotrovimab 治疗的患者发生 covid-19 严重后果的风险低于接受 molnupiravir 治疗的患者。对底层可识别和可能可重新识别的假名电子健康记录数据的访问受到各种立法和监管框架的严格管理，并受到最佳实践的限制。OpenSAFELY 中的数据来自英格兰的一般实践数据，其中 TPP 是数据处理器。TPP 开发人员启动一个自动化过程，在核心 OpenSAFELY 数据库中创建假名记录，这些记录是可识别记录中关键结构化数据表的副本。这些假名记录链接到关键的外部数据资源，这些数据资源也通过 SHA-512 使用共享盐对 NHS 号码进行单向哈希处理。Bennett Institute for Applied Data Science 开发人员和与 NHS England 签订合同的主要研究人员可以根据需要访问 OpenSAFELY 假名数据表，以开发 OpenSAFELY 工具。这些工具反过来使具有 OpenSAFELY 数据访问协议的研究人员能够编写和执行用于数据管理和数据分析的代码，而无需直接访问底层的原始假名患者数据，并审查该代码的输出。完整数据管理管道的所有代码，从原始数据到此分析的完整结果，以及整个 OpenSAFELY 平台，均可在以下位置进行审查.