Key Laboratory of Medical Molecular Virology Ministry of Education (MOE)/National Health Commission of China (NHC)/Chinese Academy of Medical Sciences (CAMS), Shanghai Institute of Infectious Disease and Biosecurity, School of Basic Medical Sciences, Fudan University, Shanghai, China.
Department of Laboratory Medicine, Shanghai Fourth People's Hospital, School of Medicine, Tongji University, Shanghai, China.
Front Immunol. 2023 Mar 1;14:1157892. doi: 10.3389/fimmu.2023.1157892. eCollection 2023.
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant has prevailed globally since November 2021. The extremely high transmissibility and occult manifestations were notable, but the severity and mortality associated with the Omicron variant and subvariants cannot be ignored, especially for immunocompromised populations. However, no prognostic model for specially predicting the severity of the Omicron variant infection is available yet. In this study, we aim to develop and validate a prognostic model based on immune variables to early recognize potentially severe cases of Omicron variant-infected patients.
This was a single-center prognostic study involving patients with SARS-CoV-2 Omicron variant infection. Eligible patients were randomly divided into the training and validation cohorts. Variables were collected immediately after admission. Candidate variables were selected by three variable-selecting methods and were used to construct Cox regression as the prognostic model. Discrimination, calibration, and net benefit of the model were evaluated in both training and validation cohorts.
Six hundred eighty-nine of the involved 2,645 patients were eligible, consisting of 630 non-ICU cases and 59 ICU cases. Six predictors were finally selected to establish the prognostic model: age, neutrophils, lymphocytes, procalcitonin, IL-2, and IL-10. For discrimination, concordance indexes in the training and validation cohorts were 0.822 (95% CI: 0.748-0.896) and 0.853 (95% CI: 0.769-0.942). For calibration, predicted probabilities and observed proportions displayed high agreements. In the 21-day decision curve analysis, the threshold probability ranges with positive net benefit were 01 and nearly 00.75 in the training and validation cohorts, correspondingly.
This model had satisfactory high discrimination, calibration, and net benefit. It can be used to early recognize potentially severe cases of Omicron variant-infected patients so that they can be treated timely and rationally to reduce the severity and mortality of Omicron variant infection.
自 2021 年 11 月以来，严重急性呼吸综合征冠状病毒 2（SARS-CoV-2）的奥密克戎变异株在全球范围内流行。其具有极高的传染性和隐匿性，但奥密克戎变异株及其亚变体的严重程度和死亡率不容忽视，尤其是对免疫功能低下的人群。然而，目前尚无专门预测奥密克戎变异株感染严重程度的预后模型。本研究旨在开发和验证一种基于免疫变量的预后模型，以早期识别奥密克戎变异株感染患者中可能出现的重症病例。
这是一项单中心预后研究，涉及 SARS-CoV-2 奥密克戎变异株感染患者。符合条件的患者被随机分为训练和验证队列。入院后立即收集变量。通过三种变量选择方法选择候选变量，并将其用于构建 Cox 回归作为预后模型。在训练和验证队列中评估模型的区分度、校准度和净获益。
在纳入的 2645 例患者中，有 689 例符合条件，包括 630 例非 ICU 病例和 59 例 ICU 病例。最终选择 6 个预测因子来建立预后模型：年龄、中性粒细胞、淋巴细胞、降钙素原、IL-2 和 IL-10。在区分度方面，训练和验证队列的一致性指数分别为 0.822（95%CI：0.748-0.896）和 0.853（95%CI：0.769-0.942）。在校准方面，预测概率和观察比例显示出高度一致性。在 21 天决策曲线分析中，训练和验证队列的阈值概率范围分别为 01 和 00.75，具有正净获益。
该模型具有良好的高区分度、校准度和净获益。它可以用于早期识别奥密克戎变异株感染患者中可能出现的重症病例，以便及时、合理地进行治疗，降低奥密克戎变异株感染的严重程度和死亡率。
