Department of Infectious Diseases, National Medical Center for Infectious Diseases, Shanghai Key Laboratory of Infectious Diseases and Biosafety Emergency Response, Huashan Hospital, Shanghai Institute of Infectious Diseases and Biosecurity, Fudan University, Shanghai, China.
Department of Infectious Diseases, Jing'An Branch of Huashan Hospital, Fudan University, Shanghai, China.
J Med Virol. 2023 Feb;95(2):e28550. doi: 10.1002/jmv.28550.
Prolonged severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has received much attention since it is associated with mortality and is hypothesized as the cause of long COVID-19 and the emergence of a new variant of concerns. However, a prediction model for the accurate prediction of prolonged infection is still lacking. A total of 2938 confirmed patients with COVID-19 diagnosed by positive reverse transcriptase-polymerase chain reaction tests were recruited retrospectively. This study cohort was divided into a training set (70% of study patients; n = 2058) and a validation set (30% of study patients; n = 880). Univariate and multivariate logistic regression analyses were utilized to identify predictors for prolonged infection. Model 1 included only preadmission variables, whereas Model 2 also included after-admission variables. Nomograms based on variables of Model 1 and Model 2 were built for clinical use. The efficiency of nomograms was evaluated by using the area under the curve, calibration curves, and concordance indexes (C-index). Independent predictors of prolonged infection included in Model 1 were: age ≥75 years, chronic kidney disease, chronic lung disease, partially or fully vaccinated, and booster. Additional independent predictors in Model 2 were: treated with nirmatrelvir/ritonavir more than 5 days after diagnosis and glucocorticoid. The inclusion of after-admission variables in the model slightly improved the discriminatory power (C-index in the training cohort: 0.721 for Model 1 and 0.737 for Model 2; in the validation cohort: 0.699 for Model 1 and 0.719 for Model 2). In our study, we developed and validated predictive models based on readily available variables of preadmission and after-admission for predicting prolonged SARS-CoV-2 infection of patients with COVID-19.
长期严重急性呼吸系统综合征冠状病毒 2（SARS-CoV-2）感染受到了广泛关注，因为它与死亡率相关，并被假设是长新冠和新关注变种出现的原因。然而，目前仍然缺乏一种能够准确预测长期感染的预测模型。本研究回顾性地招募了 2938 名经阳性逆转录-聚合酶链反应检测确诊的 COVID-19 患者。该研究队列分为训练集（研究患者的 70%；n=2058）和验证集（研究患者的 30%；n=880）。采用单变量和多变量逻辑回归分析来确定延长感染的预测因素。模型 1 仅包含入院前变量，而模型 2 还包含入院后变量。基于模型 1 和模型 2 的变量构建了列线图，用于临床应用。通过曲线下面积、校准曲线和一致性指数（C 指数）评估列线图的效率。纳入模型 1 的延长感染的独立预测因素包括：年龄≥75 岁、慢性肾脏病、慢性肺病、部分或完全接种疫苗以及加强针。模型 2 中的其他独立预测因素包括：确诊后 5 天以上使用奈玛特韦/利托那韦治疗和糖皮质激素治疗。在模型中纳入入院后变量略微提高了区分能力（训练队列中的 C 指数：模型 1 为 0.721，模型 2 为 0.737；验证队列中的 C 指数：模型 1 为 0.699，模型 2 为 0.719）。在我们的研究中，我们基于 COVID-19 患者入院前和入院后的可获得变量开发并验证了预测模型，用于预测 SARS-CoV-2 感染的持续时间。
