The distinction between severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)–related and community-acquired pneumonias poses significant difficulties, as both frequently involve the elderly. This study aimed to predict the risk of SARS-CoV-2-related pneumonia based on clinical characteristics at hospital presentation. Case-control study of all patients admitted for pneumonia at Semmelweis University Emergency Department. Cases (n = 30) were patients diagnosed with SARS-CoV-2-related pneumonia (based on polymerase chain reaction test) between 26 March 2020 and 30 April 2020; controls (n = 82) were historical pneumonia cases between 1 January 2019 and 30 April 2019. Logistic models were built with SARS-CoV-2 infection as outcome using clinical characteristics at presentation. Patients with SARS-CoV-2-related pneumonia were younger (mean difference, 95% CI: 9.3, 3.2–15.5 years) and had a higher lymphocyte count, lower C-reactive protein, presented more frequently with bilateral infiltrate, less frequently with abdominal pain, diarrhoea, and nausea in age- and sex-adjusted models. A logistic model using age, sex, abdominal pain, C-reactive protein, and the presence of bilateral infiltrate as predictors had an excellent discrimination (AUC 0.88, 95% CI: 0.81–0.96) and calibration (p = 0.27–Hosmer-Lemeshow test). The clinical use of our screening prediction model could improve the discrimination of SARS-CoV-2 related from other community-acquired pneumonias and thus help patient triage based on commonly used diagnostic approaches. However, external validation in independent datasets is required before its clinical use.

区分严重急性呼吸系统综合症冠状病毒 2 (SARS-CoV-2) 相关肺炎和社区获得性肺炎存在很大困难，因为两者都经常涉及老年人。本研究旨在根据医院就诊时的临床特征预测 SARS-CoV-2 相关肺炎的风险。塞梅尔维斯大学急诊科所有因肺炎入院的患者的病例对照研究。病例（n = 30）是 2020 年 3 月 26 日至 2020 年 4 月 30 日期间被诊断为 SARS-CoV-2 相关肺炎（基于聚合酶链反应试验）的患者；控制 ( n= 82) 是 2019 年 1 月 1 日至 2019 年 4 月 30 日期间的历史性肺炎病例。以 SARS-CoV-2 感染为结果，使用就诊时的临床特征建立逻辑模型。SARS-CoV-2 相关肺炎患者更年轻（平均差，95% CI：9.3，3.2-15.5 岁），淋巴细胞计数较高，C 反应蛋白较低，双侧浸润更常见，双侧浸润较少见年龄和性别调整模型中的腹痛、腹泻和恶心。使用年龄、性别、腹痛、C 反应蛋白和双侧浸润作为预测因子的逻辑模型具有出色的辨别力（AUC 0.88，95% CI：0.81-0.96）和校准（p= 0.27–Hosmer-Lemeshow 检验）。我们的筛查预测模型的临床应用可以提高 SARS-CoV-2 与其他社区获得性肺炎相关的鉴别能力，从而帮助基于常用诊断方法对患者进行分类。然而，在临床使用之前，需要在独立数据集中进行外部验证。