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.
【저자키워드】 SARS-CoV-2, Pneumonia, prediction, case-control study, Aging population, 【초록키워드】 coronavirus, Clinical characteristics, SARS-COV-2 infection, hospital, diagnostic, Sex, C-reactive protein, risk, outcome, abdominal pain, Lymphocyte count, Patient, Control, age, predictor, university, predict, community-acquired pneumonia, Emergency, external validation, AUC, diarrhoea, Clinical use, Logistic, acute respiratory syndrome, approaches, patient triage, help, nausea, mean difference, Department, polymerase chain, IMPROVE, diagnosed, required, less, independent dataset, pneumonia case, 【제목키워드】 pandemic, Clinical characteristics, Patient, community-acquired pneumonia, Hungary,