【Abstract】
Objectives To develop a predictive model and scoring system to enhance the diagnostic efficiency for coronavirus disease 2019 (COVID-19).
Methods From January 19 to February 6, 2020, 88 confirmed COVID-19 patients presenting with pneumonia and 80 non- COVID-19 patients suffering from pneumonia of other origins were retrospectively enrolled. Clinical data and laboratory results were collected. CT features and scores were evaluated at the segmental level according to the lesions』 position, attenuation, and form. Scores were calculated based on the size of the pneumonia lesion, which graded at the range of 1 to 4. Air bronchogram, tree-in-bud sign, crazy-paving pattern, subpleural curvilinear line, bronchiectasis, air space, pleural effusion, and mediastinal and/ or hilar lymphadenopathy were also evaluated.
Results Multivariate logistic regression analysis showed that history of exposure (β = 3.095, odds ratio (OR) = 22.088), leukocyte count (β = − 1.495, OR = 0.224), number of segments with peripheral lesions (β = 1.604, OR = 1.604), and crazy-paving pattern (β = 2.836, OR = 2.836) were used for establishing the predictive model to identify COVID-19-positive patients (p < 0.05). In this model, values of area under curve (AUC) in the training and testing groups were 0.910 and 0.914, respectively (p < 0.001). A predicted score for COVID-19 (PSC-19) was calculated based on the predictive model by the following formula: PSC-19 = 2 × history of exposure (0–1 point) − 1 × leukocyte count (0–2 points) + 1 × peripheral lesions (0–1 point) + 2 × crazy- paving pattern (0–1 point), with an optimal cutoff point of 1 (sensitivity, 88.5%; specificity, 91.7%).
Conclusions Our predictive model and PSC-19 can be applied for identification of COVID-19-positive cases, assisting physicians and radiologists until receiving the results of reverse transcription–polymerase chain reaction (RT-PCR) tests.
【中文摘要】
目的：建立預測模型和評分系統，以提高新型冠狀病毒感染（COVID-19）的診斷效能。
方法：回顧性分析2020年1月19日至2月6日，88例確診為COVID-19肺炎和80例其他病原體感染的非COVID-19肺炎患者的臨床資料、實驗室檢查結果和影像資料。根據病變的位置、密度及分布形式進行CT徵象評估與評分，病變範圍大小評分1- 4分，同時有無空氣支氣管徵、樹芽徵、鋪路石徵、胸膜下線、支氣管擴張、肺氣囊、胸腔積液及縱膈或肺門淋巴結腫大等CT徵象納入分析。
結果：多因素logistic回歸分析顯示接觸史（β= 3.095，OR= 22.088），白細胞計數（β= − 1.495，OR = 0.224），病變外周分布累及的肺段數（β= 1.604，OR = 1.604）和鋪路石徵陽性（β= 2.836，OR = 2.836）可用於建立預測模型以識別COVID-19陽性患者（p <0.05）。在該模型中，訓練組和測試組的曲線下面積（AUC）值分別為0.910和0.914（p <0.001）。基於該預測模型，通過以下公式計算的COVID-19（PSC-19）預測分數：PSC-19 = 2×接觸史（0-1分）− 1×白細胞計數（0-2分） + 1×外周分布（0-1分）+ 2×鋪路石徵（0-1分），最佳臨界值為1時，COVID-19診斷敏感性為88.5％、特異性為91.7％。
結論：該預測模型和PSC-19評分系統可用於在獲得逆轉錄聚合酶鏈反應（RT-PCR）測試結果前，協助臨床和放射科醫生識別與鑑別COVID-19陽性病例。