A predictive model and scoring system combining clinical and CT characteristics for the diagnosis of COVID-19
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A predictive model and scoring system combining clinical and CT characteristics for the diagnosis of COVID-19 Le Qin 1 & Yanzhao Yang 1 & Qiqi Cao 1 & Zenghui Cheng 1 & Xiaoyang Wang 2 & Qingfeng Sun 3 & Fuhua Yan 1 & Jieming Qu 4 & Wenjie Yang 1 Received: 7 April 2020 / Revised: 21 May 2020 / Accepted: 9 June 2020 # European Society of Radiology 2020
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 nonCOVID-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 × crazypaving 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. Key Points • Prediction of RT-PCR positivity is crucial for fast diagnosis of patients suspected of having coronavirus disease 2019 (COVID-19). • Typical CT manifestations are advantageous for diagnosing COVID-19 and differentiation of COVID-19 from other types of pneumonia. • A predictive model and scoring system combining both clinical and CT features were herein developed to enable high diagnostic efficiency for COVID-19. Keywords Tomography, x-ray computed . COVID-19 . Pneumoni . Predictive value of tests
Le Qin and Yanzhao Yang contributed equally to this work. Wenjie Yang and Jieming Qu are co-corresponding authors. * Jieming Qu [email protected]
2
Department of Radiology, Ruian People’s Hospital, No. 108, Wan Song Road, Ruian 325200, Zhejiang Province, China
* Wenjie Yan
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