Selected article for: "admission group and logistic regression"
Author: Zhichao Feng; Qizhi Yu; Shanhu Yao; Lei Luo; Junhong Duan; Zhimin Yan; Min Yang; Hongpei Tan; Mengtian Ma; Ting Li; Dali Yi; Ze Mi; Huafei Zhao; Yi Jiang; Zhenhu He; Huiling Li; Wei Nie; Yin Liu; Jing Zhao; Muqing Luo; Xuanhui Liu; Pengfei Rong; Wei Wang
Title: Early Prediction of Disease Progression in 2019 Novel Coronavirus Pneumonia Patients Outside Wuhan with CT and Clinical Characteristics
  • Document date: 2020_2_23
    • ID: j0ufth5d_1
    Snippet: Objective: To determine the predictive value of CT and clinical characteristics for short-term disease progression in patients with 2019 novel coronavirus pneumonia (NCP). Materials and Methods: 224 patients with confirmed 2019 novel coronavirus (COVID-19) infection outside Wuhan who had chest CT examinations were retrospectively screened. Clinical data were obtained from electronic medical records. CT images were reviewed and scored for lesion d.....
    Document: Objective: To determine the predictive value of CT and clinical characteristics for short-term disease progression in patients with 2019 novel coronavirus pneumonia (NCP). Materials and Methods: 224 patients with confirmed 2019 novel coronavirus (COVID-19) infection outside Wuhan who had chest CT examinations were retrospectively screened. Clinical data were obtained from electronic medical records. CT images were reviewed and scored for lesion distribution, lobe and segment involvement, ground-glass opacities, consolidation, and interstitial thickening. All included patients with moderate NCP were observed for at least 14 days from admission to determine whether they exacerbated to severe NCP (progressive group) or not (stable group). CT and clinical characteristics between the two groups were compared, and multivariate logistic regression and sensitivity analyses were performed to identify the risk factors for developing severe NCP.

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