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Combined clinical and imaging features better predict the critical outcomes of patients with SARS-COV-2.
Yue, Ting; Zhou, Wenli; He, Jie; Wang, Huilin; Liu, Yongjiu; Wang, Bing; Zhu, QingQing; Xia, Huawei; Hu, Hongjie.
  • Yue T; Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang.
  • Zhou W; Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang.
  • He J; Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang.
  • Wang H; Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang.
  • Liu Y; Department of Radiology, First People's Hospital of Jingmen, Jingmen, Hubei, China.
  • Wang B; Department of Radiology, First People's Hospital of Jingmen, Jingmen, Hubei, China.
  • Zhu Q; Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang.
  • Xia H; Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang.
  • Hu H; Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang.
Medicine (Baltimore) ; 100(12): e25083, 2021 Mar 26.
Article in English | MEDLINE | ID: covidwho-1150005
Semantic information from SemMedBD (by NLM)
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ABSTRACT
ABSTRACT The purpose of this study was to investigate the predictive value of combined clinical and imaging features, compared with the clinical or radiological risk factors only. Moreover, the expected results aimed to improve the identification of severe acute respiratory syndrome coronavirus-2 (SARS-COV-2) patients who may have critical outcomes.This retrospective study included laboratory-confirmed SARS-COV-2 cases between January 18, 2020, and February 16, 2020. The patients were divided into 2 groups with noncritical illness and critical illness regarding severity status within the hospitalization. Univariable and multivariable logistic regression models were used to explore the risk factors associated with clinical and radiological outcomes in patients with SARS-COV-2. The ROC curves were performed to compare the prediction performance of different factors.A total of 180 adult patients in this study included 20 critical patients and 160 noncritical patients. In univariate logistic regression analysis, 15 risk factors were significantly associated with critical outcomes. Of importance, C-reactive protein (1.051, 95% confidence interval 1.024-1.078), D-dimer (1.911, 95% CI, 1.050-3.478), and CT score (1.29, 95% CI, 1.053-1.529) on admission were independent risk factors in multivariate analysis. The combined model achieved a better performance in disease severity prediction (P = .05).CRP, D-dimer, and CT score on admission were independent risk factors for critical illness in adults with SARS-COV-2. The combined clinical and radiological model achieved better predictive performance than clinical or radiological factors alone.
Subject(s)

Full text: Available Collection: International databases Database: MEDLINE Main subject: Diagnostic Techniques and Procedures / COVID-19 Type of study: Diagnostic study / Etiology study / Observational study / Prognostic study / Randomized controlled trials / Risk factors Limits: Adult / Aged / Female / Humans / Male / Middle aged Language: English Journal: Medicine (Baltimore) Year: 2021 Document Type: Article

Full text: Available Collection: International databases Database: MEDLINE Main subject: Diagnostic Techniques and Procedures / COVID-19 Type of study: Diagnostic study / Etiology study / Observational study / Prognostic study / Randomized controlled trials / Risk factors Limits: Adult / Aged / Female / Humans / Male / Middle aged Language: English Journal: Medicine (Baltimore) Year: 2021 Document Type: Article