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  • A Tool for Early Prediction of Severe Coronavirus Disease 2019 (COVID-19): A Multicenter Study Using the Risk Nomogram in Wuhan and Guangdong, China #MMPMID32296824
  • Gong J; Ou J; Qiu X; Jie Y; Chen Y; Yuan L; Cao J; Tan M; Xu W; Zheng F; Shi Y; Hu B
  • Clin Infect Dis 2020[Jul]; 71 (15): 833-840 PMID32296824show ga
  • BACKGROUND: Because there is no reliable risk stratification tool for severe coronavirus disease 2019 (COVID-19) patients at admission, we aimed to construct an effective model for early identification of cases at high risk of progression to severe COVID-19. METHODS: In this retrospective multicenter study, 372 hospitalized patients with nonsevere COVID-19 were followed for > 15 days after admission. Patients who deteriorated to severe or critical COVID-19 and those who maintained a nonsevere state were assigned to the severe and nonsevere groups, respectively. Based on baseline data of the 2 groups, we constructed a risk prediction nomogram for severe COVID-19 and evaluated its performance. RESULTS: The training cohort consisted of 189 patients, and the 2 independent validation cohorts consisted of 165 and 18 patients. Among all cases, 72 (19.4%) patients developed severe COVID-19. Older age; higher serum lactate dehydrogenase, C-reactive protein, coefficient of variation of red blood cell distribution width, blood urea nitrogen, and direct bilirubin; and lower albumin were associated with severe COVID-19. We generated the nomogram for early identifying severe COVID-19 in the training cohort (area under the curve [AUC], 0.912 [95% confidence interval {CI}, .846-.978]; sensitivity 85.7%, specificity 87.6%) and the validation cohort (AUC, 0.853 [95% CI, .790-.916]; sensitivity 77.5%, specificity 78.4%). The calibration curve for probability of severe COVID-19 showed optimal agreement between prediction by nomogram and actual observation. Decision curve and clinical impact curve analyses indicated that nomogram conferred high clinical net benefit. CONCLUSIONS: Our nomogram could help clinicians with early identification of patients who will progress to severe COVID-19, which will enable better centralized management and early treatment of severe disease.
  • |Adult[MESH]
  • |Area Under Curve[MESH]
  • |Betacoronavirus/pathogenicity[MESH]
  • |China[MESH]
  • |Coronavirus Infections/*diagnosis/*pathology/virology[MESH]
  • |Disease Progression[MESH]
  • |Female[MESH]
  • |Humans[MESH]
  • |Male[MESH]
  • |Middle Aged[MESH]
  • |Nomograms[MESH]
  • |Pandemics[MESH]
  • |Pneumonia, Viral/*diagnosis/*pathology/virology[MESH]
  • |Prognosis[MESH]
  • |Retrospective Studies[MESH]
  • |Risk Assessment/methods[MESH]
  • |Risk Factors[MESH]

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  • suck abstract from ncbi

    833 15.71 2020