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  • Development and validation a nomogram for predicting the risk of severe COVID-19: A multi-center study in Sichuan, China #MMPMID32421703
  • Zhou Y; He Y; Yang H; Yu H; Wang T; Chen Z; Yao R; Liang Z
  • PLoS One 2020[]; 15 (5): e0233328 PMID32421703show ga
  • BACKGROUND: Since December 2019, coronavirus disease 2019 (COVID-19) emerged in Wuhan and spread across the globe. The objective of this study is to build and validate a practical nomogram for estimating the risk of severe COVID-19. METHODS: A cohort of 366 patients with laboratory-confirmed COVID-19 was used to develop a prediction model using data collected from 47 locations in Sichuan province from January 2020 to February 2020. The primary outcome was the development of severe COVID-19 during hospitalization. The least absolute shrinkage and selection operator (LASSO) regression model was used to reduce data size and select relevant features. Multivariable logistic regression analysis was applied to build a prediction model incorporating the selected features. The performance of the nomogram regarding the C-index, calibration, discrimination, and clinical usefulness was assessed. Internal validation was assessed by bootstrapping. RESULTS: The median age of the cohort was 43 years. Severe patients were older than mild patients by a median of 6 years. Fever, cough, and dyspnea were more common in severe patients. The individualized prediction nomogram included seven predictors: body temperature at admission, cough, dyspnea, hypertension, cardiovascular disease, chronic liver disease, and chronic kidney disease. The model had good discrimination with an area under the curve of 0.862, C-index of 0.863 (95% confidence interval, 0.801-0.925), and good calibration. A high C-index value of 0.839 was reached in the interval validation. Decision curve analysis showed that the prediction nomogram was clinically useful. CONCLUSION: We established an early warning model incorporating clinical characteristics that could be quickly obtained on admission. This model can be used to help predict severe COVID-19 and identify patients at risk of developing severe disease.
  • |*Nomograms[MESH]
  • |*Severity of Illness Index[MESH]
  • |Adolescent[MESH]
  • |Adult[MESH]
  • |Aged[MESH]
  • |Betacoronavirus[MESH]
  • |China[MESH]
  • |Coronavirus Infections/*diagnosis[MESH]
  • |Female[MESH]
  • |Humans[MESH]
  • |Logistic Models[MESH]
  • |Male[MESH]
  • |Middle Aged[MESH]
  • |Pandemics[MESH]
  • |Pneumonia, Viral/*diagnosis[MESH]
  • |Retrospective Studies[MESH]
  • |Risk Factors[MESH]
  • |Young Adult[MESH]

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  • suck abstract from ncbi

    e0233328 5.15 2020