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10.1016/j.ijid.2020.05.021

http://scihub22266oqcxt.onion/10.1016/j.ijid.2020.05.021
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32437939!7207134!32437939
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suck abstract from ncbi

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  • Chest X-ray severity index as a predictor of in-hospital mortality in coronavirus disease 2019: A study of 302 patients from Italy #MMPMID32437939
  • Borghesi A; Zigliani A; Golemi S; Carapella N; Maculotti P; Farina D; Maroldi R
  • Int J Infect Dis 2020[Jul]; 96 (ä): 291-293 PMID32437939show ga
  • OBJECTIVES: This study aimed to assess the usefulness of a new chest X-ray scoring system - the Brixia score - to predict the risk of in-hospital mortality in hospitalized patients with coronavirus disease 2019 (COVID-19). METHODS: Between March 4, 2020 and March 24, 2020, all CXR reports including the Brixia score were retrieved. We enrolled only hospitalized Caucasian patients with COVID-19 for whom the final outcome was available. For each patient, age, sex, underlying comorbidities, immunosuppressive therapies, and the CXR report containing the highest score were considered for analysis. These independent variables were analyzed using a multivariable logistic regression model to extract the predictive factors for in-hospital mortality. RESULTS: 302 Caucasian patients who were hospitalized for COVID-19 were enrolled. In the multivariable logistic regression model, only Brixia score, patient age, and conditions that induced immunosuppression were the significant predictive factors for in-hospital mortality. According to receiver operating characteristic curve analyses, the optimal cutoff values for Brixia score and patient age were 8 points and 71 years, respectively. Three different models that included the Brixia score showed excellent predictive power. CONCLUSIONS: Patients with a high Brixia score and at least one other predictive factor had the highest risk of in-hospital death.
  • |*Betacoronavirus[MESH]
  • |*Hospital Mortality[MESH]
  • |*Radiography, Thoracic[MESH]
  • |Aged[MESH]
  • |Aged, 80 and over[MESH]
  • |Coronavirus Infections/diagnostic imaging/*mortality[MESH]
  • |Female[MESH]
  • |Humans[MESH]
  • |Italy/epidemiology[MESH]
  • |Logistic Models[MESH]
  • |Male[MESH]
  • |Middle Aged[MESH]
  • |Pandemics[MESH]
  • |Pneumonia, Viral/diagnostic imaging/*mortality[MESH]
  • |Retrospective Studies[MESH]


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  • suck abstract from ncbi

    291 ä.96 2020