key: cord-0898109-yswlt555
authors: Borghesi, Andrea; Zigliani, Angelo; Golemi, Salvatore; Carapella, Nicola; Maculotti, Patrizia; Farina, Davide; Maroldi, Roberto
title: Chest X-ray severity index as a predictor of in-hospital mortality in coronavirus disease 2019: A study of 302 patients from Italy
date: 2020-05-08
journal: International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases
DOI: 10.1016/j.ijid.2020.05.021
sha: dfe3e0bb96147cbc06e4cf7b5f3635cda8d13106
doc_id: 898109
cord_uid: yswlt555

Abstract 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 containing 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 the 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. On 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 high Brixia score and at least one other predictive factor had the highest risk of in-hospital death.

The coronavirus disease 2019 (COVID-19), caused by a novel virus-the severe acute respiratory This retrospective study was notified to the Institutional Ethical Committee. Given the retrospective 83 design of the study, the need for informed consent was waived.

To investigate significant differences between recovered and dead patients, we selected nine 85 independent variables that were presumed to influence the final outcome (Table 1) . These variables 86 were analyzed using Chi-square test or the Mann-Whitney U test. The significant variables were 87 subsequently included in a multivariable logistic regression model to extract the predictive factors

We identified 302 Caucasian patients who were hospitalized for COVID-19 (Table 1) conditions that induced immunosuppression were significant predictive factors for in-hospital 96 mortality, and therefore, were included in the predictive model (Table 2) . With regard to 97 immunosuppressive conditions, most patients had advanced renal failure (42%), hematological 98 disorders (20%), or were treated with corticosteroids (18%).

On receiver operating characteristic curve analyses, the optimal cutoff values for Brixia score and 100 patient age were 8 points and 71 years, respectively. Three different models that included the Brixia 101 score showed excellent predictive power (Figure 1 ).

to assess the effectiveness of a CXR scoring system for predicting in-hospital mortality in infected 123 patients. The main limitations of this study include the retrospective study design and lack of 124 laboratory parameters included in the predictive models because currently, these data were collected 125 from a limited number of cases.

In conclusion, this study demonstrated for the first time that high Brixia score and at least one other 127 predictive factor-patient age and conditions that induced immunosuppression-conferred the 128 highest risk of death due to COVID-19. This information may help clinicians with patient 129 management and treatment planning, and additionally, would help them to be prepared for possible 

Real estimates of mortality 143 following COVID-19 infection

Chest CT findings in 146 coronavirus disease-19 (COVID-19): Relationship to duration of infection

COVID-19 outbreak in Italy: experimental chest X-ray scoring 149 system for quantifying and monitoring disease progression

How is immunosuppressive status 152 affecting children and adults in SARS-CoV-2 infection? A systematic review

Time course of lung changes on chest CT 155 during recovery from 2019 novel coronavirus (COVID-19) pneumonia. Radiology