Document Type : Research articles


1 General and Emergency Surgery Department, Bufalini Hospital, Cesena, Italy

2 Intensive Care Unit, ASST Papa Giovanni XXIII, Bergamo, Italy

3 Emergency Surgery Unit, State University of Pisa, Cisanello Hospital, Pisa, Italy

4 Internal Medicine Unit, Bufalini Hospital, Cesena, Italy

5 Intensive Care Unit, Bufalini Hospital, Cesena, Italy


Background: The Coronavirus Disease 2019 (COVID-19) pandemic has necessitated the alteration of the organization of entire hos- pitals to try to prevent them from becoming epidemiological clusters. The adopted diagnostic tools lack sensitivity or specificity. Objectives: The aim of the study was to create an easy-to-get risk score (Ri.S.I.Co., risk score for infection with the new coronavirus) developed on the field to stratify patients admitted to hospitals according to their risk of COVID-19 infection.
Methods: In this prospective study, we included all patients who were consecutively admitted to the suspected COVID-19 depart- ment of the Bufalini Hospital, Cesena (Italy). All clinical, radiological, and laboratory predictors were included in the multivariate logistic regression model to create a risk model. A simplified model was internally and externally validated, and two score thresh- olds for stratifying the probability of COVID-19 infection were introduced.
Results: From 11th March to 5th April 2020, 200 patients were consecutively admitted. A Ri.S.I.Co lower than 2 showed a higher sensitivity than SARS-Cov-2 nucleic acid detection (96.2% vs. 65.4%; P < 0.001). The presence of ground-glass pattern on the lung-CT scan had a lower sensitivity than a Ri.S.I.Co lower than 2 (88.5% vs. 96.2%; P < 0.001) and a lower specificity than a Ri.S.I.Co higher than 6 (75.0% vs. 96.9%; P < 0.001).
Conclusions: We believe that the Ri.S.I.Co could allow to stratify admitted patients according to their risk, preventing hospitals from becoming the main COVID-19 carriers themselves. Furthermore, it could guide clinicians in starting therapies early in severe- onset cases with a high probability of COVID-19, before molecular SARS-CoV-2 infection is confirmed.


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