Nomogram to identify severe Coronavirus Disease 2019 (COVID-19) based on initial clinical and CT characteristics: a multi-center study

Abstract

Background: To develop and validate a nomogram for early identification of severe coronavirus disease 2019 (COVID-19) based on initial clinical and CT characteristics.Methods: The initial clinical and CT imaging data of 217 patients with COVID-19 were analyzed retrospectively from January to March 2020. 217 patients with 146 mild cases and 71 severe cases were randomly divided into training and validation cohorts. Independent risk factors were selected to construct the nomogram for predicting severe COVID-19. Nomogram performance in terms of discrimination and calibration ability was evaluated using the area under the curve (AUC), calibration curve, decision curve, clinical impact curve and risk chart.Results: In the training cohort, the severity score of lung in the severe group (7, interquartile range [IQR]:5-9) was significantly higher than that of the mild group (4, IQR:2-5) (P < 0.001). Age, density, mosaic perfusion sign and severity score of lung were independent risk factors for severe COVID-19. The nomogram had a AUC of 0.929 (95% CI, 0.889-0.969), sensitivity of 84.0% and specificity of 86.3%, in the training cohort, and a AUC of 0.936 (95% CI, 0.867-1.000), sensitivity of 90.5% and specificity of 88.6% in the validation cohort. The calibration curve, decision curve, clinical impact curve and risk chart showed that nomogram had high accuracy and superior net benefit in predicting severe COVID-19.Conclusion: The nomogram incorporating initial clinical and CT characteristics may help to identify the severe patients with COVID-19 in the early stage.

Prognostic value of CT integrated with clinical and laboratory data during the first peak of the COVID-19 pandemic in Northern Italy: A nomogram to predict unfavorable outcome

“…To our knowledge, only two recent retrospective studies have proposed the construction of a nomogram in order to identify the predictors of severe coronavirus disease [47,48]. The first study used multivariate analysis to evaluate various clinical and laboratory parameters.…”

“…However, the author did not include CT imaging among the risk indicators [47]. In contrast, CT was considered in the second paper, where initial clinical data and CT imaging data were evaluated in 217 COVID-19 patients [48]. Patients were classified into two groups; mild and severe disease.…”

Can we estimate cytokine storm from initial computed tomography images of Coronavirus disease-2019 patients?

“…It has been shown in many studies that the lesions in COV-ID-19 pneumonia classically show a more peripheral distribution (7,9). In the literature, there are data showing that the central or diffuse distribution is more common in cases with a severe prognosis (15). Therefore, we believed that the distribution characteristic on admission CT might change the predictive value of the CT score.…”

“…In another study investigating the predictive value of CT score as to mortality and disease severity, the parameter of age and high CT score were shown to be independent predictors of death and severity of the disease (6). In a study by Yu et al, multivariate logistic regression analysis showed that the lung severity score calculated by age, lesion density, and involvement rate were independent predictors for predicting the severity of the disease, while the presence of accompanying comorbidity was not a predictive factor (15). In our study, the score detected from admission CT was higher in the males and elderly included in the cytokinestorm group.…”

Development and validation of nomogram to predict severe illness requiring intensive care follow up in hospitalized COVID-19 cases

“…In the literature, there are studies proposing nomograms and/or different models to predict serious illness or death in COVID-19 patients [ 19 , 21 , 22 , 24 , 25 , 35 – 37 ]. Age was reported as a predictor for severe COVID-19 or mortality in previous studies and is included in most of the prediction models [ 16 – 21 , 24 , 25 , 32 , 35 , 38 , 39 ]. We found an increased risk of severe infection requiring ICU admission in the patients 56.5 years of age and older.…”