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Two hundred seventeen 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      In the training cohort, the severity score of lung in the severe group (7, interquartile range [IQR]:5\u20139) was significantly higher than that of the mild group (4, IQR,2\u20135) (\n                      <jats:italic>P<\/jats:italic>\n                      \u2009&lt;\u20090.001). 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\u20130.969), sensitivity of 84.0% and specificity of 86.3%, in the training cohort, and a AUC of 0.936 (95% CI, 0.867\u20131.000), 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.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The nomogram incorporating initial clinical and CT characteristics may help to identify the severe patients with COVID-19 in the early stage.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12880-020-00513-z","type":"journal-article","created":{"date-parts":[[2020,10,2]],"date-time":"2020-10-02T08:03:02Z","timestamp":1601625782000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Nomogram to identify severe coronavirus disease 2019 (COVID-19) based on initial clinical and CT characteristics: a multi-center study"],"prefix":"10.1186","volume":"20","author":[{"given":"Yixing","family":"Yu","sequence":"first","affiliation":[]},{"given":"Ximing","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Min","family":"Li","sequence":"additional","affiliation":[]},{"given":"Lan","family":"Gu","sequence":"additional","affiliation":[]},{"given":"Zongyu","family":"Xie","sequence":"additional","affiliation":[]},{"given":"Wenhao","family":"Gu","sequence":"additional","affiliation":[]},{"given":"Feng","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Yaxing","family":"Bao","sequence":"additional","affiliation":[]},{"given":"Rongrong","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Su","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Mengjie","family":"Hu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4656-1802","authenticated-orcid":false,"given":"Chunhong","family":"Hu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,10,2]]},"reference":[{"key":"513_CR1","doi-asserted-by":"publisher","unstructured":"Zu ZY, Jiang MD, Xu PP, et al. 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And the need to obtain informed consent was waived by the IRBs because of de-identified data involving no potential risk to patients and no link between the patients and the researchers.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"111"}}