{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T01:20:57Z","timestamp":1778635257505,"version":"3.51.4"},"reference-count":17,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2020,5,16]],"date-time":"2020-05-16T00:00:00Z","timestamp":1589587200000},"content-version":"vor","delay-in-days":15,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,5,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Background<\/jats:title>\n                  <jats:p>There is currently a lack of nonspecific laboratory indicators as a quantitative standard to distinguish between the 2019 coronavirus disease (COVID-19) and an influenza A or B virus infection. Thus, the aim of this study was to establish a nomogram to detect COVID-19.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>A nomogram was established using data collected from 457 patients (181 with COVID-19 and 276 with influenza A or B infection) in China. The nomogram used age, lymphocyte percentage, and monocyte count to differentiate COVID-19 from influenza.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Our nomogram predicted probabilities of COVID-19 with an area under the receiver operating characteristic curve of 0.913 (95% confidence interval [CI], 0.883\u20130.937), greater than that of the lymphocyte:monocyte ratio (0.849; 95% CI, 0.812\u20130.880; P = .0007), lymphocyte percentage (0.808; 95% CI, 0.768\u20130.843; P &amp;lt; .0001), monocyte count (0.780; 95% CI, 0.739\u20130.817; P &amp;lt; .0001), or age (0.656; 95% CI, 0.610\u20130.699; P &amp;lt; .0001). The predicted probability conformed to the real observation outcomes of COVID-19, according to the calibration curves.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>We found that age, lymphocyte percentage, and monocyte count are risk factors for the early-stage prediction of patients infected with the 2019 novel coronavirus. As such, our research provides a useful test for doctors to differentiate COVID-19 from influenza.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/ofid\/ofaa169","type":"journal-article","created":{"date-parts":[[2020,5,16]],"date-time":"2020-05-16T21:12:31Z","timestamp":1589663551000},"source":"Crossref","is-referenced-by-count":11,"title":["Differentiating Between 2019 Novel Coronavirus Pneumonia and Influenza Using a Nonspecific Laboratory Marker\u2013Based Dynamic Nomogram"],"prefix":"10.1093","volume":"7","author":[{"given":"Linghang","family":"Wang","sequence":"first","affiliation":[{"name":"Emergency Department of Infectious Diseases of Beijing Ditan Hospital, Capital Medical University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yao","family":"Liu","sequence":"first","affiliation":[{"name":"Center of Integrative Medicine, Beijing Ditan Hospital, Capital Medical University, Beijing, 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