{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T23:40:05Z","timestamp":1724456405981},"reference-count":35,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2021,6,24]],"date-time":"2021-06-24T00:00:00Z","timestamp":1624492800000},"content-version":"vor","delay-in-days":7,"URL":"http:\/\/www.nationalarchives.gov.uk\/doc\/open-government-licence\/version\/3\/"}],"funder":[{"name":"Anhui Province Key Research and Development Plan Project","award":["1804h08020233"],"award-info":[{"award-number":["1804h08020233"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,7,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Objective<\/jats:title><jats:p>This research aims to develop a laboratory model that can accurately distinguish pneumonia from nonpneumonia in patients with COVID-19 and to identify potential protective factors against lung infection.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We recruited 50 patients diagnosed with COVID-19 infection with or without pneumonia. We selected candidate predictors through group comparison and punitive least absolute shrinkage and selection operator (LASSO) analysis. A stepwise logistic regression model was used to distinguish patients with and without pneumonia. Finally, we used a decision-tree method and randomly selected 50% of the patients 1000 times from the same specimen to verify the effectiveness of the model.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We found that the percentage of eosinophils, a high\u2013fluorescence-reticulocyte ratio, and creatinine had better discriminatory power than other factors. Age and underlying diseases were not significant for discrimination. The model correctly discriminated 77.1% of patients. In the final validation step, we observed that the model had an overall predictive rate of 81.3%.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>We developed a laboratory model for COVID-19 pneumonia in patients with mild to moderate symptoms. In the clinical setting, the model will be able to predict and differentiate pneumonia vs nonpneumonia before any lung computed tomography findings. In addition, the percentage of eosinophils, a high\u2013fluorescence-reticulocyte ratio, and creatinine were considered protective factors against lung infection in patients without pneumonia.<\/jats:p><\/jats:sec>","DOI":"10.1093\/labmed\/lmab015","type":"journal-article","created":{"date-parts":[[2021,2,9]],"date-time":"2021-02-09T13:21:53Z","timestamp":1612876913000},"page":"e104-e114","source":"Crossref","is-referenced-by-count":1,"title":["Laboratory Predictors of COVID-19 Pneumonia in Patients with Mild to Moderate Symptoms"],"prefix":"10.1093","volume":"52","author":[{"given":"Jiaxia","family":"Li","sequence":"first","affiliation":[{"name":"Department of Neurology, the Second People\u2019s Hospital of Hefei, Affiliated Hefei Hospital of Anhui Medical University, Hefei, Anhui, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Wan","sequence":"additional","affiliation":[{"name":"Affiliated Psychological Hospital of Anhui Medical University, Hefei Fourth People\u2019s Hospital, Anhui Mental Health Center, Hefei, Anhui, China"},{"name":"National Clinic Research Center for Mental Disorders-Anhui Branch, Anhui, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuan","family":"Feng","sequence":"additional","affiliation":[{"name":"Department of Neurology, the Second People\u2019s Hospital of Hefei, Affiliated Hefei Hospital of Anhui Medical University, Hefei, Anhui, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huilin","family":"Zuo","sequence":"additional","affiliation":[{"name":"Division of Life Science and Medicine, University of Science and Technology of China, Hefei, Anhui, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qian","family":"Zhao","sequence":"additional","affiliation":[{"name":"Division of Life Science and Medicine, University of Science and Technology of China, Hefei, Anhui, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiecheng","family":"Ren","sequence":"additional","affiliation":[{"name":"Division of Life Science and Medicine, University of Science and Technology of China, Hefei, Anhui, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaochu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Division of Life Science and Medicine, University of Science and Technology of China, Hefei, Anhui, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingwu","family":"Xia","sequence":"additional","affiliation":[{"name":"Department of Neurology, the Second People\u2019s Hospital of Hefei, Affiliated Hefei Hospital of Anhui Medical University, Hefei, Anhui, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2021,6,17]]},"reference":[{"issue":"17","key":"2021070821045730200_CIT0001","doi-asserted-by":"crossref","first-page":"644","DOI":"10.1556\/650.2020.31830","article-title":"A koronav\u00edrus okozta COVID\u201319-pand\u00e9mia. 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