{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T16:03:30Z","timestamp":1753891410572,"version":"3.41.2"},"reference-count":38,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2022,7,25]],"date-time":"2022-07-25T00:00:00Z","timestamp":1658707200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Microbiol."],"abstract":"<jats:sec><jats:title>Objective<\/jats:title><jats:p>We developed and validated a prediction model based on individuals' risk profiles to predict the severity of lung involvement and death in patients hospitalized with coronavirus disease 2019 (COVID-19) infection.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>In this retrospective study, we studied hospitalized COVID-19 patients with data on chest CT scans performed during hospital stay (February 2020-April 2021) in a training dataset (TD) (<jats:italic>n<\/jats:italic> = 2,251) and an external validation dataset (eVD) (<jats:italic>n<\/jats:italic> = 993). We used the most relevant demographical, clinical, and laboratory variables (<jats:italic>n<\/jats:italic> = 25) as potential predictors of COVID-19-related outcomes. The primary and secondary endpoints were the severity of lung involvement quantified as mild (\u226425%), moderate (26\u201350%), severe (&amp;gt;50%), and in-hospital death, respectively. We applied random forest (RF) classifier, a machine learning technique, and multivariable logistic regression analysis to study our objectives.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In the TD and the eVD, respectively, the mean [standard deviation (<jats:italic>SD<\/jats:italic>)] age was 57.9 (18.0) and 52.4 (17.6) years; patients with severe lung involvement [<jats:italic>n<\/jats:italic> (%):185 (8.2) and 116 (11.7)] were significantly older [mean (<jats:italic>SD<\/jats:italic>) age: 64.2 (16.9), and 56.2 (18.9)] than the other two groups (mild and moderate). The mortality rate was higher in patients with severe (64.9 and 38.8%) compared to moderate (5.5 and 12.4%) and mild (2.3 and 7.1%) lung involvement. The RF analysis showed age, C reactive protein (CRP) levels, and duration of hospitalizations as the three most important predictors of lung involvement severity at the time of the first CT examination. Multivariable logistic regression analysis showed a significant strong association between the extent of the severity of lung involvement (continuous variable) and death; adjusted odds ratio (OR): 9.3; 95% CI: 7.1\u201312.1 in the TD and 2.6 (1.8\u20133.5) in the eVD.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>In hospitalized patients with COVID-19, the severity of lung involvement is a strong predictor of death. Age, CRP levels, and duration of hospitalizations are the most important predictors of severe lung involvement. A simple prediction model based on available clinical and imaging data provides a validated tool that predicts the severity of lung involvement and death probability among hospitalized patients with COVID-19.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fmicb.2022.893750","type":"journal-article","created":{"date-parts":[[2022,7,26]],"date-time":"2022-07-26T09:22:30Z","timestamp":1658827350000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["The risk profile of patients with COVID-19 as predictors of lung lesions severity and mortality\u2014Development and validation of a prediction model"],"prefix":"10.3389","volume":"13","author":[{"given":"Ezat","family":"Rahimi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mina","family":"Shahisavandi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Albert Cid","family":"Royo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad","family":"Azizi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Said","family":"el Bouhaddani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Naseh","family":"Sigari","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Miriam","family":"Sturkenboom","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fariba","family":"Ahmadizar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2022,7,25]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"479","DOI":"10.1007\/s10140-022-02034-4","article-title":"Covid Visual Assessment Scale (\u201cCo.V.A.Sc.\u201d): quantification of COVID-19 disease extent on admission chest computed tomography (CT) in the prediction of clinical outcome-a retrospective analysis of 273 patients","volume":"29","author":"Arkoudis","year":"2022","journal-title":"Emerg. 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