{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T16:10:47Z","timestamp":1782576647057,"version":"3.54.5"},"reference-count":28,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2022,12,16]],"date-time":"2022-12-16T00:00:00Z","timestamp":1671148800000},"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. Med."],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Several models have been developed to predict the severity and prognosis of chronic obstructive pulmonary disease (COPD). This study aimed to identify potential predictors and construct a prediction model for COPD severity using biochemical and immunological parameters.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>A total of 6,274 patients with COPD were recruited between July 2010 and July 2018. COPD severity was classified into mild, moderate, severe, and very severe based on the Global Initiative for Chronic Obstructive Lung Disease guidelines. A multivariate logistic regression model was constructed to identify predictors of COPD severity. The predictive ability of the model was assessed by measuring sensitivity, specificity, accuracy, and concordance.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Of 6,274 COPD patients, 2,644, 2,600, and 1,030 had mild\/moderate, severe, and very severe disease, respectively. The factors that could distinguish between mild\/moderate and severe cases were vascular disorders (OR: 1.44; <jats:italic>P<\/jats:italic> &amp;lt; 0.001), high-density lipoprotein (HDL) (OR: 1.83; <jats:italic>P<\/jats:italic> &amp;lt; 0.001), plasma fibrinogen (OR: 1.08; <jats:italic>P<\/jats:italic> = 0.002), fructosamine (OR: 1.12; <jats:italic>P<\/jats:italic> = 0.002), standard bicarbonate concentration (OR: 1.09; <jats:italic>P<\/jats:italic> &amp;lt; 0.001), partial pressure of carbon dioxide (OR: 1.09; <jats:italic>P<\/jats:italic> &amp;lt; 0.001), age (OR: 0.97; <jats:italic>P<\/jats:italic> &amp;lt; 0.001), eosinophil count (OR: 0.66; <jats:italic>P<\/jats:italic> = 0.042), lymphocyte ratio (OR: 0.97; <jats:italic>P<\/jats:italic> &amp;lt; 0.001), and apolipoprotein A1 (OR: 0.56; <jats:italic>P<\/jats:italic> = 0.003). The factors that could distinguish between mild\/moderate and very severe cases were vascular disorders (OR: 1.59; <jats:italic>P<\/jats:italic> &amp;lt; 0.001), HDL (OR: 2.54; <jats:italic>P<\/jats:italic> &amp;lt; 0.001), plasma fibrinogen (OR: 1.10; <jats:italic>P<\/jats:italic> = 0.012), fructosamine (OR: 1.18; <jats:italic>P<\/jats:italic> = 0.001), partial pressure of oxygen (OR: 1.00; <jats:italic>P<\/jats:italic> = 0.007), plasma carbon dioxide concentration (OR: 1.01; <jats:italic>P<\/jats:italic> &amp;lt; 0.001), standard bicarbonate concentration (OR: 1.13; <jats:italic>P<\/jats:italic> &amp;lt; 0.001), partial pressure of carbon dioxide (OR: 1.16; <jats:italic>P<\/jats:italic> &amp;lt; 0.001), age (OR: 0.91; <jats:italic>P<\/jats:italic> &amp;lt; 0.001), sex (OR: 0.71; <jats:italic>P<\/jats:italic> = 0.010), allergic diseases (OR: 0.51; <jats:italic>P<\/jats:italic> = 0.009), eosinophil count (OR: 0.42; <jats:italic>P<\/jats:italic> = 0.014), lymphocyte ratio (OR: 0.93; <jats:italic>P<\/jats:italic> &amp;lt; 0.001), and apolipoprotein A1 (OR: 0.45; <jats:italic>P<\/jats:italic> = 0.005). The prediction model correctly predicted disease severity in 60.17% of patients, and kappa coefficient was 0.35 (95% CI: 0.33\u20130.37).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>This study developed a prediction model for COPD severity based on biochemical and immunological parameters, which should be validated in additional cohorts.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fmed.2022.1073536","type":"journal-article","created":{"date-parts":[[2022,12,16]],"date-time":"2022-12-16T05:01:56Z","timestamp":1671166916000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Development of a model for predicting the severity of chronic obstructive pulmonary 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