{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T23:37:35Z","timestamp":1792193855725,"version":"4.4.0"},"reference-count":23,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,9,15]],"date-time":"2025-09-15T00:00:00Z","timestamp":1757894400000},"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>Severe pneumonia often leads to acute respiratory failure requiring mechanical ventilation (MV), significantly increasing patient morbidity and mortality. Early prediction of MV requirement could optimize patient management and resource allocation. This study aimed to identify key risk factors and develop a practical nomogram model to predict the need for mechanical ventilation among patients with severe pneumonia.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>In this retrospective study, patients with severe pneumonia admitted between January 2021 and December 2024 were analyzed at a single tertiary institution. Patients were stratified based on the use of MV within 24\u202fh of admission. Multivariable logistic regression identified independent predictors of MV, which were used to construct a nomogram. Model performance was evaluated via receiver operating characteristic (ROC) curves, bootstrap validation, calibration, and decision curve analysis (DCA).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>A total of 216 patients were included, with 165 in the MV group and 51 in the non-MV group. Patients requiring MV were significantly older and demonstrated lower oxygenation index (OI), partial pressure of oxygen [p(O\u2082)], central venous oxygen saturation (ScvO\u2082), and procalcitonin (PCT) levels, along with higher partial pressure of carbon dioxide [p(CO\u2082)], alveolar-arterial oxygen gradient [p(A-a)O\u2082], and APACHE II scores (all <jats:italic>p<\/jats:italic>\u202f&amp;lt;\u202f0.01). Age, OI, p(O\u2082), p(CO\u2082), and p(A-a)O\u2082 were independent predictors included in the nomogram. The model showed excellent discrimination (area under the ROC curve, AUC\u202f=\u202f0.819), calibration (concordance index, C-index\u202f=\u202f0.805), and substantial clinical utility.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>This retrospective study suggests that age, OI, p(O\u2082), p(CO\u2082), and p(A-a)O\u2082 could help predict MV in severe pneumonia. The proposed nomogram might offer good predictive accuracy, calibration, and clinical utility, potentially aiding early risk stratification. Prospective multicenter validation is needed to confirm its generalizability and clinical utility.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fmed.2025.1598952","type":"journal-article","created":{"date-parts":[[2025,9,15]],"date-time":"2025-09-15T04:13:50Z","timestamp":1757909630000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Risk factors and nomogram for predicting mechanical ventilation in severe 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