Risk factors and nomogram for predicting mechanical ventilation in severe pneumonia Article Swipe
Yuquan Chen
,
Yufei Zhang
,
Dehong Huo
,
Dan Luo
,
Weiwei Chen
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3389/fmed.2025.1598952
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3389/fmed.2025.1598952
Background 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. Methods 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 h 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). Results 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(O₂)], central venous oxygen saturation (ScvO₂), and procalcitonin (PCT) levels, along with higher partial pressure of carbon dioxide [p(CO₂)], alveolar-arterial oxygen gradient [p(A-a)O₂], and APACHE II scores (all p < 0.01). Age, OI, p(O₂), p(CO₂), and p(A-a)O₂ were independent predictors included in the nomogram. The model showed excellent discrimination (area under the ROC curve, AUC = 0.819), calibration (concordance index, C-index = 0.805), and substantial clinical utility. Conclusion This retrospective study suggests that age, OI, p(O₂), p(CO₂), and p(A-a)O₂ 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.
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- https://doi.org/10.3389/fmed.2025.1598952
- https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1598952/pdf
- OA Status
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- 23
- OpenAlex ID
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Risk factors and nomogram for predicting mechanical ventilation in severe pneumoniaWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
- Publication date
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2025-09-15Full publication date if available
- Authors
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Yuquan Chen, Yufei Zhang, Dehong Huo, Dan Luo, Weiwei ChenList of authors in order
- Landing page
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https://doi.org/10.3389/fmed.2025.1598952Publisher landing page
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https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1598952/pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1598952/pdfDirect OA link when available
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0Total citation count in OpenAlex
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| referenced_works | https://openalex.org/W4317717850, https://openalex.org/W3163793977, https://openalex.org/W3198227039, https://openalex.org/W4390584378, https://openalex.org/W4282929425, https://openalex.org/W3087999014, https://openalex.org/W3134933559, https://openalex.org/W4292229870, https://openalex.org/W3173996885, https://openalex.org/W2162194708, https://openalex.org/W2099361102, https://openalex.org/W3168302439, https://openalex.org/W4404236433, https://openalex.org/W2444043200, https://openalex.org/W3115138785, https://openalex.org/W2138098696, https://openalex.org/W2610169724, https://openalex.org/W4289962909, https://openalex.org/W4412980587, https://openalex.org/W4388302827, https://openalex.org/W4412724919, https://openalex.org/W4412682425, https://openalex.org/W66687513 |
| referenced_works_count | 23 |
| abstract_inverted_index.= | 218, 224 |
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| abstract_inverted_index.h | 91 |
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| abstract_inverted_index.24 | 90 |
| abstract_inverted_index.51 | 142 |
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| abstract_inverted_index.216 | 131 |
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| abstract_inverted_index.(ROC) | 117 |
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| abstract_inverted_index.operating | 115 |
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| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5100367124 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| corresponding_institution_ids | https://openalex.org/I182386381, https://openalex.org/I4210117574 |
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| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |