Development and validation of a clinical prediction model for in-hospital mortality of severe pneumonia based on machine learning Article Swipe
Kai Xie
,
Xiajin Huang
,
Zhen Li
,
Wenjing Yin
,
Xiaoxuan He
,
Xinyu Miao
,
Haifeng Wang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3389/fphar.2025.1660893
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3389/fphar.2025.1660893
Objective We aimed to develop an interpretable model to predict the mortality risk for severe pneumonia patients. Methods The study retrospectively employed data from severe pneumonia patients at two hospitals as the training set for the model development. Patients with severe pneumonia admitted from the same two hospitals were prospectively included as the test set for the model evaluation. A total of 115 candidate features were extracted based on clinical relevance and existing literature. The least absolute shrinkage and selection operator (LASSO) regression was applied to select features for the establishment of five models: logistic regression (LR), support vector machine (SVM), decision tree (DT), random forest (RF) and extreme gradient boosting (XGBoost). The performance of the models was assessed from discrimination, calibration and clinical practicability. The optimal model was screened out, and SHapley Additive exPlanation (SHAP) method was used to explain. Results A total of 323 eligible patients with severe pneumonia were enrolled, including 226 patients in the training set and 97 in test set. In comparison to the other four models, the XGBoost model demonstrated the third highest area under the receiver operating characteristic (AUROC), along with optimal calibration and clinical practicability. The SHAP value of the XGBoost model indicated that the application of retention catheterization was identified as the most important influential predictor in the model, followed by oral Chinese herbal decoction, blood urea nitrogen (BUN) level, age, application of tracheotomy, complication of septic shock, and TCM syndrome (pathogenic qi falling into and prostration syndrome). Conclusion Older age, increased BUN level, complication of septic shock, tracheotomy application, retention catheterization application, oral Chinese herbal decoction, and TCM syndrome (pathogenic qi falling into and prostration syndrome) may be potential risk factors that affect mortality in severe pneumonia, while application of tracheotomy and oral Chinese herbal decoction were associated with reduced mortality. The XGBoost model exhibits superior overall performance in predicting hospital mortality risk for severe pneumonia, greater than traditional scoring systems such as Pneumonia Severity Index (PSI), Sequential Organ Failure Assessment (SOFA), and Acute Physiology and Chronic Health Evaluation II (APACHE II), which assists clinicians in prognostic assessment, resulting in improved therapeutic strategies and optimal resource allocation for patients.
Related Topics
Concepts
Medicine
Pneumonia
Logistic regression
Machine learning
Decision tree
Receiver operating characteristic
Random forest
Artificial intelligence
Test set
Support vector machine
Training set
Intensive care medicine
Calibration
Clinical trial
Emergency medicine
Gradient boosting
Mortality rate
Area under the curve
Predictive modelling
Test data
Erythrocyte sedimentation rate
Decision tree learning
Regression
Complication
Bacterial pneumonia
Prospective cohort study
Confidence interval
Medical record
Data set
Regression analysis
Clinical Practice
Decision tree model
Metadata
- Type
- article
- Landing Page
- https://doi.org/10.3389/fphar.2025.1660893
- https://public-pages-files-2025.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1660893/pdf
- OA Status
- gold
- References
- 51
- OpenAlex ID
- https://openalex.org/W7106647533
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W7106647533Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3389/fphar.2025.1660893Digital Object Identifier
- Title
-
Development and validation of a clinical prediction model for in-hospital mortality of severe pneumonia based on machine learningWork title
- Type
-
articleOpenAlex work type
- Publication year
-
2025Year of publication
- Publication date
-
2025-11-26Full publication date if available
- Authors
-
Kai Xie, Xiajin Huang, Zhen Li, Wenjing Yin, Xiaoxuan He, Xinyu Miao, Haifeng WangList of authors in order
- Landing page
-
https://doi.org/10.3389/fphar.2025.1660893Publisher landing page
- PDF URL
-
https://public-pages-files-2025.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1660893/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://public-pages-files-2025.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2025.1660893/pdfDirect OA link when available
- Concepts
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Medicine, Pneumonia, Logistic regression, Machine learning, Decision tree, Receiver operating characteristic, Random forest, Artificial intelligence, Test set, Support vector machine, Training set, Intensive care medicine, Calibration, Clinical trial, Emergency medicine, Gradient boosting, Mortality rate, Area under the curve, Predictive modelling, Test data, Erythrocyte sedimentation rate, Decision tree learning, Regression, Complication, Bacterial pneumonia, Prospective cohort study, Confidence interval, Medical record, Data set, Regression analysis, Clinical Practice, Decision tree modelTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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51Number of works referenced by this work
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| abstract_inverted_index.Assessment | 330 |
| abstract_inverted_index.Conclusion | 247 |
| abstract_inverted_index.Evaluation | 338 |
| abstract_inverted_index.Physiology | 334 |
| abstract_inverted_index.Sequential | 327 |
| abstract_inverted_index.allocation | 356 |
| abstract_inverted_index.associated | 297 |
| abstract_inverted_index.clinicians | 344 |
| abstract_inverted_index.comparison | 166 |
| abstract_inverted_index.decoction, | 223, 265 |
| abstract_inverted_index.identified | 208 |
| abstract_inverted_index.mortality. | 300 |
| abstract_inverted_index.pneumonia, | 286, 315 |
| abstract_inverted_index.predicting | 309 |
| abstract_inverted_index.prognostic | 346 |
| abstract_inverted_index.regression | 82, 95 |
| abstract_inverted_index.strategies | 352 |
| abstract_inverted_index.syndrome). | 246 |
| abstract_inverted_index.(pathogenic | 240, 269 |
| abstract_inverted_index.application | 203, 230, 288 |
| abstract_inverted_index.assessment, | 347 |
| abstract_inverted_index.calibration | 121, 189 |
| abstract_inverted_index.evaluation. | 58 |
| abstract_inverted_index.exPlanation | 134 |
| abstract_inverted_index.influential | 213 |
| abstract_inverted_index.literature. | 73 |
| abstract_inverted_index.performance | 113, 307 |
| abstract_inverted_index.prostration | 245, 274 |
| abstract_inverted_index.therapeutic | 351 |
| abstract_inverted_index.tracheotomy | 257, 290 |
| abstract_inverted_index.traditional | 318 |
| abstract_inverted_index.application, | 258, 261 |
| abstract_inverted_index.complication | 233, 253 |
| abstract_inverted_index.demonstrated | 175 |
| abstract_inverted_index.development. | 37 |
| abstract_inverted_index.tracheotomy, | 232 |
| abstract_inverted_index.establishment | 90 |
| abstract_inverted_index.interpretable | 6 |
| abstract_inverted_index.prospectively | 49 |
| abstract_inverted_index.characteristic | 184 |
| abstract_inverted_index.catheterization | 206, 260 |
| abstract_inverted_index.discrimination, | 120 |
| abstract_inverted_index.practicability. | 124, 192 |
| abstract_inverted_index.retrospectively | 20 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile |