Construction of a prediction model for severe pneumonia complicate with respiratory failure
Objective To explore predictive factors of severe community-acquired pneumonia (CAP) complicated with respiratory failure (RF) and to develop and internally validate a clinical prediction model. MethodsA retrospective study was conducted on 350 patients with severe CAP admitted to Tianyou Hospital A...
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Shanghai Chinese Clinical Medicine Press Co., Ltd.
2025-06-01
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| Series: | Zhongguo Linchuang Yixue |
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| Online Access: | https://www.c-jcm.com/article/doi/10.12025/j.issn.1008-6358.2025.20250102 |
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| author | Siyu GAO Sheng ZHANG Xi CHEN Zhixia ZHANG Yumei YANG |
| author_facet | Siyu GAO Sheng ZHANG Xi CHEN Zhixia ZHANG Yumei YANG |
| author_sort | Siyu GAO |
| collection | DOAJ |
| description | Objective To explore predictive factors of severe community-acquired pneumonia (CAP) complicated with respiratory failure (RF) and to develop and internally validate a clinical prediction model. MethodsA retrospective study was conducted on 350 patients with severe CAP admitted to Tianyou Hospital Affiliated to Wuhan University of Science and Technology from September 2022 to December 2024. Patients were randomly divided into a training set (n＝245) and a validation set (n＝105) in a 7∶3 ratio, and further categorized into RF and non-RF groups. LASSO regression was applied to optimize variable selection. Multivariate logistic analysis was used to construct the prediction model, followed by internal validation. ResultsUnivariate regression analysis identified male, hypertension, diabetes, coronary heart disease, age, CURB-65 score, white blood cell count, neutrophil count, C-reactive protein (CRP), serum amyloid A, procalcitonin, and hospital stay as risk factors for RF in severe CAP, while albumin level was a protective factor. LASSO regression selected CURB-65 score, albumin level, and CRP for inclusion in the final model. The area under the receiver operating characteristic curve was 0.903 in the training set and 0.919 in the validation set. Calibration curve analysis demonstrated excellent agreement between predicted and observed probabilities in both sets, and Hosmer-Lemeshow goodness-of-fit tests indicated no significant deviations. Threshold probabilities ranged from 0.01 to 0.99 in both training and validation sets. ConclusionsCURB-65 score, albumin level, and CRP are independent predictors of RF in severe CAP. The clinical prediction model based on these factors exhibits strong discrimination, calibration, goodness-of-fit, and clinical utility. |
| format | Article |
| id | doaj-art-33602ce8b23c4f6c8ba9cb8b301cbdc6 |
| institution | Kabale University |
| issn | 1008-6358 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Shanghai Chinese Clinical Medicine Press Co., Ltd. |
| record_format | Article |
| series | Zhongguo Linchuang Yixue |
| spelling | doaj-art-33602ce8b23c4f6c8ba9cb8b301cbdc62025-08-20T03:28:51ZengShanghai Chinese Clinical Medicine Press Co., Ltd.Zhongguo Linchuang Yixue1008-63582025-06-0132344945710.12025/j.issn.1008-6358.2025.2025010220250102Construction of a prediction model for severe pneumonia complicate with respiratory failureSiyu GAO0Sheng ZHANG1Xi CHEN2Zhixia ZHANG3Yumei YANG4Department of Respiratory and Critical Care Medicine, Tianyou Hospital Affiliated to Wuhan University of Science and Technology, Wuhan 430064, Hubei, ChinaCancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, Hubei, ChinaBrooks College (Sunnyvale), California 94089, the United StatesDepartment of Respiratory and Critical Care Medicine, Tianyou Hospital Affiliated to Wuhan University of Science and Technology, Wuhan 430064, Hubei, ChinaDepartment of Respiratory and Critical Care Medicine, Tianyou Hospital Affiliated to Wuhan University of Science and Technology, Wuhan 430064, Hubei, ChinaObjective To explore predictive factors of severe community-acquired pneumonia (CAP) complicated with respiratory failure (RF) and to develop and internally validate a clinical prediction model. MethodsA retrospective study was conducted on 350 patients with severe CAP admitted to Tianyou Hospital Affiliated to Wuhan University of Science and Technology from September 2022 to December 2024. Patients were randomly divided into a training set (n＝245) and a validation set (n＝105) in a 7∶3 ratio, and further categorized into RF and non-RF groups. LASSO regression was applied to optimize variable selection. Multivariate logistic analysis was used to construct the prediction model, followed by internal validation. ResultsUnivariate regression analysis identified male, hypertension, diabetes, coronary heart disease, age, CURB-65 score, white blood cell count, neutrophil count, C-reactive protein (CRP), serum amyloid A, procalcitonin, and hospital stay as risk factors for RF in severe CAP, while albumin level was a protective factor. LASSO regression selected CURB-65 score, albumin level, and CRP for inclusion in the final model. The area under the receiver operating characteristic curve was 0.903 in the training set and 0.919 in the validation set. Calibration curve analysis demonstrated excellent agreement between predicted and observed probabilities in both sets, and Hosmer-Lemeshow goodness-of-fit tests indicated no significant deviations. Threshold probabilities ranged from 0.01 to 0.99 in both training and validation sets. ConclusionsCURB-65 score, albumin level, and CRP are independent predictors of RF in severe CAP. The clinical prediction model based on these factors exhibits strong discrimination, calibration, goodness-of-fit, and clinical utility.https://www.c-jcm.com/article/doi/10.12025/j.issn.1008-6358.2025.20250102severe pneumoniarespiratory failureclinical predictive modelinternal validationlasso regression |
| spellingShingle | Siyu GAO Sheng ZHANG Xi CHEN Zhixia ZHANG Yumei YANG Construction of a prediction model for severe pneumonia complicate with respiratory failure Zhongguo Linchuang Yixue severe pneumonia respiratory failure clinical predictive model internal validation lasso regression |
| title | Construction of a prediction model for severe pneumonia complicate with respiratory failure |
| title_full | Construction of a prediction model for severe pneumonia complicate with respiratory failure |
| title_fullStr | Construction of a prediction model for severe pneumonia complicate with respiratory failure |
| title_full_unstemmed | Construction of a prediction model for severe pneumonia complicate with respiratory failure |
| title_short | Construction of a prediction model for severe pneumonia complicate with respiratory failure |
| title_sort | construction of a prediction model for severe pneumonia complicate with respiratory failure |
| topic | severe pneumonia respiratory failure clinical predictive model internal validation lasso regression |
| url | https://www.c-jcm.com/article/doi/10.12025/j.issn.1008-6358.2025.20250102 |
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