Risk prediction models for mortality in patients with severe pneumonia: a systematic review and meta-analysis
BackgroundThe number of risk prediction models for mortality in patients with severe pneumonia (SP) is increasing, while the quality and clinical applicability of these models remain unclear. This study aimed to systematically review published research on risk prediction models for mortality in pati...
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2025.1564545/full |
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| author | Xiaoyu Wang Xiaoyu Wang Zhenzhen Feng Zhenzhen Feng Zhenzhen Feng Lu Wang Lu Wang Wenrui Liu Wenrui Liu Jiansheng Li Jiansheng Li Jiansheng Li |
| author_facet | Xiaoyu Wang Xiaoyu Wang Zhenzhen Feng Zhenzhen Feng Zhenzhen Feng Lu Wang Lu Wang Wenrui Liu Wenrui Liu Jiansheng Li Jiansheng Li Jiansheng Li |
| author_sort | Xiaoyu Wang |
| collection | DOAJ |
| description | BackgroundThe number of risk prediction models for mortality in patients with severe pneumonia (SP) is increasing, while the quality and clinical applicability of these models remain unclear. This study aimed to systematically review published research on risk prediction models for mortality in patients with SP.MethodsPubMed, Embase, Cochrane Library, and Web of Science were searched from inception to August 31, 2024. Data from selected studies were extracted, including study design, participants, diagnostic criteria, sample size, predictors, model development, and performance. The prediction model risk of bias assessment tool was used to assess the risk of bias and applicability. A meta-analysis of the area under the curve (AUC) values from validated models was conducted using Stata 17.0 software.ResultsA total of 22 prediction models from 18 studies were included in this review, including 15 logistic regression models, two cox proportional regression hazards models, two classification and regression trees, one light gradient boosting machine, and one multilayer perceptron. The reported AUC values ranged from 0.713 to 0.952. Seventeen studies were found to have a high risk of bias, primarily due to inappropriate data sources and poor reporting of the analysis domain. The pooled AUC value of five validated models was 0.85 (95% confidence interval: 0.81–0.88), indicating a fair level of discrimination.ConclusionAlthough the included studies reported that the risk prediction models for mortality in patients with SP exhibited a certain level of discriminative ability, most of these models were found to have a high risk of bias. Future studies should focus on developing new models with larger sample sizes, rigorous study designs, and multicenter external validation.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/view/CRD42024589877, identifier: CRD42024589877. |
| format | Article |
| id | doaj-art-75727f47f94c40ccac7bda14f894ed8b |
| institution | DOAJ |
| issn | 2296-858X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Medicine |
| spelling | doaj-art-75727f47f94c40ccac7bda14f894ed8b2025-08-20T02:50:33ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2025-07-011210.3389/fmed.2025.15645451564545Risk prediction models for mortality in patients with severe pneumonia: a systematic review and meta-analysisXiaoyu Wang0Xiaoyu Wang1Zhenzhen Feng2Zhenzhen Feng3Zhenzhen Feng4Lu Wang5Lu Wang6Wenrui Liu7Wenrui Liu8Jiansheng Li9Jiansheng Li10Jiansheng Li11Department of Respiratory Diseases, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, Henan, ChinaThe First Clinical Medical College, Henan University of Chinese Medicine, Zhengzhou, Henan, ChinaDepartment of Respiratory Diseases, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, Henan, ChinaThe First Clinical Medical College, Henan University of Chinese Medicine, Zhengzhou, Henan, ChinaCollaborative Innovation Center for Chinese Medicine and Respiratory Diseases Co-constructed by Henan Province & Education Ministry of P.R. China/Henan Key Laboratory of Chinese Medicine for Respiratory Diseases, Henan University of Chinese Medicine, Zhengzhou, Henan, ChinaDepartment of Respiratory Diseases, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, Henan, ChinaThe First Clinical Medical College, Henan University of Chinese Medicine, Zhengzhou, Henan, ChinaDepartment of Respiratory Diseases, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, Henan, ChinaThe First Clinical Medical College, Henan University of Chinese Medicine, Zhengzhou, Henan, ChinaDepartment of Respiratory Diseases, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, Henan, ChinaThe First Clinical Medical College, Henan University of Chinese Medicine, Zhengzhou, Henan, ChinaCollaborative Innovation Center for Chinese Medicine and Respiratory Diseases Co-constructed by Henan Province & Education Ministry of P.R. China/Henan Key Laboratory of Chinese Medicine for Respiratory Diseases, Henan University of Chinese Medicine, Zhengzhou, Henan, ChinaBackgroundThe number of risk prediction models for mortality in patients with severe pneumonia (SP) is increasing, while the quality and clinical applicability of these models remain unclear. This study aimed to systematically review published research on risk prediction models for mortality in patients with SP.MethodsPubMed, Embase, Cochrane Library, and Web of Science were searched from inception to August 31, 2024. Data from selected studies were extracted, including study design, participants, diagnostic criteria, sample size, predictors, model development, and performance. The prediction model risk of bias assessment tool was used to assess the risk of bias and applicability. A meta-analysis of the area under the curve (AUC) values from validated models was conducted using Stata 17.0 software.ResultsA total of 22 prediction models from 18 studies were included in this review, including 15 logistic regression models, two cox proportional regression hazards models, two classification and regression trees, one light gradient boosting machine, and one multilayer perceptron. The reported AUC values ranged from 0.713 to 0.952. Seventeen studies were found to have a high risk of bias, primarily due to inappropriate data sources and poor reporting of the analysis domain. The pooled AUC value of five validated models was 0.85 (95% confidence interval: 0.81–0.88), indicating a fair level of discrimination.ConclusionAlthough the included studies reported that the risk prediction models for mortality in patients with SP exhibited a certain level of discriminative ability, most of these models were found to have a high risk of bias. Future studies should focus on developing new models with larger sample sizes, rigorous study designs, and multicenter external validation.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/view/CRD42024589877, identifier: CRD42024589877.https://www.frontiersin.org/articles/10.3389/fmed.2025.1564545/fullsevere pneumoniamortalityprediction modelsystematic reviewmeta-analysis |
| spellingShingle | Xiaoyu Wang Xiaoyu Wang Zhenzhen Feng Zhenzhen Feng Zhenzhen Feng Lu Wang Lu Wang Wenrui Liu Wenrui Liu Jiansheng Li Jiansheng Li Jiansheng Li Risk prediction models for mortality in patients with severe pneumonia: a systematic review and meta-analysis Frontiers in Medicine severe pneumonia mortality prediction model systematic review meta-analysis |
| title | Risk prediction models for mortality in patients with severe pneumonia: a systematic review and meta-analysis |
| title_full | Risk prediction models for mortality in patients with severe pneumonia: a systematic review and meta-analysis |
| title_fullStr | Risk prediction models for mortality in patients with severe pneumonia: a systematic review and meta-analysis |
| title_full_unstemmed | Risk prediction models for mortality in patients with severe pneumonia: a systematic review and meta-analysis |
| title_short | Risk prediction models for mortality in patients with severe pneumonia: a systematic review and meta-analysis |
| title_sort | risk prediction models for mortality in patients with severe pneumonia a systematic review and meta analysis |
| topic | severe pneumonia mortality prediction model systematic review meta-analysis |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2025.1564545/full |
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