Predictive for patients with pneumonia in pediatric intensive care unit
IntroductionPneumonia is globally recognized as a significant disease burden, particularly among pediatric patients in intensive care units (ICU), where its etiology is complex and prognosis often poor.MethodsData were extracted from a pediatric-specific intensive care (PIC) database, selecting 795...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Pediatrics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fped.2025.1583573/full |
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| author | Mingxuan Jia Xiyan Hu Lin Ji Jiawen Lin Jialin Liu Yong Wang |
| author_facet | Mingxuan Jia Xiyan Hu Lin Ji Jiawen Lin Jialin Liu Yong Wang |
| author_sort | Mingxuan Jia |
| collection | DOAJ |
| description | IntroductionPneumonia is globally recognized as a significant disease burden, particularly among pediatric patients in intensive care units (ICU), where its etiology is complex and prognosis often poor.MethodsData were extracted from a pediatric-specific intensive care (PIC) database, selecting 795 pediatric pneumonia patients in ICUs (2010–2018). After applying rigorous inclusion/exclusion criteria, 543 cases formed the study cohort. We analyzed patient baseline information and 70 laboratory indicators to identify 25 prognosis-associated biomarkers. For prognostic model construction, we used stepwise regression to filter 28 variables, then Spearman and Pearson correlation analyses to identify an intersection of 14 key indicators from the top 20 features. Twelve machine learning algorithms underwent parameter tuning and combination, forming 113 model combinations for survival outcome prediction.ResultsThe “Stepglm [both] + GBM” combination achieved the highest average accuracy (79.4%) in both training and testing sets. Twelve prognostic variables were identified: WBC Count, Glucose, Neutrophils Count, Cystatin C, Temperature (body), Sodium (Whole Blood), Cholesterol (Total), Absolute Lymphocyte Count, Urea, Lactate, and Bilirubin (Total).DiscussionThese 12 variables provide a dependable basis and novel insights for prognostic evaluation, supporting clinical diagnosis, treatment, and early intervention. |
| format | Article |
| id | doaj-art-134e9b1b35ac4b428c74c4097fd4bc5d |
| institution | Kabale University |
| issn | 2296-2360 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Pediatrics |
| spelling | doaj-art-134e9b1b35ac4b428c74c4097fd4bc5d2025-08-20T03:26:09ZengFrontiers Media S.A.Frontiers in Pediatrics2296-23602025-06-011310.3389/fped.2025.15835731583573Predictive for patients with pneumonia in pediatric intensive care unitMingxuan Jia0Xiyan Hu1Lin Ji2Jiawen Lin3Jialin Liu4Yong Wang5Middlebury College, Middlebury, VT, United StatesStanford University, Stanford, CA, United StatesShanghai Literature Institute of Traditional Chinese Medicine, Shanghai, ChinaYangpu Hospital of Traditional Chinese Medicine, Shanghai, ChinaChengZheng Wisdom (Shanghai) Health Sciences and Technology Co., Ltd., Shanghai, ChinaShanghai Literature Institute of Traditional Chinese Medicine, Shanghai, ChinaIntroductionPneumonia is globally recognized as a significant disease burden, particularly among pediatric patients in intensive care units (ICU), where its etiology is complex and prognosis often poor.MethodsData were extracted from a pediatric-specific intensive care (PIC) database, selecting 795 pediatric pneumonia patients in ICUs (2010–2018). After applying rigorous inclusion/exclusion criteria, 543 cases formed the study cohort. We analyzed patient baseline information and 70 laboratory indicators to identify 25 prognosis-associated biomarkers. For prognostic model construction, we used stepwise regression to filter 28 variables, then Spearman and Pearson correlation analyses to identify an intersection of 14 key indicators from the top 20 features. Twelve machine learning algorithms underwent parameter tuning and combination, forming 113 model combinations for survival outcome prediction.ResultsThe “Stepglm [both] + GBM” combination achieved the highest average accuracy (79.4%) in both training and testing sets. Twelve prognostic variables were identified: WBC Count, Glucose, Neutrophils Count, Cystatin C, Temperature (body), Sodium (Whole Blood), Cholesterol (Total), Absolute Lymphocyte Count, Urea, Lactate, and Bilirubin (Total).DiscussionThese 12 variables provide a dependable basis and novel insights for prognostic evaluation, supporting clinical diagnosis, treatment, and early intervention.https://www.frontiersin.org/articles/10.3389/fped.2025.1583573/fullpneumoniaintensive care unitmachine learning algorithmspaediatricspredictive models |
| spellingShingle | Mingxuan Jia Xiyan Hu Lin Ji Jiawen Lin Jialin Liu Yong Wang Predictive for patients with pneumonia in pediatric intensive care unit Frontiers in Pediatrics pneumonia intensive care unit machine learning algorithms paediatrics predictive models |
| title | Predictive for patients with pneumonia in pediatric intensive care unit |
| title_full | Predictive for patients with pneumonia in pediatric intensive care unit |
| title_fullStr | Predictive for patients with pneumonia in pediatric intensive care unit |
| title_full_unstemmed | Predictive for patients with pneumonia in pediatric intensive care unit |
| title_short | Predictive for patients with pneumonia in pediatric intensive care unit |
| title_sort | predictive for patients with pneumonia in pediatric intensive care unit |
| topic | pneumonia intensive care unit machine learning algorithms paediatrics predictive models |
| url | https://www.frontiersin.org/articles/10.3389/fped.2025.1583573/full |
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