Construction and verification of a risk factor prediction model for neonatal severe pneumonia
ObjectiveTo construct and validate a risk factor prediction model for neonatal severe pneumonia.MethodsThis study collected data from newborns diagnosed with pneumonia in Children’s Hospital Affiliated to Zhengzhou University. A total of 652 newborns were included. Risk factors were identified using...
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Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2025.1536705/full |
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| author | Weihua Gong Kaijie Gao Jiajia Ni Ying Shi Zhiming Shan Hongqi Sun Shanshan Wang Jiangtao Xu Junmei Yang |
| author_facet | Weihua Gong Kaijie Gao Jiajia Ni Ying Shi Zhiming Shan Hongqi Sun Shanshan Wang Jiangtao Xu Junmei Yang |
| author_sort | Weihua Gong |
| collection | DOAJ |
| description | ObjectiveTo construct and validate a risk factor prediction model for neonatal severe pneumonia.MethodsThis study collected data from newborns diagnosed with pneumonia in Children’s Hospital Affiliated to Zhengzhou University. A total of 652 newborns were included. Risk factors were identified using Least Absolute Selection and Shrinkage Operator (LASSO) regression and logistic regression analysis. The nomogram was used to construct a prediction model. The effectiveness of the model was evaluated using calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA).ResultsOut of 652 newborns, 186 (29%) were diagnosed with severe pneumonia. The patients were randomly divided into a training set (n = 554) and a testing set (n = 98) in a ratio of 85:15. A total of 30 indicators were analyzed. Respiratory rate (OR = 1.058, 95% CI: 1.035–1.081), weight (OR = 0.483, 95% CI: 0.340–0.686), C-reactive protein (CRP) (OR = 1.142, 95% CI: 1.028–1.268), neutrophil (NEU) (OR = 1.384, 95% CI: 1.232–1.555), hemoglobin (HGB) (OR = 0.989, 95% CI: 0.979–0.999), uric acid (UA) (OR = 1.006, 95% CI: 1.002–1.010), and blood urea nitrogen (BUN) (OR = 1.230, 95% CI: 1.058–1.431) were identified as independent risk factors for neonatal severe pneumonia. The calibration curve showed significant agreement. The area under the ROC curve (AUC) was 0.884 (95% CI: 0.852–0.916) for the training set, and 0.835 (95% CI: 0.747–0.922) for the testing set. DCA demonstrated good predictive properties.ConclusionThe prediction model based on respiratory rate, weight, CRP, NEU, HGB, UA, and BUN has shown promising predictive value in distinguishing between mild to moderate pneumonia and severe pneumonia in neonates. |
| format | Article |
| id | doaj-art-e0b67af7a8d249daace583cdd166fcd1 |
| institution | OA Journals |
| issn | 2296-858X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Medicine |
| spelling | doaj-art-e0b67af7a8d249daace583cdd166fcd12025-08-20T02:24:15ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2025-06-011210.3389/fmed.2025.15367051536705Construction and verification of a risk factor prediction model for neonatal severe pneumoniaWeihua Gong0Kaijie Gao1Jiajia Ni2Ying Shi3Zhiming Shan4Hongqi Sun5Shanshan Wang6Jiangtao Xu7Junmei Yang8Department of Clinical Laboratory, Children’s Hospital Affiliated to Zhengzhou University, Zhengzhou Key Laboratory of Children’s Infection and Immunity, Zhengzhou, Henan, ChinaDepartment of Clinical Laboratory, Children’s Hospital Affiliated to Zhengzhou University, Zhengzhou Key Laboratory of Children’s Infection and Immunity, Zhengzhou, Henan, ChinaDepartment of Detection and Diagnosis Technology Research, Guangzhou National Laboratory, Guangzhou, ChinaDepartment of Neonatal Disease Screening, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaDepartment of Clinical Laboratory, Children’s Hospital Affiliated to Zhengzhou University, Zhengzhou Key Laboratory of Children’s Infection and Immunity, Zhengzhou, Henan, ChinaDepartment of Clinical Laboratory, Children’s Hospital Affiliated to Zhengzhou University, Zhengzhou Key Laboratory of Children’s Infection and Immunity, Zhengzhou, Henan, ChinaDepartment of Clinical Laboratory, Children’s Hospital Affiliated to Zhengzhou University, Zhengzhou Key Laboratory of Children’s Infection and Immunity, Zhengzhou, Henan, ChinaDepartment of Clinical Laboratory, Children’s Hospital Affiliated to Zhengzhou University, Zhengzhou Key Laboratory of Children’s Infection and Immunity, Zhengzhou, Henan, ChinaDepartment of Clinical Laboratory, Children’s Hospital Affiliated to Zhengzhou University, Zhengzhou Key Laboratory of Children’s Infection and Immunity, Zhengzhou, Henan, ChinaObjectiveTo construct and validate a risk factor prediction model for neonatal severe pneumonia.MethodsThis study collected data from newborns diagnosed with pneumonia in Children’s Hospital Affiliated to Zhengzhou University. A total of 652 newborns were included. Risk factors were identified using Least Absolute Selection and Shrinkage Operator (LASSO) regression and logistic regression analysis. The nomogram was used to construct a prediction model. The effectiveness of the model was evaluated using calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA).ResultsOut of 652 newborns, 186 (29%) were diagnosed with severe pneumonia. The patients were randomly divided into a training set (n = 554) and a testing set (n = 98) in a ratio of 85:15. A total of 30 indicators were analyzed. Respiratory rate (OR = 1.058, 95% CI: 1.035–1.081), weight (OR = 0.483, 95% CI: 0.340–0.686), C-reactive protein (CRP) (OR = 1.142, 95% CI: 1.028–1.268), neutrophil (NEU) (OR = 1.384, 95% CI: 1.232–1.555), hemoglobin (HGB) (OR = 0.989, 95% CI: 0.979–0.999), uric acid (UA) (OR = 1.006, 95% CI: 1.002–1.010), and blood urea nitrogen (BUN) (OR = 1.230, 95% CI: 1.058–1.431) were identified as independent risk factors for neonatal severe pneumonia. The calibration curve showed significant agreement. The area under the ROC curve (AUC) was 0.884 (95% CI: 0.852–0.916) for the training set, and 0.835 (95% CI: 0.747–0.922) for the testing set. DCA demonstrated good predictive properties.ConclusionThe prediction model based on respiratory rate, weight, CRP, NEU, HGB, UA, and BUN has shown promising predictive value in distinguishing between mild to moderate pneumonia and severe pneumonia in neonates.https://www.frontiersin.org/articles/10.3389/fmed.2025.1536705/fullneonatalsevere pneumoniapredictive modelnomogramrisk factor |
| spellingShingle | Weihua Gong Kaijie Gao Jiajia Ni Ying Shi Zhiming Shan Hongqi Sun Shanshan Wang Jiangtao Xu Junmei Yang Construction and verification of a risk factor prediction model for neonatal severe pneumonia Frontiers in Medicine neonatal severe pneumonia predictive model nomogram risk factor |
| title | Construction and verification of a risk factor prediction model for neonatal severe pneumonia |
| title_full | Construction and verification of a risk factor prediction model for neonatal severe pneumonia |
| title_fullStr | Construction and verification of a risk factor prediction model for neonatal severe pneumonia |
| title_full_unstemmed | Construction and verification of a risk factor prediction model for neonatal severe pneumonia |
| title_short | Construction and verification of a risk factor prediction model for neonatal severe pneumonia |
| title_sort | construction and verification of a risk factor prediction model for neonatal severe pneumonia |
| topic | neonatal severe pneumonia predictive model nomogram risk factor |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2025.1536705/full |
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