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Huang X, Li S, Feng Q, Tian X, Jiang YN, Tian B, Zhai S, Guo W, He H, Li Y, Ma L, Zheng R, Fan S, Wang H, Chen L, Mei H, Xie H, Li X, Yang M, Zhang L. A nomogram for predicting death for infants born at a gestational age of <28 weeks: a population-based analysis in 18 neonatal intensive care units in northern China. Transl Pediatr 2023; 12:1769-1781. [PMID: 37969124 PMCID: PMC10644021 DOI: 10.21037/tp-23-337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 09/15/2023] [Indexed: 11/17/2023] Open
Abstract
Background In China, the number of preterm infants is the second largest globally. Compared with those in developed countries, the mortality rate and proportion of treatment abandonment for extremely preterm infants (EPIs) are higher in China. It would be valuable to conduct a multicenter study and develop predictive models for the mortality risk. This study aimed to identify a predictive model among EPIs who received complete care in northern China in recent years. Methods This study included EPIs admitted to eighteen neonatal intensive care units (NICUs) within 72 hours of birth for receiving complete care in northern China between January 1, 2015, and December 31, 2018. Infants were randomly assigned into a training dataset and validation dataset with a ratio of 7:3. Univariate Cox regression analysis and multiple regression analysis were used to select the predictive factors and to construct the best-fitting model for predicting in-hospital mortality. A nomogram was plotted and the discrimination ability was tested by an area under the receiver operating characteristic curve (AUROC). The calibration ability was tested by a calibration curve along with the Hosmer-Lemeshow (HL) test. In addition, the clinical effectiveness was examined by decision curve analysis (DCA). Results A total of 568 EPIs were included and divided into the training dataset and validation dataset. Seven variables [birth weight (BW), being inborn, chest compression in the delivery room (DR), severe respiratory distress syndrome, pulmonary hemorrhage, invasive mechanical ventilation, and shock] were selected to establish a predictive nomogram. The AUROC values for the training and validation datasets were 0.863 [95% confidence interval (CI): 0.813-0.914] and 0.886 (95% CI: 0.827-0.945), respectively. The calibration plots and HL test indicated satisfactory accuracy. The DCA demonstrated that positive net benefits were shown when the threshold was >0.6. Conclusions A nomogram based on seven risk factors is developed in this study and might help clinicians identify EPIs with risk of poor prognoses early.
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Affiliation(s)
- Xiaofang Huang
- Department of Pediatrics, Peking University First Hospital, Beijing, China
| | - Shuaijun Li
- Department of Pediatrics, Peking University First Hospital, Beijing, China
| | - Qi Feng
- Department of Pediatrics, Peking University First Hospital, Beijing, China
| | - Xiuying Tian
- Department of Neonatology, Tianjin Central Hospital of Gynecology Obstetrics, Tianjin, China
| | - Ya-Nan Jiang
- Department of Neonatology, Peking University Third Hospital, Beijing, China
| | - Bo Tian
- Department of Neonatology, Tangshan Maternal and Child Health Hospital, Tangshan, China
| | - Shufen Zhai
- Department of Pediatrics, Handan Central Hospital, Handan, China
| | - Wei Guo
- Department of Pediatrics, Xingtai People’s Hospital, Xingtai, China
| | - Haiying He
- Department of Pediatrics, Baogang Third Hospital of Hongci Group, Baotou, China
| | - Yuemei Li
- Department of Pediatrics, The Second Hospital of Hebei Medical University, Shijiazhuang, China
| | - Li Ma
- Department of Pediatrics, Hebei Children’s Hospital, Shijiazhuang, China
| | - Rongxiu Zheng
- Department of Neonatology, Tianjin Medical University General Hospital, Tianjin, China
| | - Shasha Fan
- Department of Neonatology, The First Hospital of Tsinghua University, Beijing, China
| | - Hongyun Wang
- Department of Pediatrics, Inner Mongolia Maternal and Child Health Hospital, Hohhot, China
| | - Lu Chen
- Department of Neonatology, Beijing Children’s Hospital, Capital Medical University, Beijing, China
| | - Hua Mei
- Department of Pediatrics, The Affiliated Hospital of Inner Mongolia Medical University, Hohhot, China
| | - Hua Xie
- Department of Pediatrics, Affiliated Hospital of Chifeng University, Chifeng, China
| | - Xiaoxiang Li
- Department of Pediatrics, Beijing Luhe Hospital, Capital Medical University, Beijing, China
| | - Ming Yang
- Department of Neonatology, Beijing United Family Hospital, Beijing, China
| | - Liang Zhang
- Department of Pediatrics, Chifeng Municipal Hospital, Chifeng, China
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Feng Z, Fan Y, Xie J, Liu S, Duan C, Wang Q, Ye Y, Yin W. HIF-1α promotes the expression of syndecan-1 and inhibits the NLRP3 inflammasome pathway in vascular endothelial cells under hemorrhagic shock. Biochem Biophys Res Commun 2022; 637:83-92. [DOI: 10.1016/j.bbrc.2022.10.102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Revised: 10/13/2022] [Accepted: 10/29/2022] [Indexed: 11/06/2022]
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