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

Xiaofang Huang; Shuaijun Li; Qi Feng; Xiuying Tian; Ya-Nan Jiang; Bo Tian; Shufen Zhai; Wei Guo; Haiying He; Yuemei Li; Li Ma; Rongxiu Zheng; Shasha Fan; Hongyun Wang; Lu Chen; Hua Mei; Hua Xie; Xiaoxiang Li; Ming Yang; Liang Zhang

doi:10.21037/tp-23-337

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 Oct 30;12(10):1769-1781. doi: 10.21037/tp-23-337. Epub 2023 Oct 19.

Authors

Xiaofang Huang¹, Shuaijun Li¹, Qi Feng¹, Xiuying Tian², Ya-Nan Jiang³, Bo Tian⁴, Shufen Zhai⁵, Wei Guo⁶, Haiying He⁷, Yuemei Li⁸, Li Ma⁹, Rongxiu Zheng¹⁰, Shasha Fan¹¹, Hongyun Wang¹², Lu Chen¹³, Hua Mei¹⁴, Hua Xie¹⁵, Xiaoxiang Li¹⁶, Ming Yang¹⁷, Liang Zhang¹⁸

Affiliations

¹ Department of Pediatrics, Peking University First Hospital, Beijing, China.
² Department of Neonatology, Tianjin Central Hospital of Gynecology Obstetrics, Tianjin, China.
³ Department of Neonatology, Peking University Third Hospital, Beijing, China.
⁴ Department of Neonatology, Tangshan Maternal and Child Health Hospital, Tangshan, China.
⁵ Department of Pediatrics, Handan Central Hospital, Handan, China.
⁶ Department of Pediatrics, Xingtai People's Hospital, Xingtai, China.
⁷ Department of Pediatrics, Baogang Third Hospital of Hongci Group, Baotou, China.
⁸ Department of Pediatrics, The Second Hospital of Hebei Medical University, Shijiazhuang, China.
⁹ Department of Pediatrics, Hebei Children's Hospital, Shijiazhuang, China.
¹⁰ Department of Neonatology, Tianjin Medical University General Hospital, Tianjin, China.
¹¹ Department of Neonatology, The First Hospital of Tsinghua University, Beijing, China.
¹² Department of Pediatrics, Inner Mongolia Maternal and Child Health Hospital, Hohhot, China.
¹³ Department of Neonatology, Beijing Children's Hospital, Capital Medical University, Beijing, China.
¹⁴ Department of Pediatrics, The Affiliated Hospital of Inner Mongolia Medical University, Hohhot, China.
¹⁵ Department of Pediatrics, Affiliated Hospital of Chifeng University, Chifeng, China.
¹⁶ Department of Pediatrics, Beijing Luhe Hospital, Capital Medical University, Beijing, China.
¹⁷ Department of Neonatology, Beijing United Family Hospital, Beijing, China.
¹⁸ Department of Pediatrics, Chifeng Municipal Hospital, Chifeng, China.

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.

Keywords: Extremely preterm infants (EPIs); mortality risk; neonatal intensive care units (NICUs); nomogram; predictor.