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已发表论文
新生儿高胆红素血症预测模型的构建与评估
Authors Xu S, Guo X, Li C, Chen J, Zhang C
Received 27 April 2025
Accepted for publication 8 October 2025
Published 13 November 2025 Volume 2025:18 Pages 6933—6940
DOI https://doi.org/10.2147/IJGM.S537139
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Woon-Man Kung
Shasha Xu,1 Xiaoheng Guo,1 Cuiyan Li,1 Jing Chen,2 Changgeng Zhang1
1Clinical Laboratory, Hengshui People’s Hospital, Hengshui, Hebei, 053000, People’s Republic of China; 2Obstetrics Department, Hengshui People’s Hospital, Hengshui, Hebei, 053000, People’s Republic of China
Correspondence: Changgeng Zhang, Email zcg660319@163.com
Objective: To investigate the factors that contribute to neonatal hyperbilirubinemia (NHB), develop a prediction model and predictive factors, which provide reference base for the early detection of NHB.
Methods: A retrospective study was done to collect clinical data of 683 neonates and their mothers which delivered in the hospital’s obstetrics department between January 2019 and January 2023, of these neonates 216 were hyperbilirubinemic and 467 were not. Clinical data and laboratory results of newborns and their mothers were collected and analysed. Multifactorial logistic regression analysis was utilised to develop an early prediction model and identify determinants of newborn hyperbilirubinemia.
Results: The logistic regression model revealed that Delayed meconium (OR=4.024, P=0.002), maternal age (OR=1.106, P< 0.01), maternal-infant blood group incompatibility (OR=4.457, P=0.001), Gestational Diabetes Mellitus (GDM) (OR=5.356, P=0.03), Pre-pregnancy co-morbidity (OR=2.810, < 0.01), and Weight Growth Rate (WGR) during pregnancy (OR=28.367, P=0.048) were independent risk factors for NHB; 25-(OH)D3 (OR=0.880, P=0.002). The Conjoint predictor ROC curve is below 0.851 (P< 0.01, 95% CI:0.821– 0.882). The highest Youden’s index was 0.55, with a sensitivity of 0.81 and a specificity of 0.76, indicating that the predictor had a decent predictive effect.
Conclusion: This study identified the risk factors and protective factors associated with NHB. Additionally, a joint prediction model was developed to more accurately predict the risk of hyperbilirubinemia which can serve as a foundation for the clinical identification of pertinent susceptibility factors and the development of intervention measures.
Keywords: neonatal hyperbilirubinemia, GDM, weight growth rate, 25-(OH) D3, prediction model
1Clinical Laboratory, Hengshui People’s Hospital, Hengshui, Hebei, 053000, People’s Republic of China; 2Obstetrics Department, Hengshui People’s Hospital, Hengshui, Hebei, 053000, People’s Republic of China
Correspondence: Changgeng Zhang, Email zcg660319@163.com
Objective: To investigate the factors that contribute to neonatal hyperbilirubinemia (NHB), develop a prediction model and predictive factors, which provide reference base for the early detection of NHB.
Methods: A retrospective study was done to collect clinical data of 683 neonates and their mothers which delivered in the hospital’s obstetrics department between January 2019 and January 2023, of these neonates 216 were hyperbilirubinemic and 467 were not. Clinical data and laboratory results of newborns and their mothers were collected and analysed. Multifactorial logistic regression analysis was utilised to develop an early prediction model and identify determinants of newborn hyperbilirubinemia.
Results: The logistic regression model revealed that Delayed meconium (OR=4.024, P=0.002), maternal age (OR=1.106, P< 0.01), maternal-infant blood group incompatibility (OR=4.457, P=0.001), Gestational Diabetes Mellitus (GDM) (OR=5.356, P=0.03), Pre-pregnancy co-morbidity (OR=2.810, < 0.01), and Weight Growth Rate (WGR) during pregnancy (OR=28.367, P=0.048) were independent risk factors for NHB; 25-(OH)D3 (OR=0.880, P=0.002). The Conjoint predictor ROC curve is below 0.851 (P< 0.01, 95% CI:0.821– 0.882). The highest Youden’s index was 0.55, with a sensitivity of 0.81 and a specificity of 0.76, indicating that the predictor had a decent predictive effect.
Conclusion: This study identified the risk factors and protective factors associated with NHB. Additionally, a joint prediction model was developed to more accurately predict the risk of hyperbilirubinemia which can serve as a foundation for the clinical identification of pertinent susceptibility factors and the development of intervention measures.
Keywords: neonatal hyperbilirubinemia, GDM, weight growth rate, 25-(OH) D3, prediction model