Establishment and verification of a risk prediction model for neonatal sepsis in premature infants

Corresponding author: WU Xiayang, 56425477@qq.com

DOI: 10.12201/bmr.202505.00013

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  Abstract: Objective: To construct a risk prediction model for admission sepsis in preterm infants, providing a basis for early clinical identification and intervention. Methods: A retrospective collection of preterm infants admitted to Xiamen Childrens Hospital from January 2020 to December 2023 was conducted as the study subjects. The infants were divided into a sepsis group and a non-sepsis group based on the occurrence of sepsis after admission. LASSO regression combined with multivariate Logistic regression was used to screen risk factors, and a nomogram prediction model was constructed. External validation of the model was performed with 174 infants admitted from January 2024 to December 2024. Results: Gestational age, Apgar score ≤7 at 10 minutes, total bilirubin, respiratory failure, and respiratory rate were identified as independent risk factors for admission sepsis in preterm infants. The AUROC of the training set was 0.853, and the external validation AUROC was 0.937. The calibration results in the calibration curve are close to the ideal curve (Hosmer-Lemeshow test χ2=6.599、P=0.580). Conclusion: The prediction model developed based on seven bedside indicators demonstrates excellent performance, enabling rapid risk stratification and antimicrobial decision-making without the need for microbiological culture support.

  Key words: Preterm infants; sepsis; risk prediction model; nomogram; risk factors

  Submit time: 17 May 2025

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
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