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早产儿入院败血症风险预测模型的构建与验证

中文摘要英文摘要

目的:旨在构建早产儿入院败血症风险预测模型,为临床早期识别及干预提供依据。方法:回顾性收集2020年1月至2023年12月厦门市儿童医院收治的早产患儿作为研究对象,根据入院后是否发生败血症分为败血症组和非败血症组,采用LASSO回归结合多因素Logistic回归筛选危险因素,构建列线图预测模型。选取2024年1月至2024年12月收治的174例患儿进行模型的外部验证。 结果:胎龄、10 min Apgar评分≤7、总胆红素、呼吸衰竭及呼吸频率是早产儿入院败血症的独立危险因素。模型训练集AUROC为0.853,外部验证AUROC为0.937,校准曲线中校正结果与理想曲线接近(Hosmer-Lemeshow检验χ2=6.599、P=0.580)。 结论:基于7项床旁指标构建的预测模型性能理想,无需微生物培养支持,可实现快速风险分层及抗菌药物决策。

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.

蔡欣欣、吴夏阳

10.12201/bmr.202505.00013

儿科学临床医学医学研究方法

早产儿败血症风险预测模型列线图危险因素

Preterm infantssepsisrisk prediction modelnomogramrisk factors

蔡欣欣,吴夏阳.早产儿入院败血症风险预测模型的构建与验证[EB/OL].(2025-04-14)[2025-07-21].https://www.biomedrxiv.org.cn/article/doi/bmr.202505.00013.点此复制

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