利用住院信息预测极早产儿无重大发病的死亡和生存:一项多中心队列研究

IF 1.5 4区 医学 Q2 PEDIATRICS
Translational pediatrics Pub Date : 2025-05-30 Epub Date: 2025-05-21 DOI:10.21037/tp-2025-33
Xincheng Cao, Shujuan Li, Xinyue Gu, Huiyao Chen, Chuanzhong Yang, Miao Qian, Xiuying Tian, Falin Xu, Zuming Yang, Yang Wang, Jinzhen Guo, Shoo K Lee, Siyuan Jiang, Yun Cao
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引用次数: 0

摘要

背景:在早期阶段准确预测极早产儿(EPIs)的结局对于帮助临床医生和家长做出决定非常重要。本研究旨在利用新生儿重症监护病房(NICUs)入院信息,开发并验证EPIs无重大发病的死亡率和生存率预测模型。方法:在中国纳入两个最大的当代24+0-28+6周出生的epi队列。两种预测模型分别用于预测出院时无重大发病率的死亡率和生存率。如果在文献中与新生儿结局有明确的关联,并且在新生儿重症监护病房入院时容易获得,则确定潜在的预测因素,包括胎龄、出生体重、性别、先天、产前类固醇、5分钟Apgar评分和入院时的有创通气。采用Logistic回归建立模型。通过曲线下面积(AUC)评估模型性能。结果:发展队列中2438例epi患者死亡率为17.7%(431/ 2438),无重大发病生存率为52.5%(1281 / 2438)。在验证队列中的5045名婴儿中,9.2%(463/ 5045)死亡,59.1%(2981 / 5045)存活,无重大发病。选择胎龄、出生体重、新生儿重症监护病房入院时的有创通气、产前类固醇使用和5分钟Apgar评分作为死亡率模型的预测因子,AUC为0.77[95%可信区间(CI): 0.75-0.79]。对于无重大发病生存模型,预测因子为胎龄、出生体重、新生儿重症监护病房入院时的有创通气、性别和5分钟Apgar评分,AUC为0.72 (95% CI: 0.70-0.74)。验证队列的auc分别为0.76 (95% CI: 0.73-0.78)和0.70 (95% CI: 0.68-0.71)。结论:利用常用的新生儿重症监护病房入院预测指标,包括胎龄、出生体重、新生儿重症监护病房入院时的有创通气、产前类固醇使用、性别和5分钟Apgar评分,我们成功开发并验证了两种不同的模型,它们具有可接受的性能,可预测epi患者的死亡率和生存率,且无重大发病率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predicting death and survival without major morbidity for extremely preterm infants using information on hospital admission: a multicenter cohort study.

Background: Accurate prediction of outcomes for extremely preterm infants (EPIs) during the early stage is important to assist clinicians and parents in making decisions. This study aimed to develop and validate models for predicting mortality and survival without major morbidity for EPIs using information available on neonatal intensive care units (NICUs) admission.

Methods: Two of the largest contemporary cohorts of EPIs born at 24+0-28+6 weeks' gestation were included in China. Two predictive models were generated separately to predict mortality and survival without major morbidity at discharge. Potential predictors were identified if they had a well-established association with neonatal outcomes in literatures and could be easily obtained on NICU admission, including gestational age, birth weight, sex, inborn, antenatal steroids, 5-min Apgar score, and invasive ventilation on admission. Logistic regression was employed to develop the models. Model performance was assessed via area under the curve (AUC).

Results: Among 2,438 EPIs in the development cohort, the mortality rate was 17.7% (431/2,438) and the rate of survival without major morbidity was 52.5% (1,281/2,438). Among the 5,045 infants in the validation cohort, 9.2% (463/5,045) died, and 59.1% (2,981/5,045) survived without major morbidity. Gestational age, birth weight, invasive ventilation on NICU admission, antenatal steroids use, and 5-min Apgar score were selected as predictors in the mortality model, yielding the AUC of 0.77 [95% confidence interval (CI): 0.75-0.79]. For the survival without major morbidity model, predictors were gestational age, birth weight, invasive ventilation on NICU admission, sex, and 5-min Apgar score, and the AUC was 0.72 (95% CI: 0.70-0.74). The validation cohort resulted in AUCs of 0.76 (95% CI: 0.73-0.78) and 0.70 (95% CI: 0.68-0.71) for the mortality and survival without major morbidity models, respectively.

Conclusions: Using commonly available predictors on NICU admission including gestational age, birth weight, invasive ventilation on NICU admission, antenatal steroids use, sex, and 5-min Apgar score, we successfully developed and validated two distinct models with acceptable performance, predicting mortality and survival without major morbidity for EPIs.

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来源期刊
Translational pediatrics
Translational pediatrics Medicine-Pediatrics, Perinatology and Child Health
CiteScore
4.50
自引率
5.00%
发文量
108
期刊介绍: Information not localized
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