反跳性高胆红素血症预测模型的开发与验证:一项中国新生儿队列研究。

IF 1.5 4区 医学 Q2 PEDIATRICS
Translational pediatrics Pub Date : 2024-08-31 Epub Date: 2024-08-23 DOI:10.21037/tp-24-21
Huiyi Li, Xihua Huang, Zhenyu Liang, Haijian Liang, Si He, Li Tang
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引用次数: 0

摘要

背景:高达 10% 的新生儿仍存在反跳性高胆红素血症(HBB)。然而,已有的高胆红素血症反跳预测模型对中国新生儿的适用性尚不明确。本研究旨在建立一个预测中国新生儿光疗后HBB反弹的模型:方法:对 1035 名接受光疗的 HBB 新生儿进行了回顾性队列研究。光疗结束后72小时内血清总胆红素(TSB)恢复到或超过美国儿科学会(AAP)规定的光疗阈值即为HBB反弹。对之前发表的两变量和三变量评分的预测效果进行了验证。新生儿按 6:4 的比例随机分配到训练组(621 名)和测试组(414 名)。通过最小绝对收缩和选择算子(LASSO)回归分析,使用训练集中的所有变量来选择预测因子。预测模型的内部验证使用测试集进行。模型的预测性能通过曲线下面积(AUC)、准确性、灵敏度和特异性进行评估,每个指标都有 95% 的置信区间(CI)。构建的接收者操作特征曲线(ROC)和校准曲线分别用于评估预测模型的辨别能力和拟合效果:210名患者(20.3%)出现了HBB反弹。两变量和三变量评分的AUC分别为0.498(95% CI:0.455-0.540)和0.498(95% CI:0.457-0.540)。HBB反弹风险的预测因素包括配方奶喂养(>3次/天)、标准光疗照射时间、光疗终止时的TSB水平和年龄、新生儿体重以及光疗终止时的TSB水平与光疗阈值之间的差异。预测模型的AUC为0.935(95% CI:0.911-0.958),灵敏度为0.880(95% CI:0.809-0.950),特异度为0.831(95% CI:0.790-0.871),准确度为0.841(95% CI:0.805-0.876):结论:所建立的模型在预测中国婴儿HBB反弹风险方面表现良好,这可能有利于治疗和管理婴儿HBB。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Development and validation of a prediction model for rebound hyperbilirubinemia: a Chinese neonatal cohort study.

Background: Rebound hyperbilirubinemia (HBB) is still present in as high as 10% of newborn babies. However, the applicability of established prediction models for rebound HBB to Chinese newborns is unclear. This study aimed to establish a model to predict HBB rebound after phototherapy among Chinese neonates.

Methods: A retrospective cohort study was conducted on 1,035 HBB infants receiving phototherapy. Rebound HBB was defined as total serum bilirubin (TSB) returning to or above the American Academy of Pediatrics (AAP) phototherapy threshold within 72 hours after the end of phototherapy. The predictive effects of previously published two- and three-variable scores were verified. Neonates were randomly assigned in a 6:4 ratio to the training (n=621) group and the testing (n=414) group. All variables in the training set were used to select predictors by least absolute shrinkage and selection operator (LASSO) regression analysis. The internal validation of the prediction model was performed using the testing set. The model's predictive performance was evaluated by area under the curve (AUC), accuracy, sensitivity, and specificity, each with 95% confidence intervals (CIs). Receiver operating characteristic (ROC) and calibration curves were constructed to evaluate the discrimination ability and fitting effect of the prediction model, respectively.

Results: Rebound HBB was observed in 210 patients (20.3%). The AUC for the two- and three-variable scores were 0.498 (95% CI: 0.455-0.540) and 0.498 (95% CI: 0.457-0.540), respectively. Predictive factors for the risk of rebound HBB included formula feeding (>3 times/day), standard phototherapy irradiation time, TSB levels and age at termination of phototherapy, neonatal weight, and differences between TSB levels at the phototherapy termination and phototherapy threshold. The prediction model's AUC was 0.935 (95% CI: 0.911-0.958), the sensitivity was 0.880 (95% CI: 0.809-0.950), the specificity was 0.831 (95% CI: 0.790-0.871), and the accuracy was 0.841 (95% CI: 0.805-0.876).

Conclusions: The established model performed well in predicting rebound risk among Chinese infants with HBB, which may be beneficial in treating and managing HBB in infants.

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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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