INTRODUCTION Prediction models assessing the mortality of very-low-birth-weight (VLBW) infants were confined to models using only pre- and perinatal variables. We aimed to construct a prediction model comprising multifactorial clinical events with data obtainable at various time points. METHODS We included 15,790 (including 2,045 in-hospital deaths) VLBW infants born between 2013 and 2020 who were enrolled in the Korean Neonatal Network, a nationwide registry. In total, 53 prenatal and postnatal variables were sequentially added into the three discrete models stratified by hospital days: (1) within 24 h (TL-1d), (2) from day 2 to day 7 after birth (TL-7d), (3) from day 8 after birth to discharge from the neonatal intensive care unit (TL-dc). Each model predicted the mortality of VLBW infants within the affected period. Multilayer perception (MLP)-based network analysis was used for modeling, and ensemble analysis with traditional machine learning (ML) analysis was additionally applied. The performance of models was compared using the area under the receiver operating characteristic curve (AUROC) values. The Shapley method was applied to reveal the contribution of each variable. RESULTS Overall, the in-hospital mortality was 13.0% (1.2% in TL-1d, 4.1% in TL-7d, and 7.7% in TL-dc). Our MLP-based mortality prediction model combined with ML ensemble analysis had AUROC values of 0.932 (TL-1d), 0.973 (TL-7d), and 0.950 (TL-dc), respectively, outperforming traditional ML analysis in each timeline. Birth weight and gestational age were constant and significant risk factors, whereas the impact of the other variables varied. CONCLUSION The findings of the study suggest that our MLP-based models could be applied in predicting in-hospital mortality for high-risk VLBW infants. We highlight that mortality prediction should be customized according to the timing of occurrence.

中文翻译：

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基于学习的极低出生体重婴儿死亡风险纵向预测模型：一项全国队列研究。

简介 评估极低出生体重（VLBW）婴儿死亡率的预测模型仅限于仅使用产前和围产期变量的模型。我们的目的是构建一个包含多因素临床事件以及在不同时间点获得的数据的预测模型。方法 我们纳入了 15,790 名（包括 2,045 名院内死亡）2013 年至 2020 年间出生的极低出生体重婴儿，他们被纳入全国登记处韩国新生儿网络。总共，将 53 个产前和产后变量依次添加到按住院天数分层的三个离散模型中：（1）24 小时内（TL-1d），（2）出生后第 2 天至第 7 天（TL-7d）， (3)从出生后第8天到从新生儿重症监护病房(TL-dc)出院。每个模型都预测了受影响期间极低出生体重婴儿的死亡率。基于多层感知（MLP）的网络分析用于建模，并另外应用了集成分析和传统机器学习（ML）分析。使用受试者工作特征曲线下面积 (AUROC) 值来比较模型的性能。应用 Shapley 方法揭示每个变量的贡献。结果 总体而言，院内死亡率为 13.0%（TL-1d 为 1.2%，TL-7d 为 4.1%，TL-dc 为 7.7%）。我们基于 MLP 的死亡率预测模型与 ML 集成分析相结合，其 AUROC 值分别为 0.932 (TL-1d)、0.973 (TL-7d) 和 0.950 (TL-dc)，在每个时间线均优于传统的 ML 分析。出生体重和胎龄是恒定且重要的风险因素，而其他变量的影响各不相同。结论 研究结果表明，我们基于 MLP 的模型可用于预测高危 VLBW 婴儿的院内死亡率。我们强调，死亡率预测应根据发生的时间进行定制。